A Modular Open Source Workflow for Lipidomics Using Liquid Chromatography High Resolution Tandem Mass Spectrometry (UHPLC-HRMS/MS)

Material Information

A Modular Open Source Workflow for Lipidomics Using Liquid Chromatography High Resolution Tandem Mass Spectrometry (UHPLC-HRMS/MS)
Koelmel, Jeremy P
Place of Publication:
[Gainesville, Fla.]
University of Florida
Publication Date:
Physical Description:
1 online resource (221 p.)

Thesis/Dissertation Information

Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Committee Chair:
Committee Co-Chair:
Committee Members:


Subjects / Keywords:
aif -- all-ion-fragmentation -- all-ions -- bioinformatics -- data-dependent-analysis -- dda -- ddms2 -- diacylglycerol -- esi -- hcd -- heat-treatment -- high-resolution -- identification -- lc-hrms -- lc-ms -- lipidomics -- lipids -- mass-spectrometry -- metabolomics -- orbitrap -- quantification -- software -- tandem -- uhplc -- uhplc-hrms
Chemistry -- Dissertations, Academic -- UF
bibliography ( marcgt )
theses ( marcgt )
government publication (state, provincial, terriorial, dependent) ( marcgt )
born-digital ( sobekcm )
Electronic Thesis or Dissertation
Chemistry thesis, Ph.D.


Lipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. The clinical utility of lipidomics is derived from the diverse biological roles of lipids. This diversity in lipid function is enabled by the diversity of lipid structures and the wide dynamic range of lipid concentrations, thus making the execution of lipidomic experiments analytically challenging. Advances in ultra-high performance liquid chromatography and high resolution tandem mass spectrometry (UHPLC-HRMS/MS) enable the acquisition of data covering a large portion of the lipidome in a single experiment. Data processing tools for mining this information have only recently emerged, and strategies are still needed to more accurately and comprehensively annotate and quantify lipid ions from UHPLC-HRMS/MS data. Therefore, we developed a suite of open source lipidomics software tools and novel strategies which cover the majority of the lipidomics workflow, including: increasing lipid fragmentation coverage, improving peak picking, removing background ions, combining redundant ion information, annotating lipid species, and quantifying lipid species using class specific lipid standards. For improved lipid coverage, we introduce LipidMatch, a lipid identification software leveraging the largest open source in-silico fragmentation library. LipidMatch covers over 250,000 lipid species, spanning over 56 lipid types. To further increase lipid coverage, we employ an R script tool (termed iterative exclusion) for generating exclusion lists from ions selected for fragmentation in previous injections, resulting in 69 % more lipid identifications in positive mode analysis of human plasma. For improved data-quality, we developed a tool based on an algorithm for removing peaks present in extraction blanks, and a unique peak picking strategy using MZmine which reduced the residual standard deviation between samples. For automated relative quantification of the lipidome, we developed another tool LipidMatch Quant, which matches standards to analytes based on lipid class, adduct, and retention time; hence accounting for both lipid structural and regional chromatographic effects on ionization efficiency. Through increased lipid coverage and more accurate measurements, these tools can increase the probability of lipid biomarker discovery and understanding disease etiology. Parameters for each tool allow the user to optimize and tailor tools depending on the vendor, instrument, and experimental design employed. Since each of these is built as a separate modular tool, users can integrate them into other lipidomics software packages to design a workflow based on their expertise, application, finances, and instrumentation. Finally, we have integrated the tools into a single software application, LipidMatch Flow, which has been designed to cover all steps of the data-processing workflow up to lipid annotation for more robust and rapid analysis. ( en )
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis (Ph.D.)--University of Florida, 2017.
Statement of Responsibility:
by Jeremy P Koelmel.

Record Information

Source Institution:
Rights Management:
Applicable rights reserved.
LD1780 2017 ( lcc )


This item has the following downloads:

Full Text




2017 Jeremy Paul Koelmel


To my family, mentors, and you, the reader


4 ACKNOWLEDGMENTS I first and foremost would like to thank the team of exceptionally talented undergraduates, Berkley Olsen, Jason Cochran, Jordan Zeldin, Nicholas Azcarate, and Nicholas Kroeger, whose dedication and enthusiasm for research is contagious. I specifically would like to thank Nicholas Kroeger, who recoded most of my LipidMatch scr i pt added essential features and coded the iterative exclusion script Here, I would also like to thank Timothy Garrett The synchronicity of both of us coming up with the idea of iterative exclusion separately within a few weeks, and his excitement and interest to implement the strategy, catalyzed this work. I also e xpress my appreciation for Jason Cochran who wrote the code for LipidMatch Quant and code for conversion of MS DIAL libraries into LipidMatch format. I owe much of my success during the course of my PhD to Jason Cochran's and Nicholas Kroeger's countless hours of work and patience as I continued to add new features and changed old ones. I also appreciate the work of Dr. Yang Li, who combined the entire lipidomics workflow that I developed with my undergraduates (from file conversion to lipid annotation), i nto a single windows application. Science is built on the shoulders of giants, and I appreciate the wider lipidomics community for their support. I am grateful to the development team of MS DIAL, specifically Hiroshi Tsugawa and the LipidBlast development team for providing open source libraries implemented in LipidMatch. I am grateful to Tamil Selvan, who provided data files for development of oxidized lipid libraries. I would like to acknowledge Christopher Beecher for his help with writing R code to ru n non negative matrix factorization ( NMF ) and interpreting NMF analysis and Lauren McIntyre for help designing the univariate statistical analyses employed in my workflow.


5 I am inspired by Dr. Richard Yost's mentoring philosophy. I flourished in the freed om he granted me to follow my passion, as well as the resources and superb scientists he connected me with to make my passion a reality Specifically, Dr. John Bowden was instrumental in my success at University of Florida. Most of my ideas for my PhD were synthesized in late night conversations with John. In addition, John trained me with hand s on experiences in all of my laboratory work. He also worked side by side with me during sample preparation, and we processed hundreds of samples together, for which I am very grateful for his time and support. Yost group alumni Dr. Candice Ulmer and Dr. Rainey Patterson, also played an important role in training me to use the Q Extractive orbitrap used in this dissertation and designed the chromatography method used th roughout. All four publications which are the backbone of this dissertation were executed in collaboration with Candice Ulmer, and her work ethic and efficiency are contagious. Other collaborators who deserve specific thanks are Dr. Christina Jones, Emil y Gill, and Dr. Tracey Schock, as well as all the Yost group members, who would spend over 3 hours at a time giving in depth feedback on my oral presentations, posters, and manuscript figures. This research was done in collaboration between Core 1 and Cor e 3 of the Southeast Center for Metabolomics (SECIM) < > (NIH Grant #U24 DK097209). I would like to thank Kaitlin Brennan from the SECIM administration core for implementing the website which hosts all the lipidomics software I developed during my PhD < tools/ >. In addition to those who directly supported me in my research, I would also like to thank the scientists and teachers who encouraged me to take the path of analytical


6 chemistry, specifically Dr. Dulasiri Amarasiriwardena and Dr. M. N. V. Prasad. Their self less devotion to their students is admirable. Had it not been for my family and friends, I would be a tense mess by the end of this PhD. My mother, father, sister, partner Harmony Miller, and friends made sure that during my PhD I lived a life of not just working hard, but playing hard. I also express gratitude to my spiritual mentors, who support me in knowing myself and purifying my heart and mind, which allowed me to have even more clarity, focus, and creativity to bring to my research. Specific mentors are Dr. David Wolfe and Marie Glasheen of Satvatove Institute, Mickey Singer of the Temple of the Universe, and S. N. Goenka, teacher of vipassana meditation, who all selflessly serve for the liberation of people from misery to happiness. Finally I thank those who hosted spiritual programs, helped me relax and connect to myself and others, including Amber and Eliot Larkin of Friday Night Meditations, and Matt Chandler of Aurora.


7 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ .......... 10 LIST OF FIGURES ................................ ................................ ................................ ........ 11 LIST OF OBJECTS ................................ ................................ ................................ ....... 15 LIST OF ABBREVIATIONS ................................ ................................ ........................... 16 ABSTRA CT ................................ ................................ ................................ ................... 21 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 23 Lipidomics, an Overview ................................ ................................ ......................... 23 Lipi domics Workflow ................................ ................................ ............................... 26 Sample Preparation ................................ ................................ .......................... 26 Data Acquisition: Reverse Phase Liquid Chromatography ............................... 27 Data Acquisition: Volatizing and Ionizing Lipids with Electrospray ................... 28 Data Acquisition: Orbitrap Mass Spectrometers for Lipidomics ........................ 32 Overcoming the difficulty of using ESI with orbitrap mass spectrometers .. 34 Measuring high resolution with orbitrap mass spectrometers .................... 35 Scanning functions for obtaining fragmentation data ................................ 39 Processing of LC HRMS/MS Lipidomics Data ................................ .................. 41 Biological Interpretation of Lipid Perturbations ................................ ................. 44 Dissertation Overview ................................ ................................ ............................. 45 2 EXPANDING LIPIDOME COVERAGE USING LC MS/MS DATA DEPENDENT ACQUISITION WITH AUTOMATED EXCLUSION LIST GENERATION ................ 52 The Case for Iterative Exclusion in Lipidomics Experiments ................................ ... 52 Methods: Lipidomics Workflow Implementing Iterative Exclusion Software ............ 55 Chemicals and Materials ................................ ................................ .................. 55 Sample Preparation ................................ ................................ .......................... 56 Data Acquisition ................................ ................................ ............................... 57 Software Pla tform for IE ................................ ................................ ................... 58 Feature Detection and Identification ................................ ................................ 59 Results and Discussion: Iterative Exclusion and Lipidome Coverage ..................... 60 Conclusion: Iterative Exclusion Increases Lipidome Coverage ............................... 68


8 3 LIPIDMATCH: AN AUTOMATED WORKFLOW FOR RULE BASED LIPID IDENTIFIC ATION USING UNTARGETED HIGH RESOLUTION TANDEM MASS SPECTROMETRY DATA ................................ ................................ ............ 78 The Challenges of Lipid Identification ................................ ................................ ..... 78 Lipid Annotation Guidelines for Correctly Reporting Structural Resolution ............. 80 Implementation of LipidMatch Software ................................ ................................ .. 86 Generation and Validation of LipidMatch in silico Libraries .............................. 87 Lipidomics Workflow with LipidMatch ................................ ............................... 88 LipidMatch Inputs and Operations ................................ ................................ .... 91 Benchmarking LipidMatch against other Open Source Software ............................ 94 Comparison of Lipid Software Features ................................ ........................... 94 A Case Study: Identification of Lipids in Red Cross Plasma ............................. 9 7 Conclusion: LipidMatch is a Flexible, Comprehensive, and A ccurate Annotation Software ................................ ................................ ................................ ............ 101 4 ANNOTATION AND QUANTIFICATION OF LIPIDS USING AN OPEN SOURCE LC HRMS/MS WORKFLOW AND LIPIDMATCH QUANT .................... 108 Relative Quantification in Lipidomics ................................ ................................ .... 108 Methods: Lipidomics Workflow and LipidMatch Quant Implementation ................ 111 Lipid Extrac tion and Data Acquisition ................................ ............................. 111 Data Processing ................................ ................................ ............................. 113 LMQ User Workflow ................................ ................................ ....................... 115 LMQ Algori thm ................................ ................................ ............................... 117 Comparison of Quantification Using Different Data Processing Methods and Different Ions ................................ ................................ ............................... 118 Results and Discussion: Coverage by AIF and Comparison of Data Processing Methods on Lipid Concentration Calculated ................................ ...................... 120 Conclusions: LMQ is a Flexible Tool for Applying Current Relative Quantification Methods to Various Worfkflows ................................ .................. 127 5 EXAMINING HEAT TREATMENT FOR STABILIZATION OF THE LIPIDOME .... 135 The Case for Heat Treatment to Improve Lipid Stability in Tissues ...................... 135 Materials and Methods ................................ ................................ .......................... 138 Materials ................................ ................................ ................................ ......... 138 Heat Treatment ................................ ................................ .............................. 139 Lipid Extrac tion ................................ ................................ ............................... 140 Lipidomic Analysis via Mass Spectrometry ................................ ..................... 141 Data Processing ................................ ................................ ............................. 142 Statistical Analysis ................................ ................................ .......................... 143 Result s and Discussion: Evidence for Deactivation of Lipases ............................. 144 Conclusion: Heat Treatment Warrants Further Investigation ................................ 153 6 CONCLUSION AND FUTURE PROSPECTIVES ................................ ................. 161


9 APPENDIX: SUPPLEMENTAL INFORMATION ................................ ......................... 169 Chapter 2 ................................ ................................ ................................ .............. 169 Chapter 3 ................................ ................................ ................................ .............. 179 Chapter 4 ................................ ................................ ................................ .............. 187 Chapter 5 ................................ ................................ ................................ .............. 194 LIST OF REFERENCES ................................ ................................ ............................. 200 BIOGRAPH ICAL SKETCH ................................ ................................ .......................... 218


10 LIST OF TABLES Table page 2 1 Comparison of diglyceride (DG) peak heights and fatty acid compositions between ddMS 2 top5 and iterative exclusion (IE) acquisition ............................ 70 3 1 Strucural r esolution and a nnotation of l ipids using m ass s pectrometry ............ 102 3 2 Comparison of lipid identification software. ................................ ...................... 103 4 1 Comparison of different lipid quantification software which can be applied to UHPLC HRMS/MS data ................................ ................................ ................... 129 4 2 Comparison of the relative standard deviation (RSD) of concentrations calculated using different methods or ions ................................ ....................... 129 5 1 Significant changes in enzymatic products by lipid class using Fisher's exact test ................................ ................................ ................................ ................... 155 5 2 Residual standard deviations (RSD) of LPC and LPE across all heat treated and flash frozen samples ................................ ................................ ................. 156 S2 1 Gradient for reverse phase liquid chromatography of lipids ............................. 169 S2 2 Mass spectrometric parameters ................................ ................................ ...... 169 S2 3 Source parameters (electrospray ionization (ESI)) ................................ ........... 170 S3 1 LipidMatch lipids as of 10/1/2016 ................................ ................................ ..... 180 S3 2 LipidMatch lipid acronyms as of 10/1/2016 ................................ ....................... 182 S4 1 Liquid c hromatography g radient ................................ ................................ ....... 187 S4 2 Mass s pectrometry s can p arameters ................................ ................................ 188 S4 3 Comparisons of LMQ derived concentrations using various data processing techniques and ions for quantification ................................ .............................. 189 S5 1 U ltra high performance liquid chromatography (UHPLC ) gradient ................... 194 S5 2 Q Exactive scanning parameters ................................ ................................ ...... 195


11 LIST OF FIGURES Figure page 1 1 General glycerophospholipid structure ................................ ............................... 48 1 2 Lipidomics workflow for liquid chromatography high resolution tandem mass spectrometry (LC HRMS/MS) ................................ ................................ ............. 48 1 3 Q Exactive schematic ................................ ................................ ......................... 49 1 4 Schematic of scanning events for data dependent and All Ion Fragmentation acquisition mode ................................ ................................ ................................ 50 1 5 Positive ion mode full scan dataset for adipose tissue from Mozambique tilapia. Data acquired in house using the workflow described in this dissertation ................................ ................................ ................................ ........ 50 1 6 Deconvolution of chromatographic data for African sharptooth catfish plasma using MZmine for the m/z corresponding to PC(38:6) ................................ ........ 51 1 7 Dissertation chapters arranged within the first three major steps of the lipidomics workflow ................................ ................................ ............................. 51 2 1 Strategy for iterative exclusion based data dependent topN analysis (IE ddMS 2 topN). ................................ ................................ ................................ ...... 72 2 2 Selected precursor ions for Red Cross plasma compared between t he first injection and second injection with iterative exclusion (IE) applied ..................... 73 2 3 Selected precursor ions for 6 repetitive injections using the traditional ddMS 2 approach an d iterative based exclusion (IE ) ................................ ...................... 74 2 4 Cumulative unique lipid molecular identifications using LipidMatch software across multiple data acquisitions with and without IE ................................ ......... 75 2 5 Boxplots of log transformed peak heights (base 10) from MZmine for unique lipid molecules identified in the first and seco nd injection applying IE ................ 76 2 6 Distribution of lipids identified using LipidMatch by lipid class using iterative exclusion based data dependent top5 (IE ddMS 2 top5) acquisitions ................. 77 3 1 Annotation of a phosphatidylcholine (PC) species outlining how to annotate each structural detail of glycerophospholipids ................................ .................. 104 3 2 General structure for plasmanyl and plasmenyl phospholipid species contain ing a glycerol backbone ................................ ................................ ........ 104


12 3 3 Options for open source software integration with LipidMatch in a lipidomics data processing workf l ow ................................ ................................ ................. 105 3 4 Workflow for using LipidMatch, with input and output folder structure and files. ................................ ................................ ................................ .................. 105 3 5 Simplified flow diagram of LipidMatch operations. ................................ ............ 106 3 6 Problematic cases which can arise when ranking lipids by the sum of fragment intensities ................................ ................................ .......................... 107 4 1 Open source lipidomics workflow employed in this study ................................ 130 4 2 Simplified schematic of LipidMatch Quant (LMQ) algorithm. ............................ 130 4 3 Examples of extracted mass chromatograms for the precursors and fragments of PC(16:0_20:4) and PC(18:2_20:4) using All Ion Fragmentation .. 131 4 4 Linear regression comparing the log 10 of lipid concentrations calculated usi ng different workflows and ions ................................ ................................ ............. 132 4 5 Bland Altman type plots showing differences in concentrations calculated using different methods and ions ................................ ................................ ...... 133 4 6 Peak integration of triglycerides with the most and least percent differen ce in concentrations calculated using peak height versus peak area. ....................... 134 5 1 D esign of heat treatment and flash frozen experiments ................................ ... 156 5 2 Principal components analysis (PCA) of cricket, worm and ghost shrimp features ................................ ................................ ................................ ............ 157 5 3 Schematic of enzymatic degradation of glycerophospholipids (GPL) and triglycerides (TG) ................................ ................................ .............................. 159 5 4 Box plots of lipid s pecies concentrations in flash frozen (N) and heat treated (D) shrimp ................................ ................................ ................................ ......... 160 S 2 1 Distribution of full widths at half maximum (FWHM) of peaks for Red Cross plasma in positive and negative polarity ................................ ........................... 170 S 2 2 Selec ted precursor ions m/z and retention times for 6 repetitive injections using the traditional ddMS 2 approach and IE ................................ .................... 171 S 2 3 Magnefied glycerophospholipid (GPL) region and i dentified lipid molecules by LipidMatch for Figure S2 2 ................................ ................................ ............... 172 S2 4 Cumulative unique lipid molecular identifications using LipidSearch software ac ross multiple data acquisitions for traditional and IE acquisition ................... 173


13 S2 5 Number of precursors selected for fragmentation across sequential injections after applying IE to Red Cross plasma lipid extracts. ................................ ....... 174 S2 6 Boxplots of log transformed peak heights for lipids in each of 6 iterative injections applying IE ................................ ................................ ........................ 174 S2 7 Graphs representing MS/MS spectral quality over sequential injectio ns with and without applying IE ................................ ................................ .................... 175 S2 8 Boxplots of log transformed peak heights for diglycerides in each of 6 iterative injections applying IE ................................ ................................ .......... 176 S 9 Distribution of lipids identified using L ipidMatch by lipid class using IE ............ 177 S2 10 Multilinear regression for predicting retent ion times of diglycerides based on t otal carbons and degrees of unsaturatio n of fatty acids ................................ 178 S2 11 Multilinear regression for predicting retention times of triglycerides based on total carbons and degrees of unsaturation of fatty acid s ................................ .. 178 S 3 1 More indepth flow d iagram of LipidMatch operations ................................ ....... 184 S3 2 Set overlap for LipidMatch, MS DIAL, and GREAZY in negative polarity analysis of Red Cross plasma. Visualization of sets based on UpSet .............. 185 S3 3 Set overlap for LipidMatch, MS DIAL, and GREAZY in positive polari ty analysis of Red Cross plasma ................................ ................................ .......... 18 6 S 3 4 Pie chart of lipid classes and the number of each identified by LipidMatch in negative polarity ................................ ................................ ............................... 187 S 3 5 Pie chart of lipid classes and the number of each identified by LipidMatch in positive polarity ................................ ................................ ................................ 187 S 4 1 A comparison of f old change (greater than 1) versus percent difference for the traditional equation for percent difference and the one used here .............. 191 S4 2 Depiction of MS/MS s cans using AIF and using DDA for two lipid isomers ...... 192 S4 3 P eak integration of the triglycerides with the most and least percent difference in concentrations calculated using peak height versus peak area. .. 193 S4 4 Extracted ion chromatograms (EICs) of PC(16:0_20:5) and PC(18:0_20:4) .... 194 S 5 1 Invertebrates in Denator Ma intainor cartridges ................................ ................. 196 S5 2 R elative concentrations and numbers of species of each lipid type in earthworm, ghost s hrimp, and common house cricket ................................ ..... 197


14 S5 3 Total PE, LPE, PC and LPC concentrations for flash frozen and heat treated cricket, earthworm, and shrimp samples ................................ .......................... 198 S5 4 Total ether linked PE, LPE, PC and LPC concentrations for flash frozen and heat treated cricket, earthworm, and shrimp samples ................................ ...... 199


15 LIST OF OBJECTS Object page 2 1 Additional supplemental information for Chapter 2 ................................ .......... 169 3 1 Additional supplemental information for Chapter 3 ................................ .......... 179 5 1 Additional supplemental information for Chapter 5 ................................ .......... 194


16 LIST OF ABBREVIATIONS AGC Automated gain control AIF All ion fragmentation ANOVA Analysis of variance APCI Atmospheric pressure chemical ionization APPI Atmospheric pressure photoionization BEH Ethylene bridged hybrid BFF Blank feature filtering CcO Cytochrome c oxidase CE Cholesterol ester Cer Ceramide CID C ollision induced dissociation CLA Conjugated linoleic acid C sv Comma separated values D Heat treated samples (with denator device) D1hr Heat treated samples (with denator device), and samples sat on ice for one hour prior to extraction Da Dalton DC Direct current DDA Data dependent analysis ddMS2 topN Data dependent top N DG Diacylglyceride DIA Data independent analysis EDTA Ethylenediaminetetraacetic acid EIC Extracted ion chromatogram


17 ELIFE European Lipidomics Initiative: shaping the life sciences ESI Electrospray ionization ether LPC Plasmenyl and/or plasmanyl lysophosphatidylcholine ether LPE Plasmenyl and/or plasmanyl lysophosphatidylethanolamine ether PC Plasmenyl and/or plasmanyl phosphatidylcholine ether PE Plasmenyl and/or plasmanyl Phosphatidylethanolamine ether TG Plasmenyl and/or plasmanyl triglyceride FA fatty acid FDR False discovery rate FT ICR Fourier transform ion cyclotron resonance FWHM Full width at half maximum GalCer Galactosylceramide GC Gas c hromatography Glog Generalized log transformation GPL Glycerophospholipid HCD Higher energy collision induced dissociation HESI Heated electrospray ionization HF Ultra high field orbitrap HILIC Hydrophilic interaction chromatography HMDB Human Metabolome Database HPLC High performance liquid chromatography HRMS High resolution mass spectrometry IACUC The Institutional Animal Care and Use Committee IE Iterative exclusion IM Ion mobility


18 InChI International Chemical Identifier IS Internal standard KEGG Kyoto Encyclopedia of Genes and Genomes LC Liquid c hromatography LDA Lipid Data Analyzer LIPID MAPS Lipid Metabolites and Pathways Strategy LMQ LipidMatch Quant LPC Lysophosphatidylcholine LPE Lysophosphatidylethanolamine LPL Lysoglycerophospholipid LPS Lysophosphatidylserine LTQ Linear ion trap m/z M ass to charge ratio MS Mass spectrometry MS/MS Tandem mass spectrometry N Flash frozen samples (not heat treated) N1hr Flash frozen samples (not heat treated), and samples sat on ice for one hour prior to extraction NCE Normalized collision energy NIST National Institute of Standards and Technology NL Neutral loss OxLPC Oxidized lysophosphatidylcholines OxPC Oxidized phosphatidylcholine OxPE Oxidized phosphatidylethanolamine OxTG Oxidized triglyceride


19 OzID Ozone induced dissociation PA phosphatidic acid PC Phosphatidylcholine PC1 Principle component 1 PC2 Principle component 2 PCA Principle components analysis PE Phosphatidylethanolamine PG Phosphatidylglycerol PI Phosphatidylinositol PLA Phospholipase A PLC Phospholipase C PLD Phospholipase D PMe Phosphatidylmethanol ppm Parts per million PS Phosphatidylserine qTOF Quadrupole time of flight RC Red Cross RF Radio frequency rpm Revolutions per minute RSD Residual standard deviation (also termed coefficient of variation (CV)) SECIM Southeast Center for Integrated Metabolomics SLIM Structures for lossless ion manipulation SM Sphingomyelin SRM Standard reference material


20 t 0 Time of collection TG Triglyceride UHPLC Ultra high performance liquid chromatography UV Ultraviolet v:v Volume to volume v:v:v Volume to volume to volume


21 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy A MODULAR OPEN SOURCE WORKFLOW FOR LIPIDOMICS USING LIQUID CHROMATOGRAPHY HIGH RESOLUTION TANDEM MASS SPECTROMETRY (UHPLC HRMS/MS) By Jeremy Paul Koelmel D ecember 2017 Chair: Richard A. Yost Major: Chemistry Lipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of hea lth and disease. The clinical utility of lipidomics is derived from the diverse biological roles of lipids. This diversity in lipid function is enabled by the diversity of lipid structure s and the wide dynamic range of lipid concentration s thus making the execution of lipidomic experiments analytically challenging. Advances in ultra high performance liquid chromatography and high resolution tandem mass spectrometry (UHPLC HRMS/MS) enable the acquisition of data covering a large portion of the lipidome in a single experiment Data processing tools for mining this information have only recently emerged, and strategies are still needed to more accurately and comprehensively annotate and quantify lipid ions from UHPLC HRMS/MS data Therefore we developed a suite of open source lipidomics software tools and novel strategies which cover the majority of the lipidomics workflow including : increasing lipid fragmentation coverage, improving peak picking, removing background ions, combining redundant ion inform ation,


22 annotating lipid species and quantifying lipid species using class specific lipid standards. For improved lipid coverage, w e introduce LipidMatch, a lipid identification software leveraging the largest open source in silico fragmentation library LipidMatch covers over 250,000 lipid species spanning over 56 lipid types. To further increase lipid coverage, we employ a n R script tool (termed iterative exclusion) for generating exclusion lists from ions selected for fragmentation in previous injectio ns resulting in 69 % more lipi d identifications in positive mode analysis of human plasma For improved data quality we developed a tool based on an algorithm for removing peaks present in extraction blanks, and a unique peak picking strategy using MZmine which reduced the residual standard deviation between samples. For automated relative quantification of the lipidome, we developed another tool LipidMatch Quant, which match es standards to analytes based on lipid class, adduct, and retention t ime; hence accounting for both lipid structural and regional chromatographic effects on ionization efficiency. Through increased lipid coverage and more accurate measurements these tools can increase the probability of lipid biomarker discovery and understanding disease etiology. Parameters for each tool allow the user to optimize and tailor tools depending on the vendor, instrument and experimental design employed S ince e ach of these is bu ilt as a separate modular tool users can integrate them into ot her lipidomics software packages to design a workflow based on their expertise, application, finances, and instrumentation. Finally, we have integrated the tools into a single software applica tion LipidMatch Flow, which has been designed to cover all steps of the data processing workflow up to lipid annotation for more robust and rapid analysis.


23 CHAPTER 1 INTRODUCTION L ipidomics, an O verview A comprehensive study of biomolecules and biopolymers, such as metabolites and proteins, can be useful in answering key biological questions, including questions regarding the mechanism of a disease. While genomics and transcriptomics generally describe th which are the small molecules generated through metabolism, are the most direct measure of as they relate directly to the phenotype. [1] Metabolomic studies are based on the concept that global anal yses of metabolites represent the physiological state of an organism at a given point in time and that the metabolite fluxes are sensitive to cellular change [2] Metabolites that are up or down regulated can be used directly as biomarkers for disease or exposure ; additionally, they can be used to und erstand the influence of disease on biological pathways. [3] One key subset of metabolites are lipids, which are either hydrophobic or amphiphilic molecules Lipidomics is a sub division of metabolomics. Lipids are ubiquitous and play essential and diverse biological roles in nearly all organisms, which mak es them likely candidates for biomarkers and under standing disease etiology The roles of lipids in biological s ystems include membrane structure and function [4] modulators of immune system function [5] energy storage [6] signaling [7 9] as targets of oxidation [10] alveoli functioning [11] and water retention in the skin [12] and eyes [13] Therefore lipids are likely biomarkers in o mics analyses due to their integration in multiple biological pathways and ubiquity across virtually all organisms and biological substrates. For example, possible lipid biomarkers have been discovered


24 in cardiovascular disease [1 4] chronic kidney disease [15] eating patterns, specific cancers [16] and as general markers for oxidative damage [10, 17] I n order to serve numerous biological roles lipid structures are highly diverse [18] Recently, biomedical re lated international initiatives have begun to establish databases to organize and document th e diversity of lipids founded in 1989 [19] Lipid Metabolites and Pathways Strategy (LIPID MAPS) in the USA founded in 2003 [20, 21] and the European Lipidomics Initiative: shaping the life sciences (ELIFE) founded in 2004 [22] One of the major lipid databases, LipidMaps, contains over 45,000 lipids (both computer si mulated and experimentally confirmed) [23, 24] However, these databases only begin to scratch the surface of the lipidome diversity. The lipidome the entire collection of individual lipid species in cells, ti ssues or biofluids has been estimated to be composed of 1,000 to more than 180,000 molecular lipid species [25, 26] but ma ny of these species are likely very low in abundance or have not been observed [27] Furthermore, t hese calculations and experimental measurements do not take into account all possible double bond positions, backbone substitutions, and stereochemistry [25] As a result, when all subtle structur al differences are accounted for s everal million potential lipids are possible It is therefore not surprising that a significant portion of the human genome is devoted to synthesize, metabolize and regul ate this lipid diversity [28] The diversity of common fatty acids (FAs) and their potential arrangement on the backbone gives rise to an enormous array of lipid structures (see Figure 1 1 for phospholipids). For example, tr iglyceride s (TGs) contain 3 fatty acyl constituents attached to a glycerol backbone and with 38 experimentally verified f atty acyl


25 constituents 9,880 TG structures can be proposed without accounting for positional isomers or differences in double bond location. This calculation is a combination (order does not matter, because sn position is unknown) with repetition, shown in Equation 1 1 where n is the n umber of possible fatty acids and r is the number fatty acyl chains on a specific lipid (3 in the case of triglycerides). (1 1 ) Each lipid is characterized by the specific backbone and corresponding f atty acyl constituents for example PC(16:0/18:1(9Z)), can be read as a phosphatidylcholine (PC) head group (PO 4 CH 2 CH 2 N(CH 3 ) 3 ), and two f atty acyl constituents attached, one with 16 carbons and no double bonds (16:0) in the sn 1 position and the other with 18 carbo ns and one cis double bond on the 9 th carbon from the backbone (18:1 (9Z) ) in the sn 2 position A generalized structure for phospholipids is shown in Figure 1 1. The structural diversity of lipids poses a challenging analytical problem for lipidomics analy ses For example, both identification and quantification in metabolomics is routinely done with labeled external or internal standards for each metabolite of interest. However, i n lipidomics, internal standards have not yet been synthesized to cover even a small portion of the lipidome and purchasing the diverse species that are available can be prohibitively expensive. Another challenge is the separation of numerous lipids with only subtle differences in chemical and physical properties. The separation an d unique characterization of these molecules is also important for identification and quantification. Therefore, lipid diversity and lack of standards poses a unique challenge to comprehensively and accurate ly measuring lipids


26 Lipidomics Workflow Each st ep in the lipidomics workflow influences the coverage and accuracy of lipid measurements. The lipidomics workflow consists of four major steps, which are sample preparation, data acquisition, data processing, and statistics/ interpretation (Figure 1 2). Sample Preparation Sample preparation consists of the following stages: sample collection, storage, homogenization, and extraction. During these steps a major factor influencing the accuracy of measurements is sample degradation due to physical factors su ch as oxidation during exposure to light and air [29] and biological factors such as enzymatic biotransformation [30] Therefore enzyme inhibitors, [30] oxidation inhibitors, [31] heat treatment, [32] sample preparation and storage under cryogenic temperatures, and/ or other technique s for stabilizing the lipidome are necessary ; however, these techniques a re not often used. Sample extraction can also influence the physical and enz ymatic transformation s of lipids. F or example extractions which are highly acidic or alkaline can induce the hydrolysis of lipids, [33] and primary alcohols such as methanol and ethanol can form methylated and ethylated glycero phospho lipids in the presence of phospholipase D [34] Additionally because lipids can range from acidic to neutral, zwitterionic to uncharged, and p olar to relatively non polar, the extraction conditions and solvents may preferentially extract certain lipid classes or species over others. [33, 35 37] The Bligh Dyer [38] and Folch extraction procedure s [39] are among the most common extraction procedures used in lipidomics, and while multiple comparisons of lipid extraction methods have been made, the benefits of each extraction procedure is


27 often sample dependent. [33, 35 37] Both Bligh Dyer and Folch extraction s capture a majority of the lipidome, with Folch extractions having higher extraction efficiencies but using more solvent and being slightly more time intensive. For Bligh Dyer and Folch extractio ns highly acidic or highly polar lipids such as phosphatid ic acid (PA) and fatty acids, respectively, are often not extracted in high enough concentrations for detection. For PA and other lipids which are not easily driven into the organic phase, salts s uch as NaCl can be used to drive the lipids into the organic layer. [40] However the use of these salts can reduce ion signals through competitive adduct formation. For fatty acids either derivatization and analysis by GC MS or analysis of polar fractions in LC MS are often used. [41] Therefore the Bligh Dyer extraction protocol ( 1 : 2 v: v, chloroform:methanol) and Folch extraction protocol ( 8 : 4:3 v: v, chloroform : methanol : water) were used in this work. Data A cquisition: Reverse P hase L iquid C hromatography The second step of the lipidomics workf low is data acquisition (Figure 1 2). As with extraction, the choice of column, elution gradient, mobile phase solvents, and mobile phase additives can drastically alter the coverage of the lipidome and the amount of information obtained for each lipid spe cies. For example, reverse phase chromatography is the most commonly employed technique for lipidomics. Reverse phase chromatography's popularity in lipidomics stems from its ability to retain analytes according to non polar interactions with the stationar y phase, enabling the separation of analytes by polarity T herefore, lipi d species can be separated by lipid class, fatty acyl constituents and even regioisomers. [42] On the contrary, more polar lipids such as fatty acids are not retained as well in reverse phase chromatography and hence methods such as normal phase chromato graphy [43] or hydrophilic interaction chromatography


28 (HILIC) [44] are often used. While reverse phase and normal phase chromatography can separate out lipids with different fatty acyl constituents, techniques such as HILIC separat e lipids out by lipid class. Therefor e, quantification by lipid class is simplified in HILIC at the expense of identification and quantification of lipids at the molecular level. When molecular species information is desired, HILIC can be placed in tandem with ion mobility (IM), which separates ions by their collision cross section a property that is characteristic of ions size, shape, and charge for given experimental conditions [45] In this work reverse phase chromatography using a C18 co lumn was employed, because it is able to separate most lipid species (as compared to other chromatographic techniques) under short chromatographic runs. Another important factor for determining the lipid coverage in a lipidomics workflow is the choice of additives and mobile phase conditions, which in turn affect which lipids will ionize in electrospray ionization (ESI). For example, most polar lipids readily form positively charged proton ated ions, and negatively charged deprotonated ions depending on th e ESI polarity Neutral lipids often do not form protonated ions at high efficiencies, and will best be observed with the addition of cations (lithium) or as ammoniated ions with the addition of ammonium salts to the mobile phase In addition, lipids such as phosphatidylcholine and many sphingolipids do not form deprotonated ions easily but form acetate, formate, and chlorinated adducts readily. [46] In this work, ammonium formate was added as a buffer to control pH and to form ammonium or formate adducts for species which are not readily protonated or deprotonated. D ata Acquisition: Volatizing and Ionizing Lipids with Electrospray One of the most important aspects of mass spectrometry is the ionization source, which determines which ions will and will not be observed The p arameters and choice


29 of the ionization source impact linear range of detection, sensitivity, and other important factors. Ionization sources which can ionize and volatilize neutral relatively high molecular mass compounds are necessary in LC MS analysis of lipids. In the 1970s and 1980s UV detection with reverse phase chromatography was used for lipid analysis because an ionization source conducive to lipid analysis lipid analysis was not widely available [42] It was no t until the late 1980s w ith the advent of electr ospray i onization (ESI) and atmospheric pressure chemical ionization (APCI) that lipids could be analyzed by mass spectrometry Mass spectrometry has become the predominant technique for lipidomics because of its ability to detect low abundant species (high sensi tivity), to q uantify lipids (proportional signal, with minimal variance across classes compared to UV), and to assign specific structures to co eluting isobaric species (high specificity due to using exact mass and c ollision induced fragmentation) [47] T hree major techniques are currently used as ion sources for lipidomics : atmospheric pressure photoionization (APPI), atmospheric pressure chemical ionization (APCI), and electrospray ionization (ESI). ESI is the ionization source employed in this work, and has a higher sensitivity for many lipid compounds in comparison to APCI and APPI when using chemical modifiers such as ammonium formate [48, 49] ; however significantly lower linear dynamic range and correlation coefficients for calibration curves have been found in ESI LC MS of fatty acid and glycerol species [48] Chech and Enke [50] provide an in depth review of the mechanics and consequences of ESI fundamentals. Briefly, a negative or positive high voltage (2 5 kV) is applied to a capillary containing a dilute solution moving at a slow flow rate (0.1 10 L/min for high performance systems ). The charge partitioning produced by this electric


30 field generates a Taylor cone where liquid protrudes from the capillary tip. Once the Ra yleigh limit is reached where the surface tension of the liquid is overcome by the columbic repulsion of the high density of charges, the droplet disintegrates into smaller droplets which are directed to the capillary sampling orifice of the mass spectrom eter by ion lenses after the capillary and electrical gradients. As the smaller droplets evaporate rapidly at atmospheric pressure within the source ions are formed. The exact mechanism of ion formation is unknown, although several theories exist includi ng charge residue [51] ion evaporation [52] or chain ejection [52] Evidence exists showing that for large molecules, such as proteins, ions are predominantly formed by the charge residue mech anism. [53] In the charge residue mechanism droplets continue to undergo f ission as an effect of the Rayleigh limit being reached until a single charged molecule or no molecules exist in each droplet. Continued evaporation leads to single ions in the gas phase. For proteins chain ejection is another mechanism shown for extended o r unfolded conformations. In this mechanism, the protein migrate s towards the droplet surface due to hydrophobic groups on the protein, and a terminus gets expelled into the vapor phase, followed step wise by the remainder of the protein. [54] F or small molecules, evidence supports that ion formation is predominantly by ion evaporation [53] Therefore for lipids, which are relatively small molecules, the ionization mechanism is most likely via ion evaporation. In ion evaporation, th e field strength at the surface of the droplet becomes strong enough to eject ions after evaporation increases charge density at the surface of the droplet Lipids have a higher propensity for ejection via ion evaporation than other metabolites, because th ey are


31 relatively non polar, and hence accumulate at the surface of droplets between the air droplet interface. [55] The ionization efficiency, which is the number of ions produced in proportion to the number of molecules introduced to the ESI source, can dras tically differ for different lipid species and the concentration of lipids. One can imagine that at high concentrations certain lipids may out compete other lipids for migration to the air droplet interface, depending on the lipid structure. Additionally lipids readily form aggregates and micelles in solution phase, especially for non polar lipid species. [56] These aggregates will less readily be ejected from the droplets via ion evaporation hence decreasing ionization efficiency of the ions involved in aggregate formation. Aggregate for mation can have a significant impact on accurate quanti fic ation and sensitivity of various methods (see the section titled: Processing of LC HRMS/MS d ata ). For example, increasing concentration of lipid samples will increase overall signal intensity, but will lead to aggregate formation dependent on fatty acyl constituents and lipid class. [56] Therefore, the degree of ionization for each lipid species can differ, and despite this variation, internal standards do not exist to correlate ion signal to concentration for most species observed. In addition, the concentration of lipids within ESI droplets depends on the chromatographic region of elution, and hence aggregate formation will depend on the concentrations of coeluting lipids Therefore ionization efficiency based on aggregate formation is difficult to predict in LC MS, as aggregate formation may be controlled by coeluting lipids and additional factors other than a specific lipid species structure an d concentration A heated electrospray ionization (HESI II) probe was used as the ion so urce in this work HESI II provides higher auxiliary gas flows and temperatures than traditional


32 ESI due to the absence of a heat exchanger. The heated capillary aid s in solvent declustering and removal of neutrals through a highe r energy and more frequent collisions. Hence, less solvent clusters with analytes of interest are observed, which simplifies spectra, and ionization efficiencies may be increased. [55] Data Acquisition: Orbitrap Mass Spectrometers for Lipidomics While the lipidomics community is moving towards the use of high resolution mass spectrometers for lipidomics, as employed in this work, traditionally ESI has been used in tandem with triple quadrupole mass spectrometers for untargeted and targeted studies of the lipidome. This technique is stil l used due to its high sensitivity and relatively high specificity for targeted studies. Scanning modes and considerations for lipidomics employing triple quadrupoles are reviewed in Han et al. [57] The complexity of the lipidome and multiple arrangements of the same constituent building blocks to form different lipids leads to a high degree of overlap both in fragmentation and precursor ion mass to charge ( m/z ) values for differing lipid species. This difficulty imposed b y the overlap of fragment and precursor ions is exacerbated by the low resolution obtained by triple quadrupole instruments. The advent of high resolution hybrid mass spectrometers (e.g., time of flight and orbitrap mass spectrometers [58, 59] ) has allowed for enhanced lipid identification and structural annotation when compared to unit resolution mass spectrometers because of improved specificity, sensitivity, and reproducibility [60, 61] High mass accuracy (often sub ppm) can r educe the list of possible molecular formula s by providing the isotopic structure detail of precursor lipid ions. The addition of resolved isobaric fragment ions reduce s false positive and negative molecular identities [62] The advantages of high


33 resolution mass spectrometry for accurately a nnotating lipids have led to its increased use in lipidomics. Fourier transform ion cyclotron resonance (FT ICR) mass spectrometers provide the highest resolution of mass spectrometers used for lipidomics. Introduced in 1974, FT ICR mass spectrometers are expensive and have a large footprint, due to the use of a large magnet. For example, systems with 7 Tesla and 12 Tesla magnets cost about $800,000 and $2,000,000 dollars respectively, and weigh on the order of tens of tons. [63] Orbitrap mass spectrometers provide the second highest level of resolution, and under the same scan speeds can provide the same resolutions as 7 Tesla FT ICR instruments at a lower cost and significantly smaller footprint Orbitrap mass spectrometers have the unique characteristic that the mass resolution scales inversely to the size of the de tector (for reasons described in the section titled: Measuring high resolution with orbitrap mass spectrometers ), which is contrary to time of flight instruments where resolution is proportional to ion flight path (often achieved though increasing the leng th of the flight tube) and FT ICR instruments where resolution is proportion al to the strength of the magnet (often increased by increasing the size of the magnet). Therefore, to achieve resolution of 240,000 at a 786 ms transient for m/z 400, an orbitrap with inner dimensions of 20 mm is used, as apposed to the multi ton 12 tesla FT ICR acquiring data at the same transient with magnitude mode spectra obtaining a resolution of 170,000 [64] Due to the small size of the orbitrap, the actual resource cost to manufacture orbitrap mass spectrometers is low compared to tha t of time of flight and FT ICR instruments. As demand increases and technologies improve it can be predicted that


34 instruments containing orbitrap detectors will decrease in price at the same time as there is an increase in performance. Overcoming the d ifficulty of u sing ESI with o rbitrap m ass s pectrometers The components of a Q Exactive used in this work are shown in Figure 1 3 which can be referenced for all lenses, ion transmission elements, and detectors mentioned hereafter. From atmospheric pressur e in the ion max housing, a rotary vane pump establishes mbar pressure, followed by two turbo pumps, with a final pressure less than 8x10 8 mbar in the orbitrap. Low pressures allow for higher ion transmission efficiencies and longer mean free paths There fore, ion dynamics measurements, bas ed on fundamentals such as mass to charge ratios ( m/z ) are not perturbed by collisions. The bent 90 flatapole decreases the number of neutral molecules entering the instrument further reducing noise and improving sensi tivity. Initially orbitraps were considered unsuitable for continual sources such as ESI due to the necessity of injecting short (less than one s) ion packets for high resolution analysis. In 2003 Hardman and Makarov proved that ion traps could be used for the collection and injection of these tight ion packets, developing an interface between orbitrap analyzers and ESI sources. [65] A curved RF only linea r trap quadrupole (C Trap) is applied in the Q Exactive [59] which uses N 2 or other cooling gas es to reduce ion kinetic energies and collect ions in a thin thread confined axially by applying 200 V to both the split lens (lens 7 ) and C Trap entrance lens (lens 8 ) The C Trap can also be used for automated gain control (AGC), where ions are accumulated in the C Trap until a certain user defined intensity is obtained (or until a user defined time is reached), after whi ch ions are ejected into the orbitrap. For AGC the image current in the orbitrap is used to predict the number of ions in the C Trap. Since the ion flux can drastically


35 change across a chromatographic run this prediction of ion flux to the C Trap is en abled by implementing rapid microscans to the orbitrap. The occurrence of these microscans is not visible to the user and is automat ically triggered in the Q Exactive when there is a dramatic change in ion flux across time. By controlling the number of ions sent to the detector using AGC the signal for trace ions can be increased by increasing trapping times Likewise, the signal for highly abundant ions can be reduced, by decreasing trapping times. Reducing the number of io ns entering the orbitrap limit s signal saturation and drift in resolution due to ion coalescence, where ions close in mass lock phases (axial frequency) [66] A high deflection voltage on the split lens ( transmission into the C Trap. Therefore a precise start and end injection time (IT) into the C Trap is obtained. Ions are either ejected from the C Trap to the orbitrap once the AGC is reached, or once the maximum IT is reached. Measuring h igh r esolution with o rbitrap m ass s pectrometers Using an offset Z lens (lens 9) to remove carrier gas, the ions are ejected into the orbitrap with a time of flight (t inj ) between the C Trap and the orbitrap governed b y Equation 1 2 [67] where L eff is the effective length of the flight path. Hence the arrival of ion packets is proportional to the square root of the m/z and ions with lower m/z arrive before ions with higher m/z (1 2 ) As ion packets are injected into the orbitrap, the electrical field of the inner electrode is ramped up, ions are pulled closer to the inner electrode and the ions begin


36 to oscillate around the inner electrode. Once the ramping voltage is stabilized, the ions reach equilibrium between their centrifugal force of rotation around the inner electrode (inertia) and the D C potential of the inner electrode (cen tripetal force). These oscillations are similar to how planets circulate the sun, where the centripetal force in the case of planets is gravity. By balancing centripetal and centrifugal force a simplified characteriz ation of the radius of the ion motion around the inner electrode (r m ) can be described as in Equation 1 3 where eV is the ions kinetic energy and eE is the inward force of the electric field on the ion. [68] (1 3 ) Based on Equation 1 3 the radius of the ion path is independent of m/z and cannot be used to measure m/z directly. Radial ion motion is therefore not important for measuring m/z but it is important for trapping ions in thin circular bands surrounding the inner electrode. With a sector instrument, which is technically a kinetic energy analyzer, the kinetic energy and radial component of the electric field must be matched to control the trajectory of the ion packet through the sector. Similarly, if the kinetic energy and the rad ial component of the electrical field are not matched in an orbitrap th e n ions will not follow narrow circular trajectories around the inner electrode and will either collide with the electrode or form broad discs after ion motions are averaged. In addit ion to the radial distance, the radial oscillations cannot be used to measure m/z to a high degree of accuracy because the oscillations depend on the velocity and initial radius of the ion trajectories. Because ion packet s entering the orbitrap from the C Trap consists of ions with varying velocities, their frequencies differ,


37 and the ion packets quickly form a circular distribution of ions around the electrode. Therefore, one of the hallmarks of the orbitrap, is to measure resolution using axial oscillatio ns in the horizontal direction (Figure 1 3 ). The orbitrap consists of two outer electrodes, shaped like "cups" facing each other, and an inner spindle shaped electrode. These electrodes form a quadrologarithmic electric field described in Equation 1 4 wh ere r and z are the cylindrical coordinates, k is the field curvature, R m is the characteristic radius, and C is a constant. While axial oscillations could theoretically be induced by applying excitation from either of the outer electrodes, ions are inject ed off axis (Figure 1 3 ), and due to the shape of the shape of the quadrologarithmic electric field described in Equation 1 4 [68] ion packets automatically begin oscillating axially without any additional force. One of the advantages of off axis injection versus injecting ions to the equator of the orbitrap (center of the orbitrap) and applying excitation, is that in the absence of excitation detection can start almost immediately, without waiting for the electronic s to stabilize. (1 4 ) When injected off axis, the initial force is from ions moving from areas with smaller distance between the inner and outer electrodes to the equator of the orbitrap where the greatest distance between the inner and out er electrodes occur s. Thereafter, ions experience a restorative force at the opposite end of the orbitrap as the ions continue their axial trajectory. Hence, without any dampening of oscillations due to the low pressures of the orbitrap (less than 8 x 10 8 m bar), oscillations take the form of a harmonic oscillator, with the frequency of oscillations , described in Equation 1 5


38 where k is the field curvature [69] The axial frequency , is experimentally determined by measuring an image current in the time domain, which is rapidly converted to the frequency domain using the Fast Fourier Transform (FFT) algorithm [67] (1 5 Because the orbitrap mass spectrometer directly measures m/z using FFT of axial oscillations which are independent of ion energies and spatial distribution, and the electrical field can be tightly controlled in the orbitrap geometry, masses can be measured with very high accuracy and resolution. The resolving power is a function of the smallest m /z difference between two ions in which the peaks can be deconvoluted. Since resolving power is directly correlated to the number of axial oscillations, we can derive Equation 1 6 from Equation 1 5 where t is scanning time. Therefore, resolution is invers ely proportional to the square root of m/z meaning that higher mass ions a re measured at lower resolution and resolution is directly proportional to the scanning time within the orbitrap. Following the same logic, resolution is inversely proportional to t he period of oscillations, and hence shortening the period of oscillations can increase the resolution even at the same acquisition rates (scan time). Therefore, resolutions of a million have been achieved by reducing the size of the orbitrap. [ 70] However, w ith a fixed sized orbitrap, methodologies are limited to an optimization between the number of scanning events which can happen over a chromatographic peak and the lev el of mass resolution desired.


39 (1 6 ) Scanning f unctions for o btaining f ragmentation d ata Fragmentation gives both fatty acid moiety information and lipid class information, as neutral losses or fragment ions are often produced at the linkages between the backbone and constituents, for example at the sn 1, sn 2, and sn 3 ester linkages in Figure 1 1. Systematic querying of this fragmentation data can be used for untargeted studies, increasing identifications of unknown or unexpected species. The scanning functions used for obtaining fragmentation data contr ol both the accuracy and number of lipid identifications. For example, due to resolution being proportional to scanning time, only a limited number of ions can be isolated and fragmented across a chromatographic peak. Hence, a number of ions will not have fragmentation data, and cannot be identified. Therefore, novel strategies are need ed for acquisition of fragmentation data. To obtain fragmentation data, ions can first be isolated to a minimum of a 0.4 amu width using the Q Exactive quadrupole. [71] Targeted lists or data dependent acquisition (DDA) modes can be used to program ion isolation Alternatively, a ll ions can be simultaneously sent through the quadrupole for fragmen tation, which is termed All Ion Fragmentation (AIF). Fragmentation occurs in a n octapole trap via higher energy induced collisional dissociation (HCD), which produce s fragmentation similar to that in a triple quadrupole instrument, giving low mass ions and full fragment mass range acquisition. [72] Contrary to the name, HCD fragmentation energies are similar to those in c ollision induced dissociation (CID) The "higher energy" in HCD refers to the higher


40 RF field used to trap fragment ions as compared to CID experiments Users define the normalized collision energy (NCE), which is norm alized to the isolation center a s the energy required for fragmentation generally scales proportionally with m/z [73] The NCE can be stepped between 3 incrementing values for a single orbitrap scan. Fragmenting ions with stepped collision energies has the advantage of providing a larger numbe r of fragments, which in turn can lead to more confident lipid identification. In data dependent top n (ddMS 2 topn), the top n most intense peaks in full spectrum acquisition are selected and fragmented. Scanning events of ddMS 2 top10 are shown in Figure 1 4 as well as scanning events for AIF. For ddMS 2 topn, additional parameters include loop count (top n), underfill ratio, apex trigger, exclude isotopes, and dynamic exclusion. The underfill ratio sets an intensity threshold based on the AGC target. Any ions falling below this threshold will not be selected for fragmentation by tandem mass spectrometry ( MS/MS ) and therefore in a top n experiment, often not all top n ions will be selected and fragmented. The apex trigger sets a lag time between the obser vation of a peak and isolation for fragmentation in order to isolate ions at the apex of the chromatographic peak and obtain maximum ion intensity. Dynamic exclusion places ions selected for fragmentation on an exclusion list for a given amount of time bef ore they can be selected again for fragmentation, hence increasing the number of ions for which MS/MS scans are obtained. For AIF the entire ion population is subjected to fragmentation simultaneously, and therefore the number of precursors with fragment ation information is not limited as in ddMS 2 topn Identification using AIF is problematic because precursor fragment relationships are lost due to precursors not being isolated in a single Da window.


41 Therefore, fragments could come from any precursor, whi ch increases the chance of false positives if only exact mass of fragments are used for identification, and false negatives if dot product identification is used. Deconvolution using chromatographic peak shape of precursors and fragments can be used to rec onstruct the precursor fragment relationship, reducing the number of false positives. Deconvolution can be difficult in the case of lipidomics where there is a high degree of exact mass overlap in narrow retention time regions both in the precursor and fra gmentation space. Therefore, because of the overlap in fragmentation from various precursors when acquiring AIF data, both dot product, reverse dot product, and deconvolution based identification will lead to an increase in false negatives. Processing of LC HRMS/MS Lipidomics Data Data acquisition often contains the most standardized and reproducible steps of the li pidomics workflow. However, data processing of lar ge volumes of mass spectra acquired using LC HRMS/MS is a major bottleneck in the lipidomics workflow for many labs Currently there is no consensus on which software tools and algorithms are optimal for data processing in lipidomics T his work introduces software tools and strategies aimed to cover the entire data processing w orkflow. The data processing workflow consists of 3 major steps, which are: feature finding, lipid annotation, and lipid quantification (Figure 1 2) The first step of the workflow is to detect features, which are molecules or groups of molecules with sim ilar polarity and adduct masses. To understand feature finding, it is important to understand the LC HRMS data format There are 3 dimensions to a full scan dataset : mass to charge ( m/z ) retention time, and intensity. An example 3 dimensional dataset is s hown for adipose tissue acquired in positive ion polarity in


42 Figure 1 5 Feature finding in this work only employ ed full scan data using MZmine 2.0, and hence no MS/MS data is used to align features. The first major step of feature finding employed in MZ mi ne 2.0 is chromatogram building In this step the 3 dimensional data set is cut into 2 dimensional slices consisting of retention time and intensity (or relative abundance, Figure 1 5 ). In these slices mass is held constant, and hence these slices are term ed reconstructed mass chromatograms. The m/z data is binned or otherwise m/z values are chosen across the mass range, and a reconstructed mass chromatogram exists for each m/z interval. After chromatogram building, the reconstructed mass chromatograms are deconvoluted (Figure 1 6 ) This feature identifies and integrates multiple peaks sharing the same or similar m/z val ue within a slice In this work a local minimum is used for deconvolution where a peak is found between two local minima in intensity. Va riables such as minimum ratio of the top of a peak to an edge (the local minimu m) can be controlled to limit the deconvolution of noise into individual peaks A list of peaks is then obtained with m/z values, retention time values, and intensities (peak ar eas and peak heights). These peak lists can be further filtered by reducing peaks from blanks, removing isotope peaks, and combining peaks from multiple adducts of the same molecular species. In lipidomics, often the interest is in comparing multiple samples. Therefore, peak lists across multiple samples are aligned using a m/z and retention time tolerance A dditional factors such as peak shape can also be accounted for during alignment. After alignment, often there will be missing peaks for some samples, while signal was detected for other samples. Therefore gap filling is the final step employed Gap filling lowers the thres hold needed to determine a peak and searches for missing


43 peaks across respective samples. Gap filling is an important step, because assigning zeroes to missing peaks can have significant effects on downstream statistical analysis. After features are detected and aligned, the MS/MS spectra must also be aligned to each feature. As previously described MS/MS is necessary for lipid identification, because lipids have numerous fatty acyl isom ers and these isomers can co elute (Figure 1 6 ). The second and third step of data processing are lipid annotation using the MS/MS data and quantification ; these steps are described in detail in Chapter 3 and Chapter 4, respectively. The major difficulty w ith annotation and quantification is the lack of internal standards. Only a negligible portion of known lipids are covered by the currently available l ipid standards due to the diversity of lipid structures In addition, even with currently available lipid standards, routine analysis is limited by the cost prohibitive nature of purchasing all available and relevant lipid standards. In annotatio n, the best practice is implement ation of retention time and MS/MS l ibraries for each lipid analyte based on data acquired for lipid standards on the same instrument and column used for analysis. Because of the lack of internal standards, in silico fragmentation libraries, with limited to no retention time criteria, are often used for annotation of lipids in untargete d studies. For absolute quantification, matrix matched standards which cover the dynamic range of lipid concentrations would be necessary to account for extraction efficiencies, ionization efficiencies, and any effect of data processing on quantitative va lues of each lipid species. While this is commonly employed in metabolomics, [74] in lipidomics, because of the lack and cost of lipid standards, only relative quantification can be performed In addition, even using long chromatographic separations and high resolution mass spectrometers there is still


44 often overlap between lipid isomers (Figure 1 6 ) Hence, quantified values are often an average across multiple lipid species. In untargeted lipidomics, class specific lipid internal standards, or multiple lipid internal standards per lipid class, are used to obtain relative quantified values for each l ipid species. These standards must be exogenous (often odd carbon numbers) or isotopically labeled to not overlap with endogenous lipid species. For initial clinical applications to determine biomarker candidates or disease etiology where relative changes are of concern relative quantification is often sufficient [75] Further clinical trials or studies for validation which requir e higher confidence in lipid identifications and absolute quantification are likely to be performed as targeted analyses with internal standards Biological Interpretation of Lipid Perturbations After data processing, the final, and currently one of the most problematic steps, is data interpretation. Currently, the bulk of knowledge on lipid biochemistry has not been compiled into easily searchable pathway databases. Furthermore, p athways containing lipids often only include lipid class, not fatty acid co nstituents. However, lipids within the same class, containing different fatty acid constituents, can have drastically different biological roles. [76 79] Therefore, the interpretation of shifts in lipid concentrations ofte n relies on experts. Even this interpretation by experts is limited because the specific biological functions for the majority of lipid species detected are unknown. Therefore, in most cases, lipidomics provides a wealth of information at the level of lipi d class and fatty acyl constituents, but only the shifts in lipid class and total free fatty acids are used for interpretation. Because the technology allowing lipid researchers to obtain and process data with extensive coverage of the lipidome is still co ntinuing to emerge, it can be expected that our understanding of lipid biology will


45 continue to grow and this information will be used to expand lipid pathway databases. This rapid expansion of pathway databases, due to introduction of robust low cost mea s urement techniques is exemplified in the field of genomics and proteomics. [80] A more detailed description of the technical difficulties that must be overcome to achieve this increased coverage and accessibility of l ipid pathway databases is described in Chapter 6: Conclusions and Future Perspectives. Dissertation Overview Lipidomics requires novel data acquisition strategies and data processing algorithms to accurately and comprehensively measure the lipidome. While current lipidomics papers employing LC HRMS/MS frequently report hundreds of lipids, there are possibl y thousands, to even tens of thousands, of lipids within their samples. Increasing coverage of the lipidome increase s the probability of find ing clinical biomarkers and determining the etiology of disease. In this dissertation, I introduce a number of strategies and software which can improve lipid coverage and the accuracy of measurements. The strategies and software introduced here cover the first three major steps of the workflow (sample preparation, data acquisition, and data processing) as shown in Figure 1 7. Chapter 2 describes a technique termed iterative exclusion which we ha ve automated and applied to lipidomics applications Using this data acqu isition technique we increase the coverage of lipid features which have MS/MS fragmentation data, and hence increase annotation of lipid ions up to 2 fold. In Chapter 3 I introduce LipidMatch, an open source software which contains in silico fragmentatio n of over 250,000 lipid species, including oxidized li pids. This software also enabled identification using AIF, a technique where fragmentation is obtained for all ions without selection in a quadrupole. This software both increase s the


46 coverage of the lipidome through increased coverage of lipid MS/MS spectra in the in silico libraries and more accurately annotate s lipids based on experimental MS/MS data. Often MS/MS data do not provide information regarding fatty acyl constituents, fatty acyl positio n on the backbone, double bond position, or double bond configuration ( cis versus trans ). Therefore, unlike other open source software, LipidMatch uses an annotation style which only denote s structural information known based on the MS/MS information, and indicates if multiple lipids are identified under a single feature. LipidMatch was compared with other open source software across the same dataset for validation. After annotation, the next data processing step is quantification. Because standards do not exist to cover even a small portion of the lipidome, class representative internal standards are used. In Chapter 4 a software is introduced for automatically selecting which internal standards to use to quantify each lipid feature. The selection of inte rnal standards is based on limiting ion suppression e ffects and differences in ionization efficiencies between lipi d structures. The software, LipidMatch Quant was applied to show how different data processing methods can significantly affect the resultin g concentration calculated. The first step in the lipidomics workflow, and often the most overlooked is sample handling and preparation, where enzymatic activity can drastically change the lipid profile from the native profile Therefore we applied the l ipidomics workflow discussed in Chapters 2, Chapter 3, and Chapter 4 to determine the differences in lipid profiles of invertebrate environmental sentinel species with and without heat treatment. The invertebrates were small enough to place into cartridges and apply heat treatment


47 as the mode of eu thanization. Evidence is presented that sho ws that heat treatment drastically or completely reduces enzymatic activity. This dissertation ends with an outlook of what must be accomplished for lipidomics to contin ue to grow as an important technique in clinic al and other scientific fields. Future directions for my work including additional software tools and integration of all software tools into a single platform are discussed.


48 Figure 1 1 General glycero phos pholipid structure showing possible head groups (R) attached to the sn 3 position, and possible f atty acyl constituents attached to the sn 1 and sn 2 positions. Replacement of the sn 3 group with a fatty acid moiety gives the structure of a tr iglyceride (TG) a type of glycerolipid R emoval of a fatty acid moiety in the sn 1 or sn 2 position gives lyso phospholipids and lyso glycerolipids. Note that the R groups, chain lengths, and saturations, presented in the figure, are only the most common in mammalian syste ms. A much larger set of head groups, chain lengths, and saturations exist. [23, 24, 81] Figure 1 2 Lipidomics workflow for liquid chromatography high resolution tandem mass spectrometry (LC HRMS/MS)


49 Figure 1 3 Q Exactive schematic a dapted from [71] Lens 1 ( S Lens ) l ens 2 (S Lens exit len s ) l ens 3 ( inter flatapole lens or lens L0 ) l ens 4 ( lens L1 ) l ens 5 ( quad exit lens ) l ens 6 ( split lens ) l ens 7 ( C Trap entrance lens ) l ens 8 ( C Trap exit lens ) and l ens 9 ( Z lens ).


50 Figure 1 4 Schematic of scanning events for data dependent acquisition (DDA) and a ll i on f ragmentation acquisition (AIF) mode A) Scanning sequence f or DDA : each full scan is followed by 10 subsequent MS/MS scans B) for MS/MS in DDA each ion is isolated in the quadrupole (quad) and fragmented in the HCD, before measurement of m/z using the orbitrap C) In all ion fragmentation mode (AIF), sc anning events are user defined. In this case, a sequence alternating between ful l scans and AIF scans is shown, and D) i n AIF, all ions are fragmented without ma ss selection in the quadrupole Figure 1 5 Positive ion mode full scan dataset for adipose tissue from Mozambique t ilapia species Data acquired in house using the workflow described in this dissertation


51 Figure 1 6 Deconvolution of chromatographic data for African sharptooth catfish species plasma using MZmine for the m/z corresponding to PC(38:6). Different colors indicate different peaks which are deconvoluted. Blue annotations indicate lipid names based on MS/MS identification for each peak. Data acquired in house. Figure 1 7 Dissertation chapters arranged within the first three major steps of the lipidomics workflow


52 CHAPTER 2 EXPANDING LIPIDOME COVERAGE USING LC MS/MS DATA DEPENDENT ACQUISITION WITH AUTOMATED EXCLUSION LIST GENERATION 1 The Case for Iterative Exclusion in Lipidomics Experiments Electrospray ionization mass spectrometry (ESI MS) is the most widely employed ionization strategy for lipidomics [82] due to its ability to ionize the diverse range of structures and conce ntrations. However, isomeric species, for example, lipid species containing different fatty acid constituents with the same total number of carbons and degrees of unsaturation (e.g. PC(16:0_20:1) and PC(18:0_18:1)), cannot be separated using ESI MS alone. If this structural detail is desired, one solution is to employ liquid chromatography to separate the isomers for quantification based on polarity using reverse phase chromatography, in combination with tandem mass spectrometry (MS/MS) to identify the fatt y acid constituents based on fragmentation patterns. However, this strategy is problematic because in order to deconvolute more lipid featur es for quantification narrower chromatographic peaks are required thus limiting the number of MS/MS scans which can be obtained across the chromatographic peaks [83] Therefore, within lipid rich retention time and m/z regimes, numerous ions of different mass to charge ratios will b e ionized at the same elution time, but only a few can be selected for fragmentation in a single injection. For lipidomic experiments where the lipids of interest are unknown, heuristic rules have been developed to fragment ions. One approach is to select ions with the highest intensity for fragmentation, commonly referred to as data dependent topN (ddMS 2 topN). Due to concentration bias, this strategy could miss important less abundant lipid species, such as d iglyceride s and Published by: Journal of the American Society for Mass Spectrometry


53 phosphatidylinositols in plasma which are both important signal molecule classes [84, 85] A strategy which overcomes the drawback of traditional ddMS 2 topN, in terms of the limited number of MS/MS spectra acquired at any given retention time, is t o continuously repeat ddMS 2 topN analysis on the same sample, excluding previously selected precursors ions in each sequential analysis. Theoretically, iterative repeat injections can be used to acquire MS/MS of all precursor ions above background signal, providing a substantial wealth of information for identification. A schematic of this technique, iterative exclusion (IE), is shown in Figure 2 1. While still uncommon, this technique has been applied in the proteomics community. In 2009, Bendall et al. de signed a proteomics software approach for their strategy termed iterative exclusion mass spectrometry (IE MS) [86] which excluded all ions in previous runs regardless of assignment. Using this technique, Bendall et al. identified 30% more proteins after 5 IE MS acquisitions, compared to 5 repetitive traditional data dependent scans. Rudomin et al. [87] were also able to identify 49% more proteins using this strategy. IE has been applied to LC/MS approaches with both LTQ Orbitrap [88] and qTOF platforms [86] and has been shown to be advantageous for proteomic applications. Examples employin g this approach include using IE to study post translational modifications of proteins [89] discover previously unknown human embryonic growth promoters [86] identify genital track markers [88] characterize Matrigel marketed as a basement membrane matrix for stem cell growth [90] and track pH induced protein changes [91] In all these applications, IE enabled the ability to characterize a greater variety of proteins, including trace proteins.


54 While numerous applications have shown the benefit of using IE across platforms for proteomics, most omics analyses do not take full advantage of IE. In part, this may be due to a lack of a simple software program capable of generating exclusion lists automatically as tr aditionally this is achieved manually. Furthermore, using available software, iterative acquisitions cost time and money, thus putting heavy emphasis on determining whether additional sample injections for IE are worth added instrument time. To this end, w exclusion lists from open source formatted data easily converted from various vendor formats; as a demonstration, we have applied it to lipidomics. IE Omics is advantageous over the IE MS script in providing multiple user parameters in a relatively simple interface and providing the ability to directly import multiple vendor formats. Recently, IE has been adapted to other omics fields, such as in lipidomics and metabolomics, as noted in Sandra et al. [92] and Edmands et al. [93] respectively. For lipidomics, IE type analyses have been used in direct infusion approaches by increasing the duration o f dynamic exclusion to the length of the analysis. For example, Nazari and Muddiman [94] used gas ph ase fractionation and dynamic exclusion to increase coverage of the lipidome, especially of low abundance species. Schwudke et al. [95] emulated precursor and neutral loss scanning using data dependent analysis with a dynamic exclusion and inclusion list based workflow for increasing the lipidome coverage. The success of IE type approaches for direct infusion supports the utility of IE for LC MS/MS. While direct infusion allows rapid biomarker discovery, LC MS/MS has been found to be more comprehensive [96]


55 To our knowledge, no research has shown the benefit of applying IE approaches to lipidomics versus traditional LC MS/MS. In addition, no omics studies have c ompared results across different matrices with varying amounts of features. Herein we report the use of our user customizable R script for IE to lipid extracts of both Red Cross plasma and substantia nigra brain tissue in both positive and negative polarit y. The results show that due to the spectral density of lipid species in a chromatographic run, especially in positive ion mode, a substantial benefit is obtained using IE for LC MS/MS based lipidomics. Methods: Lipidomics Workflow Implementing Iterative Exclusion Software Chemicals and M aterials Ammonium acetate and all analytical grade solvents (formic acid, chloroform, and methanol) were purchased from Fisher Scientific (Waltham, MA). All mobile phase solvents were Fisher Optima LC/MS grade (acetonitril e, isopropanol, and water). For Red Cross plasma the following lipid standards were used: tr iglyceride (TG(15:0/15:0/15:0) and TG(17:0/17:0/17:0)) purchased from Sigma Aldrich (St. Louis, MO) and lysophosphatidylcholine (LPC(17:0) and LPC(19:0)), phosphati dylcholine (PC(17:0/17:0) and PC(19:0/19:0)), phosphatidylethanolamine (PE(15:0/15:0) and PE(17:0/17:0)), phosphatidylserine (PS(14:0/14:0) and PS(17:0/17:0)), and phosphatidylglycerol (PG(14:0/14:0) and PG(17:0/17:0)) purchased from Avanti Polar Lipids (A labaster, Alabama). For substantia nigra samples the following standards were used: TG(15:0/15:0/15:0) from Sigma Aldrich, and PC(19:0/19:0), DG(14:0/14:0), SM(d18:1/17:0), Cer(d18:1/17:0), 13 C 2 cholesterol, PE(15:0/15:0), LPC(19:0), PG(14:0/14:0), and PS (14:0/14:0) purchased from Avanti Polar Lipids. All lipid standards were dilu ted prior to analysis in 1:2 (v: v) chloroform:methanol and a working


56 100 mg/L standard mix was then prepared by diluting the stock solution with the same solvent mixture. Sample P reparation Pooled Red Cross human EDTA plasma was purchased from the American Red Cross National Testing Laboratories (Detroit, MI) samples were stored at plasma aliquots (40 L) were thawed on ice prior to extraction. S ubstantia nigra samples were obtained from C57BL/6 mice. The Institutional Animal Care and Use Committee (IACUC # 20148382) at the University of Florida approved the use of all mice and procedures. The mice were housed with a 12 h light 12 h dark schedule and were provided food a nd water ad libitum Five month old mice were anesthetized using isoflurane vapors. The mice were sacrificed and whole brains were harvested immediately from the skull and placed on a glass petri dish. A scalpel was used to carefully remove the substantia nigra region of the brain. Upon receipt, the tissue was placed in a freezer maintained at 80C for storage. A Bel Art Mortar (Bel Art Scienc eware, Wayne, NJ) was used to pulverize the frozen tissue samples under liquid nitrogen The frozen tissue powder (10 20 mg, in triplicate) was weighed in homogenization tubes containing zirconium beads (0.7 mm diameter, BioSpec Products, Bartlesville, OK) Both Red Cross plasma and frozen substantia nigra tissue powder were extracted using the Folch method (2:1, v: v, chloroform:methanol) [39] Brie fly, 5 L of internal standard (IS) mix (100 mg/L) was spiked into the Red Cross blood plasma (40 L) on ice (IS info in chemicals and materials). For Red Cross plasma, 160 L of methanol and 320 L of chloroform was added to all samples. Samples were incu bated


57 separation, 150 L of water was added and samples were incubated on ice for an additional 10 min. The organic layer was removed and the aqueous layer was re extract ed with 250 L of chloroform:methanol (2:1, v: v). The organic layers were combined, evaporated under nitrogen, and reconstituted in 100 L of isopropanol (for lipidomics). For substantia nigra tissue, the ground tissue was homogenized for 120 seconds, wit h 100 L of methanol and 200 L of chloroform for every 15 mg of tissue 5 L of internal standard (IS) mix (100 mg/L) was spiked into the chloroform:methanol (2:1, v: v) mixture before homogenization. Samples were incubated as for plasma, and water was add ed at a volume of one fourth of the Folch solvent. Substantia nigra was re extracted with 50 L of methanol and 100 L of chloroform for every 15 mg of tissue and dried down. Substantia nigra samples were reconstituted in 200 L of isopropanol. Data Acqui sition For both lipidomics analyses, a Dionex Ultimate 3000 RS UHLPC system (Thermo Scientific, San Jose, CA) was employed. Ionization was performed with heated electrospray ionization probe (HESI II) and mass spectra acquired using a Q Exactive Orbitrap ( Thermo Scientific). Source parameters for lipidomics, in positive and negative polarity are provide in Table S 3. Samples were maintained at 4C in the autosampler. 2 L of sample was injected onto a Waters Acquity BEH C18 column (50 mm x 2.1 mm, 1.7 m, W aters, Milford, MA) maintained at 30C. For negative ion mode, 5 L of sample was injected onto the column and analyzed with the same mass spectral parameters (Table S 2). A gradient ramp (Table S 1) was employed consisting of mobile phase C (60:40 acetoni trile:water, volume fraction) and mobile phase D (90:8:2 isopropanol:acetonitrile:water, volume fraction), both with 10 mmol/L ammonium formate and 0.1% formic acid.


58 Mass spectra were acquired in full scan mode using data dependent top 5 analysis (ddMS 2 top5) in both positive and negative polarity with a mass resolution of 70,000. Full scan and ddMS 2 top5 scan parameters are shown in Table S 2. Before each analysis, the instrument was externally calibrated and at least 3 blanks were analyzed. Internal ma ss calibrants (lock masses) were used in positive ion mode and consisted of diisooctyl phthalate ( m/z 391.2842) and polysiloxanes ( m/z 371.1012 and 445.1200). No stable lock mass was observed to be used in negative ion mode. To compare iterative exclusion (IE) with traditional ddMS 2 top5 for lipidomics, a minimum of 4 sequential injections were analyzed by ddMS 2 top5 with IE and 4 without IE, for both negative and positive polarity analysis of Red Cross and substantia nigra lipid extracts. For excluding ion s previously selected for fragmentation and placed on an exclusion list, a 10 ppm exclusion tolerance was used. Software P latform for IE [97] to directly process a .ms2 file ( converted using MSConvert [98] ) and output an exclusion list in a format (.csv) which can be directly imported by Q Exactive instruments. User defined parameters include the retention time and m/z window for combining selected precursors to reduce the size of the exclusion list. A 0.02 m/z window and 0.3 min retention time window was used in this experiment. In this case, ions selected at m/z values of 400.02, 400.01, and 400.01, and respective ret ention times of 5.10, 5.15, and 5.30 min, would be combined in one row as m/z 400.02 excluded between 4.95 and 5.45 min. In addition, users can denote the number of times ions with the same m/z are selected before being considered background ions and exclu ded for the entire duration of the chromatographic run. In this experiment, a minimum of 15 instances of ions


59 selected for fragmentation with the same m/z was used, excluding these ions from 0 to 18 minutes. The IE Omics script can be found in the suppleme ntary information and the most up to date version on the Southeast Center for Integrated Metabolomics (SECIM) webpage ( tools/). Feature Detection a nd Identification Lipids were identified using both an in house workflow, LipidMa tch, and LipidSearch (Thermo Scientific, San Jose, CA) [99] LipidMatch consists of R scripts which identify lipids by matching MS/MS fragments indicative of class and fatty acid constituents from experimental fragmentation to in silico fragmentation libraries (covering over 250,000 lipid species across 56 lipid types). Only exact mass of the MS/MS fragments (not intensity) is used for matching. Before LipidMatch was applied, features were determined using MZmi ne 2.0 [100] with th e batch mode file containing all the parameters. Both the MZmine batch file and LipidMatch software can be found at < tools/ > in the LipidMatch zip file. LipidMatch used the featur es exact mass determined by MZmine with a 10 ppm m/z window for precursor ion matching. Both for LipidMatch and LipidSearch, a 10 ppm m/z window was used for fragment matching. In LipidMatch, fragments were only considered confirmed if they were above 1000 intensity units and found in at least one scan within a 0.3 min window of the feature being identified; in LipidSearch, only lipids classified with grade A were kept. The 0.3 min retention time used for finding MS/MS scans and excluding precursors in sequ ential injections employing the IE Omics script was close to the median of the full width at half maximum (F WHM) for all features (Figure S2 1). After lipid ions were annotated, redundant annotations, for example, different lipid ion adducts


60 of the same mo lecular species, were combined separately for positive and negative analysis. Results and Discussion: Iterative Exclusion and Lipidome Coverage By only fragmenting ions not selected in the previous injections, applying IE increased the coverage of both an alyte and background ions for lipids ( Figure S 2 2 and Figure 2 3). As can be seen, for example for the background ion m/z 300.2253 in Figure 2 2, some ions selected in the first injection and placed on an exclusion list, were unexpectedly selected in the s econd injection. The reason for these ions not being excluded is that the mass trigger used to select ions for fragmentation are stored with a m/z value with two decimal digits in the Thermo .raw file. Therefore, the m/z 300.23 was placed on an exclusion l ist, and using a 10 ppm exclusion tolerance ions from 300.2270 to 300.2330 were excluded. In the second injection the ion was measured at 300.2253 and therefore was not excluded, and again was placed on an exclusion list at 300.23. This problem can be over come by either changes in Thermo .raw data storage, in order to store the mass trigger for obtaining MS/MS past the 3 rd decimal point (which has been implemented in the Q Exactive Plus and HF), or by the user increasing the exclusion tolerance, such as to 100 ppm. Since different ions with m/z values within 100 ppm will all be isolated and fragmented using a 1 Da isolation window, this solution would be sufficient. Either modification would ensure that ions isolated and fragmented once are never isolated and fragmented again, thereby decreasing the number of injections needed to fragment all ions of interest. It is a well k nown that background ions compete with analytes of interest for selection and fragmentation. Therefore, by discerning the background ion patterns and automatically placing those ions on an exclusion list, analyte coverage can be


61 increased. A background ion from the mobile phase, ESI source, or from column bleed (to name a few sources), can be discerned by a single m/z covering a large portion of the retention time region, as in 300.2253 discussed above. An exclusion list for background ions can often be gen erated by running several blank injections, but this can be an inefficient process; when the column or mobile phase changes, a new list would need to be created. From this IE type of run, it can be seen that a large portion of ions selected and fragmented are background ions as depicted by a horizontal pattern across the chromatographic run ( Figure 2 2). Therefore, this pattern can be readily used to exclude background ions in the IE Omics software. Using the default IE Omics parameters, if the same m/z i s selected more than 15 times, with each instance being at least 0.15 minutes apart, that m/z is annotated as background and placed on an exclusion list across the entire analysis time. For example, m/z 391.28 was excluded across the entire retention time in the second injection, after being selected in the first injection of Red Cross plasma 134 times ( Figure 2 2). Annotated background ions can also be excluded in future experiments. After the first injection, 17 ions were automatically annotated as backgr ound ions by IE Omics software in positive ion mode of Red Cross plasma. After 6 injections, 54 m/z values were annotated as background ions according to this algorithm. In addition to generation of exclusion lists of background ions, IE also enhanced cov erage of the lipidome. When comparing the second to first injection after applying IE, it can be seen that many additional unique precursors are selected in both the glycerophospholipid ( G PL) (about m/z 700 900 at 5 10 min) and triglyceride (TG) regions (a bout m/z 700 1100 at 11 16 min) ( Figure 2 2). Figure 2 3 compares unique


62 ions selected for fragmentation in positive mode analysis of Red Cross plasma lipid extracts in 6 sequential injections without IE applied ( Figure 2 3a) and with IE applied ( Figure 2 3 B ). The IE omics approach shows that after 6 sequential injections, the number of unique ions fragments is substantially higher ( Figure 2 3 B ). As discussed previously, both new background ion signatures and lipid ions (as can be seen in the G PL and TG reg ion) are selected using IE ( Figure 2 3). An analogous figure for substantia nigra is shown in the supplementary information ( Figure S 2 2), with a zoom in of the glycerophospholipid (GPL) region overlaid with unique molecular species annotated by LipidMatch ( Figure S 2 3). Figure 2 4 displays the cumulative number of features with lipid identifications across injection number using LipidMatch from both positive and negative mode analysis of plasma and substantia nigra lipid extracts. In all cases, a greater n umber of features were identified as lipids using the IE approach compared to a traditional ddMS 2 top5 approach. The application of IE was most advantageous in positive ion mode analysis of plasma and substantia nigra lipid extracts. For plasma extracts in positive mode, applying IE and using 6 sequential injections increased the coverage of features annotated with unique molecular species by 69 % compared to the traditional ddMS 2 topN approach across 6 sequential injections. A total of 728 unique lipid mol ecular species were identified with IE, compared to 431 without IE ( Figure 2 4 A ). In negative mode analysis of Red Cross plasma, only 10 % more identifications were obtained using IE. In positive mode analysis of substantia nigra, after the 5 sequential in jections, 40 % more features were identified using IE compared to without IE, while in negative mode analysis, 18 % more identifications were obtained when applying IE.


63 Applying a different identification software, LipidSearch, provided the same general tr end, with IE providing the most advantage in positive mode analysis of Red Cross plasma and substantia nigra ( 69 % and 34 % more identifications, respectively) and least advantage in negative mode analysis of Red Cross plasma and substantia nigra (18 % and 4 %, respectively) ( Figure S 2 4). Unique annotations of lipid molecular species with retention time information, exact m/z from full scan data, and average peak intensity compiled across all sequential injections for positive and negative polarity analysi s of Red Cross plasma and substantia nigra can be found in Table S 5 and S 4, for LipidSearch and LipidMatch, respectively. Fragments observed for identification by LipidSearch are also included in Table S 5, and fragmentation criteria for LipidMatch is in cluded in Table S 6. Based on these results, it is clear that the number of additional identifications obtained when applying IE depends on sample type and the polarity measured by the mass spectrometer. It is expected that if mass spectrum is sparse, a t raditional ddMS 2 approach will likely select the majority of ions above an MS 2 threshold limit. In the lipidomic analyses, where applying IE was less advantageous in negative ion mode, negative ion spectra showed fewer ions than positive ion spectra. For e xample, the number of features (which is related to spectral sparseness), was drastically lower in negative mode than positive mode, with only 4258 features in Red Cross negative mode data versus 19,231 features in Red Cross positive mode data. Therefore, after applying exclusion lists generated by IE, fewer precursors remain above the threshold to be selected for fragmentation in negative ion mode. For example, in negative polarity analysis of plasma, MS/MS scans drastically declined from the first to the fifth


64 sequential injection (from 2491 to 414 scans), showing depletion of precursors for selection, while in positive polarity there was less of a decline in MS/MS scans (from 2746 to 2581 scans) ( Figure S 2 5). Therefore, the number of sequential injection s required may vary depending on spectral density, and spectral density will be a major factor in determining the additional benefit of IE. For example, increasing the chromatographic gradient time would increase separation of lipids while decreasing spectral density at a given time point, and hence potentially decreasing the advantage of applying IE versus traditional ddMS 2 topN approaches. It should be noted that additional identifications using IE are only useful if they provide unique information. After excluding previously selected high abundance lipids for fragmentation, sequential injections should provide fragmentation of lower abundance species when applying IE. Often less abundant or trace species serve as critical biomarkers, such as phosphat idylinositol (PI), which is an important signaling molecule class. Phosphatidylcholine (PC) concentrations in plasma, for example, are about 20 fold higher than concentrations of phosphatidylinositol (PI), phosphatidylserine (PS), and phosphatidic acid (PA ) combined [101] After applying IE in the second injection, peak heights of identified lipids were significantly lower than the initial injection for positive and negative polarity analysis of both plasma and substantia nigra lipid extracts (p value < 0.05) ( Figure 2 5). For plasma in positive ion mode, the average intensity of s elected precursors seemed to continue to decrease using IE up to the fourth injection, although not significantly ( Figure S 2 6; p value > 0.05). Exclusion of trace ions close to the threshold intensity for fragmentation in certain chromatographic regions, while high intensity species, such as in the TG region where spectra are dense,


65 continue to be selected, would explain why the average intensity of ions does not continue to decrease after a certain number of sequential injections. This is supported by the fact that the number of precursors selected declines across sequential injections when applying IE, and therefore in certain regions ions are no longer being selected for fragmentation ( Figure S 2 5). For example, the TG region contained 4819 features abov e 5 x 10 4 in 4 minutes (11 to 15 minutes) in Red Cross plasma, while the lysophospholipid region contained 3859 features in 4 minutes (0.5 to 4.5 minutes). The reduced intensity of precursor ions selected after applying IE suggests lower MS/MS spectral qu ality. This is especially true for positive ion mode fragmentation of most glycerophospholipids, where fatty acyl indicative fragments are of low abundance. To determine the quality of MS/MS spectra across sequential injections, the percentage of lipids id entified with grade A, calculated by (A / (A + B + C)), was determined using LipidSearch. These grades are based on the number of fragments identified which contain species specific structural information, with lipid identifications graded A having the mos t structural information in MS/MS spectra (for example fragments indication both fatty acyls and the head group of glycerophospholipid species) Following a similar trend to the selected ion signal, the sequential injections after applying IE had a general drop in percent A, and hence decrease in MS/MS spectral quality ( Figure S 2 7). Injections in which IE was applied had significantly lower average percentages of A compared to sequential injections without IE, for negative and positive polarity analysis of substantia nigra tissue lipid extracts (p value < 0.05) and for positive polarity analysis of Red Cross plasma (p value < 0.005). No significant difference was observed for negative polarity


66 analysis of Red Cross plasma. Therefore, unique identifications provided by IE of low abundance species often provide less structural information and are more tentative. D iglyceride s (DG) are often present at low abundance and have been noted as important signaling molecules. In plasma lipid extracts, lower abundance D G species were identified after sequential injections applying IE ( Figure S 2 8). In the initial injection, all DG species identified except one had extracted mass chromatographic peak heights of 10 6 or 10 7 while after the sixth injection applying IE, all species identified had peak heights of 10 5 (Table 2 1). All DGs at the level of carbons and double bonds in Table 2 1 have been confirmed previously in human plasma [102] except for DG(30:3), identified as DG(12:0_18:3). In addition, all fatty a cids constituents contained in DGs have been confirmed in plasma using fatty acyl profiling [102] or have been found in DGs using derivatization [103] The lower abundance DG species identified after applying IE contained both odd chain (15:0, 17:1, and 17:2), and shorter chain (12:0, 14:0, and 14:1) species, which were not identifi ed without IE (Table 2 1). These fatty acids are in lower abundance in human plasma [102] and odd chain species could represent exogenous fatty acid species or those produced by gut microbiota [104] In this case, these species represent additional biological information otherwise not obtained. Coverage was improved for certa in lipid classes using the IE approach. The majority of unique lipid molecules identified by IE in positive analysis of Red Cross plasma, but not by the traditional data dependent approach, were mainly glycerolipids, specifically, TGs, oxidized TGs, ether linked TGs and DGs ( Figure 2 6 A and Figure 2 6 C ). These molecular species were of low intensity and were present in


67 chromatographic regions where mass spectra were dense. Hence, using traditional approaches, these lower intensity ions generally never make it on the list of the top 5 most intense ions to be included for fragmentation. In addition, there was minimal identification of ether linked TGs, oxidized lyso phosphatidylcholines (OxLPC), and acyl carnitines using the traditional ddMS 2 top5 approach. Applying IE significantly increased the coverage of these lipid classes. Ether linked TGs are a trace fraction of the total TGs in blood, for example, only comprising of 0.1 % of chylomicrons in human blood plasma, where they have been note d to concentrate [105] By applying IE, these low abundant ions (making up less than 0.1% of TG peak area signal) were selected for fragmentation and tentatively identified by exact mass of the precur sor and exact masses of the neutral losses of the two non ether fatty acyl constituents. In substantia nigra positive mode analysis ( Figure 2 6 B and Figure 2 6 D ), IE improved the coverage of phosphatidylserine (PS), oxidized phosphatidylcholine (OxPC), ph osphatidylglycerol (PG) and sulfatide species, which were minimally covered by the traditional approach. In negative ion mode analysis of substantia nigra tissue, using the traditional ddMS 2 top5 approach, there was no coverage of sulfatides and minimal co verage of phosphatidic acid (PA). Applying IE significantly increased coverage of both of these species ( Figure S 2 9). These findings highlight that IE not only increases the total number of lipid identifications, but increases identifications of trace lip id species of potential interest, which are minimally covered by traditional approaches. Future developments will continue to increase the advantages of applying IE Omics. Currently, the script is not integrated in Xcalibur software, and therefore


68 exclusi on lists are not generated in real time and must be uploaded into new method files before each iterative injection. In our lipidomics workflow, we suggest using 3 to 4 iterative injections on pooled samples, which can be used to identify features of a give n sample group. Therefore, this method is sufficient for lipid identification in large quantitative studies to determine biomarkers where thousands of samples are required, as only a few additional injections are used for IE, and hence there is minimal add ition of acquisition time. Fewer injections may be required if the exclusion window is increased from 10 ppm, for example, to 100 ppm. By increasing the exclusion window, isobaric ions will only be selected in one injection, reducing the number of injectio ns needed to select all ions above a certain threshold. In the future fully automated exclusion list generation may be developed. Conclusion: Iterative Exclusion Increases Lipidome Coverage We have semi automated the IE approach using a simple open source R script. The script uses open source formatted files which can be converted from various vendor formats and produces an exclusion list in a vendor neutral format required for importing into Thermo Scientific instruments (csv). Features include smart excl usion list generation, which combines ions selected in similar m/z and retention time windows to generate a shorter exclusion list, and automatic annotation of background ions. After applying the software, IE Omics, to lipidomic datasets in Red Cross plasm a and substantia nigra brain tissue lipid extracts, IE was shown to be most advantageous in complex matrices with a high number of analyte species. Applying IE to lipidomics analyses in certain cases increased identifications by over 50 %. The greatest adv antage using IE was shown in positive ion mode and in Red Cross plasma versus substantia nigra lipid extracts, where spectra were most dense. In lipidomics, trace


69 species, such as odd chained and short chained DGs, were identified only after applying the I E technique. Future data acquisition strategies, for example only including precursor ions for fragmentation which match lipid masses and identifying polymer patterns for exclusion, could prove advantageous. In most cases, however, such as in negative mod e, after only using a few sequential injections, all ions above the threshold limit for fragmentation were selected, and therefore new data acquisition methods would not provide additional advantage in terms of MS/MS spectral coverage. New data acquisition methods might be able to reduce the number of injections needed for coverage of the majority of lipid ions and notify the user when additional injections are no longer required. In conclusion, applying IE expands the scope of the lipidome covered, both in creasing the total number and diversity of lipids identified.


70 Table 2 1. Comparison of d iglyceride (DG) peak heights and fatty acid compositions Data are from Red Cross plasma acquired in positive polarity. A) T he first ddMS 2 top5 acqu isition using Lipi dMatch (IE 1 ) and B) the 6 th injection after applying an exclusion list using the algorithm described in this paper (IE 6) a) DG(C:DB) Peak Height DG fatty acid chains DG(32:1) 1.7 x 10 7 DG(16:0_18:1) DG(16:1_18:0) DG(32:2) 2.0 x 10 7 DG(16:0_18:2) DG(16:1_18:1) DG(36:1) 2.4 x 10 6 DG(16:0_20:1) DG(18:0_18:1) DG(36:2) 1.9 x 10 7 DG(16:0_20:2) DG(16:1_20:1) DG(18:0_18:2) DG(18:1_18:1) DG(38:5) 5.1 x 10 6 DG(16:0_22:5) DG(18:1_20:4) DG(18:2_20:3) DG(36:3) 3.5 x 10 7 DG(16:1_20:2) DG(18:0_18:3) DG(18:1_18:2) DG(36:4) 1.0 x 10 7 DG(16:1_20:3) DG(18:1_18:3) DG(18:2_18:2) DG(36:3) 4.7 x 10 4 DG(18:1_18:2) b) DG(C:DB) Peak Height DG fatty acid chains DG(30:0) 4.2 x 10 5 DG(12:0_18:0) DG(14:0_16:0) DG(15:0_15:0) DG(30:1) 3.5 x 10 5 DG(12:0_18:1) DG(14:0_16:1) DG(14:1_16:0) DG(30:3) 2.6 x 10 5 DG(12:0_18:3) DG(32:3) 2.2 x 10 5 DG(14:0_18:3) DG(14:1_18:2) DG(34:4) 3.6 x 10 5 DG(14:0_20:4) DG(16:0_18:4) DG(36:5) 3.3 x 10 5 DG(16:0_20:5)


71 Table 2 1. Continued b) DG(C:DB) Peak Height DG fatty acid chains DG(38:4) 3.9 x 10 5 DG(16:0_22:4) DG(18:1_20:3) DG(35:3) 3.0 x 10 5 DG(17:1_18:2) DG(17:2_18:1) DG(38:2) 3.4 x 10 5 DG(18:1_20:1) DG(38:4) 5.8 x 10 5 DG(18:1_20:3) DG(38:5) 6.9 x 10 5 DG(18:1_20:4) DG(18:2_20:3) DG(40:7) 9.1 x 10 5 DG(18:1_22:6) DG(40:7) 4.1 x 10 5 DG(18:2_22:5)


72 Figure 2 1. Strategy for iterative exclusion based data dependent topN analysis (IE ddMS 2 topN). Multiple injections of a sample are analyzed. A) The first injection is used to create an exclusion list and B) this exclusion list is applied to the second injection He nce the next top n most abundant ions are selected and this process can be continued for n injections.


73 Figure 2 2 Selected precursor ions retention time and m/z for Red Cross plasma compared between the first injection (black dots) and second injecti on (red dots) with iterative exclusion based (IE) ddMS 2 applied. The Y region is an unknown region with molecules separated by 14 Da corresponding to CH 2 repeating units, likely representing polymer species. Three background ions are indicated with arrows, which were selected at m/z 391.28, 354.29, and 303.23 from highest to lowest mass, respectively.


74 Figure 2 3 The use of iterative exclusion increased fragmentation coverage. Selected precursor ions m/z and retention times for : A) 6 repetitive injections using the traditional ddMS 2 approach and B) iterative based exclusion ddMS 2 (IE ddMS 2 ) for Red Cross plasma lipid extracts analyzed in positive mode.


75 Figure 2 4 Cumulative unique lipid molecular identifications using LipidMat ch software across multiple data acquisitions are shown. Iterative exclusion based data dependent top5 (IE ddMS 2 top5) described in this paper is compared with traditional ddMS 2 top5 for extracts of : A) Red Cross plasma in positive mode and B) negative mod e and C) extracts of substantia nigra in positive mode and D) negative mode.


76 Figure 2 5 Boxplots of log transformed peak heights (base 10) from MZmine for unique lipid molecules identified in the first ddMS 2 top5 acquisition using LipidMatch (IE1) and after applying an exclusion list using the algorithm described in this paper (IE2). All differences where highly significant with a p value for a Student t test less than 0.001. Comparisons are made for : A) extracts of Red Cross plasma in positive mode an d B) negative mode and C) extracts of substantia nigra in positive mode D) and negative mode


77 Figure 2 6 Distribution of lipids identified using LipidMatch by lipid class using iterative exclusion based data dependent top5 (IE ddMS 2 top5) acquisitions in positive ion mode. A) The lipid class distribution of all identifications across sequential injections using the traditional ddMS 2 top5 approach is shown for Red Cross plasma and B) substan tia nigra tissue lipid extracts; C) T he dist ribution of additional unique lipid molecular identifications after applying iterative exclusion (IE) across lipid classes are shown for Red Cross plasma and D) substa ntia nigra lipid extracts


78 CHAPTER 3 LIPIDMATCH: AN AUTOMATED WORKFLOW FOR RULE BASED LIPID IDENTIFICATION USING UNTARGETED HIGH RESOLUTION TANDEM MASS SPECTROMETRY DATA 2 The Challenges of Lipid Identification In comparison to proteomics, lipidomics is an emerging technique which currently lacks community wide agreement concerning the best software choice for the comprehensive and accurate identification of lipids based on chromatographic and tandem mass spectro metric data. A major challenge is the limited number of synthesized standards available, making it difficult to cover the much larger variety of lipid structures for MS/MS spectral matching. In the absence of authentic standards, this challenge has been pa rtially ameliorated by developing in silico libraries for acyl containing lipids. For example, in 2013, Kind et al. released LipidBlast [106] developing a computer generated MS/MS library of 119,200 lipids across 26 lipid classes, which include s predicted mass/intensity pairs. A second major chall enge is the accurate annotation of lipid identifications based on the fragmentation observed [107] The annotation depends on the structural resolution, which is the structural detail inferred by experimental data, specifically the MS/MS spectra. Structural resolution for lipids is dependent on specific structural charact eristics known, such as double bond location, geometric isomerism ( cis versus trans ), and the position, lengths and degrees of unsaturation of fatty acyl constituents. For example, if only the exact mass of the precursor and choline head group of a phospha tidylcholine species is observed, the species can only be annotated by total Published by: BMC Bioinformatics Published by: Biochimica et Biophysica Acta (BBA) Molecular and Cell Biology of Lipids


79 carbons and degrees of unsaturation (e.g. annotated as PC(32:1)) (assuming no overlap from fragmentation of other choline containing species, such as the 13 C isotopic peaks of SM) If the precursor mass and fatty acyl fragments are observed, then the lipid can be identified by acyl constituents (eg. PC(16:0_18:1)), with an underscore denoting that the position of the f atty acyl constituent on the backbone is unknown. For most lipid types, this is the limit of structural resolution that can be accurately annotated using UHPLC HRMS/MS without specialized or additional approaches. Currently, most lipidomics software over report structural resolution, which can lead to incorrect biologi cal interpretation of the data. [108] A third challenge for lipid identification is t he fact that features (peaks defined by a m ass to charge ratio ( m/z ) and retention time) often contain multiple co eluting molecules with similar m/z values. One common case is lipids sharing the same class, total carbons and degrees of unsaturation, but d ifferent acyl constituents, for example PC(18:0_18:1) and PC(16:0_20:1). This overlap reduces spectral similarity scores, which are used for identification by most software. To overcome these challenges, we have developed LipidMatch. LipidMatch currently contains the most comprehensive lipid fragmentation libraries of freely available software, when ranked by the number of lipid types. LipidMatch includes in silico libraries with over 250,000 lipid species across 56 lipid types, including oxidized lipids. LipidMatch incorporates user modifiable, rule based lipid identification, which allows for accurate lipid annotation in regards to structural resolution. In addition, if multiple identifications exist for one feature, LipidMatch outputs include all possibl e identifications ranked by summed fragment intensities.


80 Lipid Annotation Guidelines for Correctly Reporting Structural Resolution While t he lipidome the entire collection of individual lipid species in cells, tissues or biofluids has been estimated to be composed of 1,000 to more than 180,000 molecular lipid species [25, 26] t hese estimations do not consider isomeric lipid species with different fatty acyl double bond positions and configurations ( cis or trans ), positional isomers (e.g., sn 1, sn 2), and stereoisomers ( R or S ). Ekroos et al. determined that the number of phosphatidylcholine (PC) positional isomers in Madin Dar by canine kidney II cells nearly doubled the total number of individual lipid species [109] which highlights the substantial presence of lipid isomers in nature. Furthermore, these lipid isomers can also exhibit a variety of specific biological roles. For example, the acyl position of membrane lipids can impact the enzymatic activity that occurs withi n cellular membranes [110] Shinzawa Itoh et al. [79] found biological specificity of acyl chain double bond configurations; though the mitochondrial inner membrane where bovine cytochrome c oxidase (CcO ) acquires its phospholipids contains trans vaccenate, only cis vaccenate is incorporated into subunit III of CcO [79] Researchers have shown differin g roles of individual conjugated linoleic acid (CLA) isomers; while the cis 9, trans 11 isomer has been shown to more broadly inhibit tumorigenesis in vitro the trans 10, cis 12 isomer has been shown to increase concentrations of human blood lipids, such as triglycerides (TG) and the ratio of LDL to HDL cholesterol, when compared to the cis 9, trans 11 isomer [76 78] Structural elucidation is vital in ensuring that biological properties are properly associated with the correct lipid species. Therefore in this section we provide guidelines for a nnotating lipids and discuss the limitations in bio logical interpretation of lipid


81 species. Software such as LipidMatch, which applies these guidelines is essential to implementation and harmonization by the wider lipidomics community. Community accepted guidelines for lipid annotations [111 113] generated/accepted by the International Lipids Classification and Nomenclature Committee have been implemented and promoted by the LIPID Metabolites And Pathways Strategy (LIPID MAPS) consortium [23, 81, 114] and are meant to completely characterize the lipid molecule as shown in Figure 3 1. However, conventional tandem mass spectrometric experiments cannot be used to generate all structural information of a given lipid molecule. Therefore, shorthand notation has been proposed to only confer the level of structural detail known based on experimental data [107] We will define this structural detail for a given lipid species, as the structural resolution Moreover, we summarize existing guidelines supplemented by new recommendations to prevent over reporting of lipid structural resolution and to further encourage the use of a common nomenclature system for lipidomics. Do not annotate lipids using only exact mass Often researchers entering the lipidomics field will annotate peaks and features based on exact mass only, for example as in Gerspach et al. [115] A feature is a peak, or group of peaks across numerous samples, represented by a specific m/z and any other measurements, such as a specific retention time if chromatography is used, or drift time if ion mobility is used [116] Since the lipidome is diverse, with enormous overlap in exact mass, we strongly warn against annotating fea tures using only exact mass, especially for previously uncharacterized sample types. It is important to note that exact mass search engines,


82 such as Metlin and LIPID MAPS, provide lipid matches annotated as fully characterized molecular species, which cann ot be elucidated from exact mass alone. Annotate by sum composition when class specific fragmentation is observed The most basic annotation of lipids is by lipid class and the sum composition of carbons and double bonds in the lipid f atty acyl constituents (Table 3 1). The sum composition annotation is useful in cases where the majority of fragmentation intensity is in class specific fragments. Examples include phosphatidylethanolamine (PE) [M+H] + (neutral loss (NL) of m/z 141.0191) [117] phosphatidylinositol (PI) [M + NH 4 ] + (NL of m/z 277.0562) [95] and sulfatide [M H] ( m/z 96.9601) [118] with these base peaks in the fragmentation specific to the lipid head group. Annotation by lipid class can often lead to false positives if fragment s are not specific to only that lipid class. A common case is the incorrect annotation of protonated adducts of sphingomyelin (SM) and phosphatidylcholine (PC) and their lysolipid, oxidized lipid, and ether linked lipid corollaries using m/z 184.0733, for example as in Jin et al. [119] Isobaric isotopic peaks of co eluting SM and PC species will be co isolated for fragmentation and hence the lipid class represented by m/ z 184.0733 is ambiguous. In this case, identifications should be noted as tentative unless reconstructed ion chromatograms of the PC and SM species within 3 daltons do not overlap or fatty acyl constituents are observed. Denote f atty acyl constituents only when fatty acyl fragment(s) are observe d Lipids can often be annotated based on fatty acyl constituents (Table 3 1). Technically, in lipids with two fatty acyl constituents, only one f atty acyl constituent is needed for identification, as the other can b e deduced using the exact mass of the precursor. This can be a helpful strategy when sn1 and sn 2 linked f atty acyl


83 constituents have different fragment efficiencies, as in PC [M+H] + adducts [120] Without assumptions based on biology or specific approaches, identification by f atty acyl constituent constitue nts is often the limit of structural resolution that can be obtained. Use the underscore to annotate lipid species with unknown positional isomers Traditional UHPLC HRMS platforms with tandem MS do not provide information about the double bond position o r orientation, stereochemistry, and in many instances, the position of the f atty acyl constituent on the glycerol backbone ( sn 1 or sn 2). L ipid identifications where the positional isomeric level of the f atty acyl constituents is known is indicated by a sla sh "/". The underscore "_" was proposed by Liebisch et al. (2013) [121] for instances where there is certainty in th e composition of the fatty acyl constituents, but not their placement on the glycerol backbone. Despite the proposed shorthand notation, there has been a mix of annotation styles present in literature. For example, of the lipidomics research articles publi shed in 2017 determined on Science Direct (accessed 02/09/2017), 3 articles [119, 122, 123] incorrectly used "/", 3 articles [124 126] used ", and 1 article [127] used "_" between fatty acyl constituents, when positional isomers were not identified. One potential source of confusion in annotating lipids is that current lipid identification software, for example Li pidSearch, MS DIAL [128] LipidBlast [106] and Greazy [129] all employ "/" when fatty acyl posi tions are not known. However, to further advance the lipidomics community, all lipid identification software should improve lipid annotation by incorporating the slash "/" or "_" correctly based on the MS/MS data. Otherwise, an incorrect level of structura l detail is assigned to the lipid annotation, providing the user with a level of certainty,


84 which is misleading for biomarker discovery, disease etiology studies, and translational science with other omics areas. Report plasmanyl species using O and plas menyl species using P Some of the most problematic cases for lipid annotation include plasmenyl and plasmanyl ether linked species, which are depicted in Figure 3 2. One problem is the use of varying annotation style. For plasmanyl lipid species, lipids are often annotated using an "e" or an "O ", while plasmenyl lipids are often annotated using a lowercase "p" or a capital "P ". We suggest using "O and "P ", the annotation style used by LIPID MAPS [23, 81, 121] Another problem arises because the vinyl ether linkage in plasmenyl species and the ether linkage in plasmanyl species only differ by a degree of unsaturation, lea ding to differing structures with the same molecular formula. For example, plasmanyl PE(O 16:0/22:6) will have the exact same mass as plasmenyl PE(P 16:0/22:5) and cannot be distinguished based on class specific fragments. In this case we suggest including both annotation by sum composition, for example PE(P 38:5) and PE(O 38:6). In the case of ether linked PC, the formate adduct will yield an abundant sn 2 fatty acyl fragment when fragmented in negative ion mode; the ether linked PE species can also be dist inguished using fragmentation [130] Hence, the vinyl ether and ether linked lipids can be distinguished using fragmentatio n, although co elution of plasmenyl and plasmanyl species often occurs, in which case both species should be reported. Report all possible lipid candidates for a feature separated by a pipe "|", not just the top few lipid candidates TGs are the most common case where co elution of isomeric species occurs. For example, our laboratory tentatively identified


85 2,607 TG ions ([M+Na ] + and [M+NH 4 ] + ) in human plasma across 370 features, meaning that, on average, each feature had 7 co eluting TGs identified (unpublished data). For one feature at m/z 920.8635 in human plasma, 49 TGs were tentatively identified. TGs are just one example of co eluting molecules, for the same human plasma analysis in positive ion mode we found that 40% of features with lipid annotations have at least two co eluting lipids identified. It is important to note that most software only include one lipid identification for a given feature in the final report, which is based on the false assumption that there are few insta nces of co eluting lipids. Examples of annotated lipids using pipes can be found in Supplementary Table S 4 of Koelmel et al. [131] for example for m/z 766.5391 at retention time 7.06, the feature was annotated as PE(18:0_20:5)+H | PE(18:1_20: 4)+H | PE(16:0_22:5)+H, with annotations ranked by a score based on the MS/MS spectra. Use comprehensive MS/MS libraries whenever possible Even when annotations include all lipids identified for a respective feature, co eluting lipids not contained in t hat software's libraries may still exist. In this case, biological interpretation will be confounded by multiple uncharacterized lipids or other molecules contributing to ith non oxidized species, but are not contained in most lipidomics software. Use pre analytical steps to prevent degradation and interconversion Pre analytical steps can also influence the correct annotation of features by affecting the stability and int ensity of lipids or leading to interconversion of the lipid species observed. Sample handling and preparation techniques, involving homogenization, freeze thawing, and/or exposure to air or light, can result in lipid oxidation or (non)enzymatic


86 degradation or interconversion [132, 133] For example, our studies have shown that by not quenching enzymatic activity during sample preparation leads to increased lysophosphatidylcholines (LPCs) (+19.31.8%) and decreased phosphatidylcholine ta), likely caused by phospholipase A activity. In this case, stabilization techniques (e.g., heat treatment, additives such as antioxidants, and freeze drying) can be employed, and common byproducts of degradation and interconversion can be measured. For certain lyso lipids, such as LPCs, acyl migration during sample preparation exists between the sn 1 and sn 2 isomer, complicating annotation and quantification [134] Therefore, sn 1 and sn 2 isomers of lyso species should be combined and reported as sum composition. For MS/MS based identification, LipidMatch is the only lipid identification software to date which employs all the annotation guidelines presented here, including using pipes "|" for multiple identifications "_" when fatty acyl position on the glycerol backbone is unknown, and annotates lipids by total carbons and degrees of unsaturation whe n only class specific fragments are observed. As annotation of lipid species becomes more accurate, we will continue to advance our understanding of the precise roles of individual lipids species in biological systems, advancing the utility of lipidomics. Implementation of LipidMatch Software LipidMatch was written in R [135] The user interface for LipidMatch consists of a series of dialogue boxes developed using gWidgets API and the tcltk R package. Users can access LipidMatch as a file in the supplementary material, with the latest version available at . A manual and video tutorials are provided to walk users through the entire lipidomics workflow, including vendor file


87 conversion to open source format, feature processing, LipidMatch identification, in silico lipid library develop ment, and the ability to append identifications from other software (e.g. MS DIAL or Greazy). Generation and Validation of Lipid M atch in s ilico Libraries In silico libraries were developed in Excel as described in video tutorial 6 in the supplemental info rmation. Briefly, an R script was used to generate a list of possible fatty acid combinations for acyl containing lipids with 2 or 3 fatty acids. A list of 39 possible endogenous fatty acids and 214 potential oxidized fatty acids were incorporated (contain ed in the LipidMatch zip file). Combinations excluded redundant possibilities such as 18:0_20:0 and 20:0_18:0. For oxidized lipids, a list of 126 potential long chain oxidized fatty acids was generated by the addition of one or more (depending on the degre es of unsaturation) O (as a ketone or epoxy), OH (as a hydroxyl radical), and OOH (as a perhydroxyl radical) to unsaturated fatty acids within the list of 39 endogenous fatty acids. A list of 88 potential short chain oxidized fatty acids were generated by cleavage of unsaturated fatty acids contained in LIPID MAPS and addition of a terminal CHO (aldehyde) or COOH (carboxylic acid). Oxidized f atty acyl constituents were combined with the original list of f atty acyl constituents to generate possible fatty acy l combinations for oxidized lipids. For each lipid class, structurally indicative fragments were compiled using other MS/MS databases (LIPID MAPS [136] LipidBlast [137] and MS DIAL [128] ), literature, and/or experimentally derived fragmentation. A summary of all lipid libraries contained as of 10/01/2016 is presented in Table S3 1. Note that this list is constantly growing, and a complete list can be found in the LIPID_ID_CRITERIA.csv file found in the most up to date LipidMatch zip file at < tools/ > Using multiple


88 sources to obtain fragmentation allowed for cross validation of fragments and generation of lipid class specific fragmentation rules (see video tutorial 6 of the supplementary information for details). Fragment masses calculated were validated with MS/MS o f internal standards obtained using HCD fragmentation [131] on a high resolution orbitrap mass spectrometer, or literature searches. The following internal standards were used for verification (acronyms are defined in Table S3 2 ): CE(17:0), CE(1 9:0), CE(2:0), Cer(d18:1/17:0), Cer(d18:1/25:0), MAG(17:0), DG (14:0/14:0), DG (19:2/19:2), DG (20:0/20:0), GlcCer(d18:1/12:0), LPA(17:0), LPC(17:0), LPC(19:0), LPE(14:0), MG(17:0), OxPC(16:0/9:0(CHO)), PA(14:0/14:0), PC(14:1/14:1), PC(17:0/17:0), PC(19:0/19: 0), PE(15:0/15:0), PE(17:0/17:0), PG(14:0/14:0), PG(15:0/15:0), PG(17:0/17:0), PI(8:0/8:0), PS(14:0/14:0), PS(17:0/17:0), SM(d18:1/17:0), SM(d18:1/6:0), TG (13:0/13:0/13:0), TG (15:0/15:0/15:0), TG (17:0/17:0/17:0), TG (17:1/17:1/17:1) and TG (19:0/19:0/19:0). All internal standards were obtained from Avanti Polar Lipids (Alabaster, Alabama), except TG species, which were purchased from Sigma Aldrich (St. Louis, MO), and cholesterol esters, which were obtained from Nu Chek Prep (Elysian, MN). Lipidomics Workflo w with LipidMatch LipidMatch is designed to be integrated with other open source software to streamline the lipidomics workflow as described in Figure 3 3 LipidMatch was developed and tested using data acquired from a Q Exactive orbitrap mass spectrometer ( Thermo Scientific, San Jose, CA). LipidMatch has also been tested using data acquired on an Agilent 6540 Q TOF (Agilent Technologies Santa Clara, CA). LipidMatch can be used with a variety of other vendors and data formats. Ion selection techniques use d to acquire fragmentation, including all ion fragmentation (AIF),


89 inclusion list based targeted approaches, and data dependent topN (ddMS 2 topN) approaches can be used with LipidMatch to annotate lipids acquired using liquid chromatography, direct injecti on, or imaging approaches. LipidMatch is not recommended for most applications using low resolution mass spectrometers. For brevity, we will focus on UHPLC MS/MS methods using the data dependent topN approach, although video tutorials for imaging approache s and AIF approaches are included in the supplemental materials. In the workflow recommended for LipidMatch, users acquire full scan data for all the samples in negative and/or positive polarity. In addition, users acquire ddMS 2 topN spectra from pooled sa mples or from other representative samples. Using iterative exclusion (IE) [131] on pooled or representative samples can increase the number of ions with respective fragmentation spectra. This is highly recommended if spectra are dense (many overlapping lipid signals). Following data acquisition, full scan data (either centroid or profil e) can be processed to determine features, defined as an ion or ions sharing the same m/z and retention time. Features can be determined from various peak picking software such as MZmine [100] XCMS [138] or MS DIAL [128] The feature table can have nearly any format, allowing flexibility in choosing feature processing workflows. Video tutorial 2 explains how users can process data using MSConvert [98] and MZmine 2.20 using a batch file for MZmine. The batch file was optimized for lipids using the chromatographic methods in Table S 2 1 and is included with the tuto rial videos in the LipidMatch file. Once feature tables are created for each biological substrate and each polarity, features can be directly annotated using LipidMatch and the previously generated


90 MS/MS data. Peak picking of MS/MS data and conversion to ms2 file format should be done using MSConvert [98] Feature table (s) and MS/MS data are placed into a directory as shown in Figure 3 4 Often researchers may have multiple feature tables one for each polarity type and feature tables for each substrate analyzed. Users can include a subfolder for each sample type, and feature tables mode. Each folder should contain respective MS/MS data for that substrate in .ms2 a ion fragmentation data. For example, the user could create a folder for a lipidomics experiment on cancer, with two sub folders, one for plasma from cancer and non cancer patients and one for healthy tissue and tumor tissue. Each sub folder could contain, for example, 2 DDA .ms2 files in positive mode and 2 DDA files in negative mode, one pooled for participants with cancer and one pooled for non cancer participants, as well as the correspondin g feature table in negative and positive polarity. Once the user runs LipidMatch and enters user parameters, LipidMatch will automatically append identifications to each feature table using MS/MS files contained in that feature table subfolder. Once lip id identifications are obtained using LipidMatch, identifications from any other software such as Greazy [129] LipidSearch ( Thermo Scientific, San Jose, CA) and MS DIAL can be appended in additional columns to the feature table ( Figure 3 3 ). The annotations are appended from one file to another if the retention time and m/z of a feature in one table matches the retention time and m/z of a feature from a second table


91 within a user defined mass tolerance and retention time tolerance. For example, if a retention time tolerance of 0.1 minutes and ma ss tolerance of 10 ppm is used, a feature annotated PE(36:2)+H with a retention time of 6.72 and m/z of 744.5536 will be appended to a feature generated by a different software with a retention time of 6.68 and m/z of 744.5540. Lipidome coverage and confidence in identifications can be increased by appending identifications from multiple software onto one feature table In addition, metabolite, xenobiotic, or other identifications from software such as Compound Disc overer ( Thermo Scientific, San Jose, CA) or MS DIAL can be appended for a more global approach. Furthermore, lipidome coverage can be increased by the user community by adding new in silico fragmentation libraries. Libraries for LipidMatch can be developed using LipidBlast Templates [137] or as explained in video tutorial 6 found in the supplementary information. Each library should be developed with the correct annotation based on the structural re solution that can be inferred by fragments chosen for the identification criteria. LipidMatch Inputs and Operations LipidMatch user inputs and respective operations are exemplified in Figure 3 5 using experimental data for PC(38:6) [M+HCO 2 ] A similar sc hematic to Figure 3 5 which includes user inputs and modifiable parameters, is provided in the supplementary information ( Figure S3 1). The user first chooses directories containing feature table (s), for example those generated by MZmine ( Figure 3 4 ). The n, LipidMatch performs exact mass matching at the MS1 level between in silico precursor ions and each features m/z using a user defined m/z tolerance (Da) (Step 1; Figure 3 5 ). Precursor ions include all adducts contained in the in silico libraries for the respective polarity, but do not include dimers, multimers or in source fragments. Each feature and lipid match will be termed a


92 and m/z tolerance of each feature is det ermined (Step 2; Figure 3 5 ). The m/z tolerance is the same as the isolation window used for selecting ions. For each MS/MS scan of each feature, experimental fragments are matched against in silico lipid fragments m/z using a tolerance window (ppm). The t otal number of scans across a feature containing that fragment is calculated. In addition, the fragments average m/z maximum intensity, and retention time at maximum intensity across all scans are calculated for a feature (Step 3 ; Figure 3 5 ). This inform ation on fragments for each feature lipid pair is saved as a table in .csv format for each lipid class. Each fragment is assigned 1 if it is above the user defined minimum intensity and scans threshold and 0 if the fragment does not meet these criteria or was not found within the m/z tolerance (Step 4 ; Figure 3 5 ). The default number of scans required is 1 based on orbitrap mass spectrometers, but can be increased for other applications. The user modifiable intensity threshold for fragment ions to be considered real is dependent on the mass analyzer, the type of detector and the noise level. In Step 4 fragments assigned a 1 are considered observed based on the threshold criteria discussed above. Lipids are identified if they contain the necessary obse rved fragments. For example, for PCs measured as formate adducts, both negative ions of the fatty acyl constituents must be observed (Step 5 of Figure 3 5 ), while for protonated PCs the PC head group ion 184.0733 must be observed, along with at least one f atty acyl indicative fragment if the lipid is to be characterized at the level of fatty acyl constituents. Default fragments which must be observed for each lipid class were determined using high collisional induced dissociation (HCD) on a Q Exactive orbit rap


9 3 mass spectrometer of internal standards, or endogenous lipids verified in literature. Users can modify which fragment ions for each lipid class must be observed for identification using a simple Excel sheet as outlined in the 6 th video tutorial. I n cer tain cases it may be important to optimize fragment criteria for applications not employing HCD fragmentation with an orbitrap analyzer. Experimental protocols including mobile phase (adducts observed), low and high mass cutoff, resolution, and type of fra gmentation (e.g. HCD, CID, or UV) will determine what fragment ions are necessary for each lipid type to be identified. Therefore, for applications other than those using HCD fragmentation and orbitrap detection, we strongly recommend checking the existing fragmentation rules against MS/MS obtained in house. Fragments chosen for confirmation should be of relative high intensity and distinguish the lipid structure from other lipids with similar fragmentation. It is important to note that while fragmentation measured on other high resolution instruments, such as qTOF platforms, can result in significant changes in the relative fragment intensities, in most cases the fragment masses observed are the same. Therefore, since LipidMatch does not include intensity i n in silico fragmentation libraries and does not include relative intensities in identification, criteria for identification will often be similar between instruments. After lipids are identified, they are assigned a number based on whether they are ident ified by class and fatty acyl constituents (1), by data independent analysis (2), only by class (3), or only by precursor m/z without fragment matching (4) (Step 6 Figure 3 5 ). If multiple lipids are identified for a single feature, the lipids are ranked by the summed intensity of all their fragments with in silico fragment exact mass matches, including those not used for confirmation (Step 7 Figure 3 5 ). The final ranked lipid


94 identifications are appended onto the feature table along with the lipid clas s and adduct of the top ranked lipid and summed fragment intensities for each identification. Benchmarking LipidMatch against other Open Source Software Comparison of Lipid Software F eatures Table 3 2 compares features in LipidMatch, MS DIAL, Greazy, and LipidSearch which can all be used to analyze UHPLC HRMS/MS data. LipidMatch, MS DIAL, and Greazy are open source, while a license must be purchased for LipidSearch. Currently, MS DIAL and LipidSearch provide the most user friendly interfaces and ease of u se. In contrast to other UHPLC HRMS/MS identification software, LipidMatch is completely written in R. Compared to the other lipid identification software written in middle level languages, such as C++, LipidMatch can take longer to run, especially for hig h resolution data. This is due to the slow speed of imbedded for loops in R and the extensive LipidMatch libraries and hence large search space. While run time can be longer, LipidMatch can readily be integrated with diverse R tools and statistical package s available for mass spectrometry and omics based studies Databases for lipid identification differ both in coverage and information type. For example, LipidMatch and Greazy databases contain only the exact m/z of precursor ions and fragment ions, while MS DIAL and LipidSearch include simulated intensities. In addition, software such as MS DIAL and LipidMatch contain static in silico libraries, while libraries in Greazy are generated as the program is executed, based on the types of lipids and f atty acyl co nstituents the user specifies. While LipidMatch libraries are static excel files, as with all four software previously mentioned, the user can select which lipid types to query using LipidMatch, hence limiting searches only to biologically relevant or expe cted lipid types and reducing run time. LipidMatch libraries contain only


95 exact m/z values of precursors and fragment ions, making it relatively trivial for users to generate in silico libraries and/or convert other databases to the LipidMatch library form at. LipidMatch contains all lipid types in MS DIAL 2.24, as well as LipidBlast release 3 development libraries. With 56 lipid types, the LipidMatch in silico libraries cover the greatest number of lipid types of any open source software to date, with MS DI AL containing 34 lipid types, and Greazy containing 24 lipid types ( Table 3 2 ). All four programs use different identification strategies. MS DIAL and LipidSearch include intensity to rank lipid identification by a similarity score. Greazy includes a similarity score as well as a false discovery probability based on the total number o f fragments observed, thus solely relying on m/z Both LipidMatch and LipidSearch include rule based identification, which allows correct annotation of lipid structure based on fragments observed (correct structural resolution). While all other open source software sort identifications by similarity score, LipidMatch sorts lipid identifications by summed fragment intensity. For each lipid species identified, all expected fragment ions are summed (using the scan with the highest intensity for each fragment). Fragment ions to sum are determined from the in silico fragment m/z values for that species and include fragments not necessary for lipid identification (for example the loss of the PC head group for PCs when the 184.0733 m/z PC fragment is observed). For each feature, the lipid ions are ranked from maximum to minimum summed intensity. LipidMatch ranking is based on the assumption that a feature often represents multiple lipid ions and that ranking is meant to determine the relative signal contribution of each lipid to the feature. In other software, by using similarity score, ranking is based on which lipid identification is most confident. While both ranking algorithms produce


96 similar results in many cases (see results and discussion), LipidMatch algorit hm is designed based on a more accurate assumption of multiple co eluting lipids sharing m/z values within the same accurate mass. In simple dot product matching, the algorithm is based on the assumption that the fragmentation spectra are solely based on the ion of interest. Any deviation from the predicted fragmentation spectra, such as additional high intensity fragment peaks from co eluting isobaric species, will reduce the dot product score. Many lipids will not be identified due to co eluting isobaric species adding more fragments to the spectra and hence reducing the dot product score. MS DIAL has approached this issue by reducing the impact of peaks not contained in the in silico fragmentation library on the modified dot product scor e. Fragments from different species which overlap in exact mass, for example fatty acyl fragments from 18:0 in TG(18:0/18:0/18:0) and TG(16:0/18:0/20:0), will still decrease the modified dot product score in MS DIAL, and hence lead to false negatives. R anking lipid identifications for a given feature is complicated by overlapping mass spectral fragments in LipidMatch as well. A number of problematic cases can arise. For example, for a given lipid type with high intensity fragments below the m/z cutoff, t he ions summed fragment intensity will be reduced compared to lipid species with the bulk intensity of fragments within the m/z range. Similarly, if high intensity fragments are missing from the in silico library for a lipid type, these lipids will be arti ficially lowered in their ranking in terms of contribution to feature signal. In addition, shared fragment ions for some lipids will artificially inflate summed fragment intensity ( Figure 3 6 B ) and fragment intensity will depend on the MS/MS scans proximit y to a


97 given ions apex ( Figure 3 6 C ). Similarity score matching, such as that used by MS DIAL, suffers similar problems. To determine the accuracy of lipid rankings and identifications using LipidMatch, identification of lipids in Red Cross plasma using L ipidMatch was compared toMS DIAL, and Greazy. Lipid software excluded for comparison included LipidSearch (Thermo Scientific), Lipidyzer (SCIEX), and SimLipid (PREMIER Biosoft), which are not open source software, and Alex [139] LipidXplorer [140] MS LAMP [141] LIMSA [142] LOBSTAHS [143] Lipid Data Analyzer [144] LipidQA [14 5] and Lipid Pro [146] which were not designed for UHPLC HRMS/MS untargeted experiments. As stated previously, LipidMatch, MS DIAL, and Greazy differ in lipid identification strategy; hence, the amount of features with the same identifications between LipidMatch and the other software platforms was used to assess the accuracy of the LipidMatch ranking algorithm. Further work, with spiked co eluting standards sharing the same exact mass at varying concen trations would be helpful to further assess the ranking algorithm accuracy. A C ase S tudy: I dentification of L ipids in Red Cross P lasma LipidMatch, Greazy, and MS DIAL were applied to five replicate injections of Red Cross blood plasma. Data was acquired i n positive and negative polarity, using iterative exclusion [131] and data dependent top 5 (ddMS 2 top5) to acquire MS/MS fragmentation. Liquid chromatography and mass spectrometer parameters are shown in supplementary Table S 2 1, Table S2 2, and Table S 2 3 Identifications from all software were appended to the MZmine feature tables using the CombineSoftwareIDs.R script. Both the script, MZmine parameters (batch file), and an excel sheet with the resulting annotations of features across all 3 software ( Table S3 3 ) are included in the


98 supplementary information. The script aligns features with similar m/z (10 ppm window used) and retention times (0.2 min window used) from two different peak picking or identification software. Compared to the oth er major open source software platforms, such as MS DIAL and Gr eazy, LipidMatch annotated more lipid ions. LipidMatch was used to identify 210 lipid ions across 159 features and 15 lipid types in negative polarity. In positive ion mode, LipidMatch was used to annotate 5159 unique lipid ions across 1401 features and 26 lipid types. The large number of unique lipid ions in comparison to a smaller amount of identified features is due to overlap of co eluting lipids sharing the same exact mass, allowing for mul tiple lipids identified for a given feature. It is important to note that annotations of class specific fragments (as indicated by "3_" in Table S3 3 ), are significantly more tentative than identifications using fatty acyl fragments. This is especially tru e for choline containing lipid classes such as SM and PC, which share common fragments. For positive ion mode, 987 features were annotated with fatty acyl information. It is also important to note that in this study, we look at the number of lipid ions ann otated, including multiple adducts for a given lipid species. When only unique lipid molecules were taken into account by manually removing redundant adducts and features, and identifications using only choline specific fragmentation were removed, a total of 728 features with unique lipid molecular annotations were identified by LipidMatch for this dataset, as has been published previously [131] The curated 728 lipid molecular identifications using LipidMatch is still significantly greater than the total lipid ions identified by MS DIAL and Greazy combined. Table S3 3 includes all features identified in Red Cross plasma, with LipidMatch, MS DIAL, and Greazy annotations.


99 MS DIAL and Greazy identified 143 and 94 features in negative mode, respectiv ely, and 411 and 180 features in positive mode, respectively. Lipid types identified, which were unique to LipidMatch, included oxidized species (151 across TG, PC, and LPC in positive polarity), plasmenyl and plasmanyl TGs (19 species in positive mode), s phingosines (2), sulfatides (1), and PI species in positive mode as ammonium adducts (18). It is important to note that many additional unique in silico libraries exist in LipidMatch, for example cardiolipin as ammonium adducts, but these species are not o bserved in plasma samples. Bar graphs displaying the number of lipid species in each lipid type identified by LipidMatch, MS DIAL, and Greazy, and overlapping identifications between software are shown in supplementary Figure S3 2 (negative polarity) and F igure S3 3 (positive polarity). In addition, pie charts showing the lipid types covered by LipidMatch are shown in Figure S3 4 (negative polarity) and Figure S3 5 (positive polarity). Since Greazy is limited to glycerophospholipid species, only 65 feature s in negative polarity and 68 features in positive polarity had identifications across all software. In negative polarity, 97% of these features had the same identification at the structural resolution of fatty acyl constituents across all 3 software platf orms. In positive polarity, 71% of features with identifications across all software tested were the same. Note that plasmenyl and plasmanyl species with differences in one degree of unsaturation were considered the same identification due to minimal diffe rence in MS/MS spectra. The greater discrepancy in identifications in positive mode is most likely to do to the low abundance of acyl chain fragments for glycerophospholipids in positive mode, thus making identification by fatty acyl constituents difficult At the

PAGE 100

100 structural resolution of lipid class and total carbons and double bonds, 94% of features contained the same identifications across all 3 software platforms in positive polarity, and 100% of features were identified the same in negative polarity. Of all lipid types identified by both MS DIAL and LipidMatch, TGs had the most discrepancy. Of the 136 features identified as TGs by both LipidMatch and MS DIAL (both sodiated and ammoniated forms), 100% of the top hits were the same at the structural reso lution of total carbons and degrees of unsaturation, but only 61% of the top hits were the same at the structural resolution of fatty acyl constituents. TG identification is complicated by the number of co eluting isomers, for example, LipidMatch identifie d over 20 co eluting TG isomers for a number of features. These co eluting isomers can share one or more fatty acyl constituents, and therefore share common fragments, further complicating identification. LipidMatch had a significant number of lipid ident ifications by fatty acyl constituents corroborated by at least one other software, suggesting that LipidMatch identification and the ranking strategy results in similar identifications for glycerophospholipid species compared to other identification algori thms. For the 68 features identified by all software in positive polarity, 92% of identifications by LipidMatch were corroborated by at least one other software. MS DIAL and Greazy had 86% and 84% of identifications corroborated for these features by at le ast one other software, respectively. In negative polarity, 98% of LipidMatch identifications (all except one) were corroborated by at least one other software, with MS DIAL having 98% identifications corroborated and Greazy having 100% of identifications corroborated.

PAGE 101

101 Conclusion : LipidMatch is a Flexible, Comprehensive, and Accurate Annotation Software LipidMatch is a freely available tool with the potential to be incorporated into a diverse range of lipidomics workflows, including imaging, direct in fusion, and LC MS/MS experiments with both low and high mass resolution. For LC MS/MS workflows, LipidMatch can be used with any feature processing software, such as MZMine, XCMS, or MS DIAL. LipidMatch contains the greatest diversity in lipid types of any current open source software platform and a unique rule based strategy for identification and summed fragment intensity based strategy for ranking top hits. Compared to other software, LipidMatch is highly customizable. For example, users can select which fragments are necessary for confirmation and develop their own fragmentation libraries in Excel. Additional tools allow the user to pool results from multiple identification software platforms into one feature table Compared to MS DIAL and Greazy, LipidM atch was found to provide the most lipid identifications for Red Cross plasma. For features with identifications using all 3 software platforms, identifications were comparable at the level of fatty acid constituents. 92% and 98% of LipidMatch identificati ons were corroborated by at least one of the other software platforms in positive and negative mode, respectively.

PAGE 102

102 Table 3 1: Structural r esolution and a nnotation of l ipids using m ass s pectrometry Structural Resolution Annotation Carbons and Double Bonds PC(34:2) Fatty Acyl Constituents PC(16:0_18:2) Positional Isomers PC(16:0/18:2) Double Bond Position PC(16:0/18:2(10,12)) Double Bond Cis/Trans PC(16:0/18:2(10E,12Z)) Stereochemistry PC(16:0/18:2(10E,12Z)[R])

PAGE 103

103 Table 3 2 Comparison of lipid identification software. LipidMatch MS DIAL GREAZY LipidSearch 4.1 Identification (ID) Strategy* Rules Similarity Similarity Rules and Similarity Fragment Intensity for ID* Yes (ranking) Yes No Yes in silico Library (Types) 56 34 24 59 User Developed Libraries Yes Difficult Difficult Difficult Programming Language R C# C++ Java Restrictions None None None License Multiple Vendor Formats Yes (.ms2) Yes (.abf) Yes (.mzML) Yes Data Independent Analysis** Yes Yes No No MS 3 analysis No No No Yes Multiple Hits in Final Report Yes (ranked) No No Yes (ranked) Structural Resolution Correct Over Reports Over Reports Correct Identifiers (eg. LipidMaps) No Yes No No Computational time (HR data) Slow Medium Fast Fast Employs False Discovery No No Yes No in silico library all ether linked lipids contained were considered two types (plasmenyl and plasmanyl) and all oxidized lipids contained across numerous classes were considered one lipid type *Please read text for further information **Not discussed in depth in th is manuscript. LipidMatch can be applied to AIF data independent analysis (currently only supports Thermo files), while MS DIAL can be applied to AIF and SWATH approaches ***Correct reporting of structural resolution means that lipids are annotated only at the level of structure known based on fragmentation

PAGE 104

104 Figure 3 1. Annotation of a phosphatidylcholine (PC) species outlining how to annotate each structural detail of glycerophospholipids. The lipid is annotated using Lipid Maps nomenclature based off o f International Union of Pure and Applied Chemists and the International Union of Biochemistry and Molecular Biology (IUPAC IUBMB) Commission on Biochemical Nomenclature. The [R] conformation is often not indicated in annotation, while [S], being the less common form, is specifically referred to. Figure 3 2. General structure for plasmanyl and plasmenyl phospholipid species containing a glycerol backbo ne

PAGE 105

105 Figure 3 3 Options for open source software integration with LipidMatch in a lipidomics data processing workflow. Acquisition modes for fragmentation which can be used to annotate lipids with LipidMatch include data dependent analysis (DDA) and data independent analysis (DIA) for both direct infusion and liquid chromatography (LC) tandem mass spec trometry (MS/MS) approaches. Figure 3 4 Workflow for using LipidMatch, with input and output folder structure and files. Green boxes represent .csv files, dark blue boxes represent open source MS 2 files (.ms2), and filled light blue boxes represent fold ers. Three stacked boxes represent that multiple files are allowed or generated. The subfolders (brain, heart, and plasma) are examples, these folders can be for any biological substrate. In addition if only one biological substrate is analyzed, only the main directory folder is needed. In the outputs generated by LipidMatch each subfolder contains an output folder as depicted above.

PAGE 106

106 Figure 3 5 Simplified flow diagram of LipidMatch operations. The steps for identification of the feature at m/z 850.5604 and retention time (RT) 5.92 as formate adducts of PC(16:0_22:6) and PC(18:2_20:4) are shown as an example in grey boxes for each step Note that the number of lipid identifications and fragments queried in the example are reduced significantly fo r illustration purposes. For Step 5, R1COO and R2COO were required for identification above an intensity threshold of 1000 in at least one scan across the peak.

PAGE 107

107 Figure 3 6 Problematic cases which can arise when ranking lipids by the sum of frag ment intensities. The first panel a) represents a case were lipids are accurately ranked (far right) based on the areas under the peak (far left). It single intensity, but a sum o f the intensity of all precursor isomers (middle). In panel b) two lipids (blue and light green) share a high intensity fragment with the same m/z (middle), inflating their intensity values leading to false ranking (far right). In panel c) the MS/MS scan m isses the apex of the lipid with a blue trace, and hence the summed intensity for the blue trace is reduced.

PAGE 108

108 CHAPTER 4 ANNOTATION AND QUANTIFICATION OF LIPIDS USING AN OPEN SOURCE LC HRMS/MS WORKFLOW AND LIPIDMATCH QUANT 3 Relative Quantification in Lipido mics Lipids diverse biological roles are achieved through the vast heterogeneity and complexity in lipid structure, distribution, and concentration For example, i ndividual lipid s can differ by over six orders of magnitude in concentration [147] while c hemical and physical properties can vary in polarity, structural orientation, and charge state (e.g., charged, zwitterionic, and neutral lipid species). Advances in mass spectrometry and the advent of electrospray ionization (ESI) has enabled researchers to begin to detect this wide diversit y of lipids ; however, quantification of these detected lipids is challenging due to their dynamic range and breadth of chemical properties. For q uantification in lipidomics, either re lative or absolute quantification can be performed. Absolute quantificat ion typically employs matrix matched external calibration curves and/or isotopically labeled internal standards for each lipid quantified This quantitative approach has limited application to untargeted lip idomics analyses due to the enormous d iversity of the lipidome limited availability of appropriate standards to cover this diversity and the cost associated with purchasing hundreds of standards R elative quantification is often sufficient where relative changes are of concern, for example between dise ased and control populations [75] Relative quantification, which doe s not employ a calibration curve, and involves the addition of a smaller set of internal standards representative of the classes of lipids analyzed, is the most commonly used approach for q uantification in untargeted lipidomics experiments Submitted to Journal of the American Society for Mass Spectrometry

PAGE 109

109 The selection o f the most appropriate internal standard to best represent a lipid feature can be challenging. The dynamic range, ionization efficiency, and specificity, which are all important for q uantification can differ depending on the lipid molecule s structure, mo re specifically lipid class, degrees of unsaturation, and number of carbons in f atty acyl constituents Lipid class generally has the greatest effect on ionization efficiency. Previous reports have shown that lipid internal standards spiked into samples at the same concentration have orders of magnitude differences in intensities across different classes [148] Therefore, lipids should generally be quantified using standards from the same lipid class. To account for the number of carbons and degrees of unsaturati on in f atty acyl constituents which both lead to an increase in ionization efficiency [148] two or more lipid standards per class, each with different carbons and degrees of unsaturation is suggested for polar lipids [149] For neutral lipids where fatty acids play a greater role in ionization efficiencies, response curves based on a wide range of internal standards is often employed The differences in carbons are often a more significant contributor to ionization efficiency than that of unsaturation at low concentrations, while at high lipid concentrations the effect of unsaturation on ionization efficiency becomes more pronounced [148] In addition to lipid structure, overlapping chromatogram s, ion suppression, large dynamic ranges in lipid concentration, extraction procedure [149] and other methodological and instrumental factors can affect the amount of lipid signal observed. Ultra high performance liquid chromatography (UHPLC) and high resolution mass s pectrometry (HRMS) can be employed to increase specificity. HRMS reduces the overlap of mass spectral peaks from isobar s, resulting in a decrease in residual

PAGE 110

110 standard deviations of measurements and more accurate peak integrations, which are used for more a ccurate quantification [150] Chromatography also reduces the possibility of peak overlap by adding a n orthogonal dimension of separation, and can reduce ion suppression by separating li pid classes and species, reducing the probabili ty of high abundant lipid classes suppress ing low abundant lipid classes [149] In summary, the best choice of lipid internal standards are those that are lipid class representative a nd elute at similar retention times to the analyte s of interest. Manually selecting representative spiked internal standards and the associated lipid analytes to quantify and applying the algorithm for q uantification can be a tedious process prone to human error especially with lists containing hundreds of lipid species Automation of the quantification process can lead to increased throughput, a reduction in errors, and harmonization of quantification methods within the lipidomics community. Therefore, w e developed LipidMatch Quant (LMQ), which can be integrated in an open source workflow to select the most appropriate internal standards for q uantification within acquired LC HRMS data While numerous open source quantification software for direct infusion based lipidomics currently exists [139, 140, 145, 151] to our knowledge Lipid Data Analyzer (LDA) [144, 152] is the only open source q uantification software for LC based lipidomics u sing class representative lipid standards to return units of concentration. LMQ differs from LDA and commercial lipid q uantification software such as LipidSearch (Thermo Scientific), SimLipid (PREMIER Biosoft), and Lipidyzer (SCIEX), in that it was built t o be integrated into workflows using any combination of peak picking and peak annotation software. For example lipids can be quantified using LMQ and outputs from MS DIAL [128] LipidSearch, or LipidMatch [153] with li ttle to no

PAGE 111

111 modification. In addition, the LMQ algorithm for selecting internal standards for feature quantification is unique ; accounting for both ion suppression effects by matching individual lipid species to lipid internal standards with the closest retention time and ionizati on efficiencies by matching lipids to internal standards by lipid class and adduct. T he effect of lip id structure on q uantification has been investigated previously [56, 149, 150, 154] while to ou r knowledge the effect of different data processing strategies and adducts utilized on final concentrations has not been examined thoroughly in UHPLC HRMS experiments Therefore we investigated different data processing methods (peak area versus peak heig ht, smoothing versus not smoothing) and utilization of different ions and polarities for lipid q uantification using LMQ. Investigating the effect of various aspects of the lipidomics workflows on q uantification using open source tools available to the wide r community is an important step in validating the utility and establishing community wide protocols for relative q uantification in lipidomics. Methods: Lipidomics Workflow and LipidMatch Quant Implementation Lipid Extraction and Data Acquisition Lipids were isolated from 40 L of National Institute for Standards and Technology ( NIST ) standard reference material ( SRM 1950 ) Metabolites in Frozen Human Plasma [155] Lipid internal standards were purchased from Avanti Lipids (Alabaster, AL), which included lysophosphatidylcholine ( LPC (19:0) ) phosphatidylcholine ( PC (17:0/17:0) ) phosphatidylglycerol ( PG (17:0/17:0) ) phosphatidylethanolamine ( PE (17:0/17:0) ) phos phatidylserine ( PS (17:0/17:0) ) triglyceride ( TG (15:0/15:0/15:0) ) ceramide ( Cer (d18:1/17:0) ) and sphingomyelin ( SM (d18:1/17:0) ) were spiked into the plasma at 1.4 nmol, 0.92 nmol, 0.93 nmol, 0.97

PAGE 112

112 nmol, 0.92 nmol, 0.26 nmol, 1.3 nmol, and 0.98 nmol, respectively. The extraction was performed using the Matyash method [156] and samples were reconstituted in 40 L of isopropanol. Samples were injected on to a Waters (Milfor d, MA) BEH C18 UHPLC column ( 50 x 2.1 mm, 1.7 m) held at 50 C with mobile phase A consisting of acetonitrile:water (60:40, v:v ) with 10 mM ammonium formate and 0.1% formic acid and mobile phase B consisting of isopropanol:acetonitrile:water (90:8:2) with 10 mM ammonium formate and 0.1% formic acid at a flow rate of 0.5 mL/min. A Dionex Ultimate 3000 RS UHLPC system (Thermo Scientific, San Jose, CA) coupled to a Thermo Q Exactive mass spectrometer (San Jose, CA) was employed for data acquisition. Three rep licate injections were run in both positive and negative polarity employing alternating full and all ion fragmentation (AIF) scans at a resolution of 70,000. In addition, 15 injections employing targeted MS/MS data acquisition were performed, with the inclusion list for fragmentation containing lipids identified by the LIPID MAPS consortium [102] The ion optic settings for the mass spectrometer included : analyzer temperature of 30 C and S Lens radio frequency level of 35 V The ionization conditions included a sheath gas flow of 30, auxiliary gas flow of 5, and sweep gas flow of 1 arbitrary uni ts, and a spray voltage of 3.5 kV, and capillary temperature of 2 50 C For positive ion mode lock masses of diisooctyl phthalate ( m/z 391.2842) and polysiloxanes ( m/z 371.1012 and 445.1200) were used, while no lock masses were used in negative ion mode. Both AIF and targeted MS/MS injections were used for lipid identification, while full scan data w ere used for feature finding and quantification. The UHPLC gradient use in this

PAGE 113

113 experiment is shown in Table S 4 1, while the mass spectrometric parameters are shown in Table S 4 2. Data Processing The open source data processing workflow for lipidomics is shown in Figure 4 1. The first step in the workflow is feature finding using MZmine 2 [100] followed by annotation with LipidMatch [153] b lank feature filtering (BFF) and quantification by LipidMatch Quant (LMQ). Note that LMQ can be employed with any feature finding and lipid identification software. A two step process was used for feature detection. First features and their respective m/z retention time, and peak heights across samples were detected using an MZmine workflow consisting of mass dete ction, chromatogram building, chromatogram deconvolution using local minimum, isotopic peak grouping, and alignment and gap filling of featur es (the batch mode is in the s u pplemental file; 2017_8_01_MZMine_Batch_Step1.xml ) This step is untargeted, in that no information on expected peaks is utilized. In this work, the untargeted step for feature detection included a blank sample and three replicate injection s of SRM 1950 in both positive and negative polarity. The resulting feature table from the untargeted step was filtered using a modified blank feature filtering (BFF) approach, w h ere the minimum intensity of the re plicate injections had to be at least five fold greater than the blank intensity. The BFF method dramatically reduces the number of peaks which are not from biological origin. After filtering, the median peak height and peak retention time from the SRM 195 0 replicate injections were used to develop a targeted peak list. In the second step, a targeted list of peak m/z and retention time values was generated from the previous

PAGE 114

114 step, and the internal standard m/z values and retention time values were appended to this list. An MZmine workflow consisting of mass detection, targeted peak detection, chromatogram deconvolution, alignment, and gap filling was used (the batch mode is in the s u pplemental file; 2017_8_01_MZMine_Batch_Step1.xml ). R eprocessing the data using a targeted peak list determined from a smaller sample set has two advantages especially for application to larger datasets One advantage is that this workflow significantly reduces data processing time for large datasets, w hile the other advantage is that peak picking and integration using a targeted peak list is more consistent across samples than aligning features from an untargeted workflow. For example if there are six pooled samples which should be representative of th e features present in 100 samples, these pooled samples are the only samples that need to be run through the initial MZmine workflow. Then a target list can be generate d after blank filtration and subsequently used to target features across all 100 samples Note that in this study only three samples were analyzed, and hence all samples were used to determine the targeted peak list. The median of retention time and m/z values across all samples was used rather than the average as often overlapping peaks lea d to average m/z and retention time values which are actually between the two peaks and neither represents the first or second peak. For cases in which there are odd sample numbers the median will always represent the location of a true peak For cases in which there are even sample numbers, the median will represent the average of two peaks and therefor e the value at the i th position of the ranked values can be used where i = n/2, and n is the total number of samples.

PAGE 115

115 Once the final peak list with reten tion time, m/z peak area, and peak heights were obtained, the data w ere annotated using LipidMatch (Figure 4 1). LipidMatch [153] identification was performed using all ion fragmentation data (AIF) and targeted MS/MS data acquisition using precursor ions from lipids determined by the LIPID MAPS consortium [102] If multiple lipids are annotated for a single feature, t he lipid annotations are then ranked by the sum of fragment intensity. The annotated feature table was further reduced to molecular species in positive and negative ion mode by selecting the top most abundant ions for features with the same molecular lipid (this was performed using the Excel "highlight duplicate" and sort function s ) The finalized feature tables with unique lipid molecular species was uploaded into LipidMatch Quant (LMQ) for quantification. A table containing the internal standard name and corresponding concentrations (nmol lipid per mL plasma) was created and uploaded LM Q U ser W orkflow The LMQ software requires two comma separate d value s (.csv) files as input for proper operation. The first required file is a feature table with the following content for each feature: (1) peak height or peak area, (2) lipid annotation, (3) lipid class, (4) lipid adduct, (5) retention time, and (6) m/z The second required file is an internal standard sheet which lists the names of all internal standards added their concentrations, retention time, and m/z for each adduct. The names of the internal standards can be in any format familiar to the user. Examples and templates of the two input tables can be found in the LipidMatch Quant zip file available at < tools/ > and in the supplemental information ( 2017_9_11_LMQ_S ).

PAGE 116

116 The user can easily generate the m/z of the adducts expected for each lipid internal standard using only the internal standard name, with a separate tool, LipidPioneer [157] The user then specifies which internal standard will be used for each lipid class in the internal st andard sheet. Note that multiple lipid classes can be represented by a single internal standard in the internal standard sheet. For example in this work we included the following lipid classes to be quantified by PC(17:0/17:0): PC, Plasmanyl PC, Plasmenyl PC, and OxPC (oxidized phosphatidylcholine). We chose to represent ether linked species using a non ether linked internal standard, as it has been shown that ether linked glycerophospholipids have the same response factor to their non ether linked counter parts [154] This internal standards sheet can be used for later experiments if the same internal standards and chromatographic conditions are employed (a nd there is no retention time drift). After open ing and running the R script in the LipidMatch zip file, popup boxes prompt the user to select the working directory folder for all files (feature table and internal standards sheet). The user is then instruc ted to select the feature table and the internal standard sheet. The user completes a series of input boxes, inputting the location of the columns for m/z, retention time, lipid class, lipid adduct in the feature table, and the row in which data starts. By not predefining the format of the feature table, users can utilize various peak picking and lipid annotation software and directly, or with minor modification, apply LMQ. Other user inputs include retention time and m/z tolerances, which are used for loca ting features representing the internal standards in the feature table using the retention time and m/z values supplied in the internal standard sheet.

PAGE 117

117 LMQ outputs a list of all internal standards that were identified in the feature table using the internal standards sheet. In addition, the feature table, with quantified values and append ed columns containing internal standard information, is created. Each feature includes columns containing information regarding which internal standard ion (molecular species and adduct) was used and a scoring column which allows the user to see how well t he internal standard represents the analyte. Lipids quantified using a score of 2 or 3, should be used only with great caution, as internal standards which match the lipid class of the feature were not found. Since lipid class significantly affects ionizat ion efficiencies, these standards only take into account ion suppression, but not ionization efficiencies. An output table for LMQ can be found in the LMQ supplementary zip file. LMQ A lgorithm A schematic of the LMQ algorithm is shown in Figure 4 2. The LMQ algorithm incorporates a scoring based approach to classify internal standards selected for each feature. A lower score indicates better representation of the feature by the internal standard while a higher score indicates poorer representation (with scores of 1, 2, and 3). For each feature, the LMQ algorithm associates the appropriate internal standard detected. If the feature and internal standard adduct and class match, the feature is scored as a 1. If the current feature class does not match any of the internal standard lipid classes, but the same adduct is found for an internal standard representing a different lipid class, a score of 2 is given. If no internal standard is found for a feature with a matching adduct or class, a score of 3 is g iven (Figure 4 2).

PAGE 118

118 It is important to note that multiple internal standards can be provided for a single lipid class. In this case, the internal standard with the closest retention time is used for each feature of the respective lipid class. Since retentio n time correlates with saturation and carbons in the lipid f atty acyl constituents this will in part account for different ionization efficiencies due to these structural differences. More importantly, ion suppression can vary across retention time, and t herefore using multiple internal standards can better account for these differences in ion suppression. If multiple standards are found using score 2 or 3, the one with the closest retention time to the average retention time for the entire lipid class an d specific adduct is used to quantify all lipids with the class and adduct. Comparison of Q uanti fication U sing D iffer ent D ata P rocessing M ethods and D ifferent I ons Different data processing methods and ions were used for q uantification to determine which m ethods had the greatest effect on the final quantitative values. The comparisons were: smoothing versus no smoothing (smoothing set to 15 in MZmine), peak height versus peak area, q uantification with negative versus positive ions, and q uantification on [M+ Na ] + adducts versus the major precursor ion. The [M+Na ] + adducts were chosen because for the majority of lipids in positive ion mode an [M+Na ] + peak is present, and hence may affect q uantification through competitive ionization. For comparison of similarit y, the slope and r 2 of linear correlations on the log10 value obtained between the two comparative methods were used. In addition, Bland Altman type plots [158] were used to determine the relative percent difference in concentrations using two different methods or ions for q uantification A distinction was that instead of normalizing to the average, as is traditionally done for calculating percent difference to

PAGE 119

119 be visualized in Bland Altman plots [159] the differences were normalized to the minimum values (hence giving a percent increase from th e minimum value). When differences are normalized to the average, the absolute relative percent difference plotted against the fold change (fold changes greater than 1) is non linear and asymptotic to 200 %, while the relative percent difference, calculate d by normalization to the minimum, is linear as compared to fold change and hence is easier to interpret (Supplemental Figure S 4 1). The equation used to calculate relative percent difference is shown below: Where x and y represent concentrations calculated using different methods or ions ( 4 1) For comparison of overall deviation between measurements, the absolute value of x y was taken in the formula above. In this case, if relative percent differences were at or below 50 % using modified Equation 4 1, the results were considered similar (for ex ample 0.5 nmol/mL and 0.75 nmol/mL), while a relative percent difference above 50 % was not considered similar. A sign test was used to determine whether the quantitative values using different methods or ions provided significantly similar results (less than o r equal to 50 % difference) across the majority of features or significantly different re sults (greater than 50 % difference). For example, if 90 out of 100 concentrations calculated using two different methods had equal to or less than 50 % difference, th ey would be considered to generally provide similar results as corroborated by the sign test p value of 0.01; while if 90 out of 100 calculated concentrations were

PAGE 120

120 different by over 50 % they would be considered to generally provide different results as co rroborated by the sign test p value of 0.01. Precision of quantification using different methods or ions for replicate injections was determined using relative standard deviation (RSD). A sign test was used to determine whether features tended to have hig her RSDs in one methodology compared to another. Results and Discussion: Coverage by AIF and Comparison of Data Processing Methods on Lipid Concentration Calculated A table showing a comparison of available lipid q uantification software used to process da ta from UHPLC HRMS/MS workflows is shown in Table 4 1. To our knowledge, LMQ and LDA are the only software programs which are both open source and can employ class representative q uantification using internal standards. While LDA is a full solution, from f eature detection to q uantification LMQ can more easily be integrated into workflows, leveraging other open source tools, for example MZmine and LipidMatch, as employed in this manuscript. Peak picking and lipid annotation can be performed with various sof tware, and parameter optimization can be application, instrument, and workflow specific. Therefore, by integrating LMQ into a larger open source or proprietary lipidomics workflow, users do not need to validate and optimize new peak picking and annotation strategies. The only requirements are a separate column in the feature table for lipid retention time, m/z, class, and adduct. This can be obtained using the text to columns function in Excel if the information is not separated in the native output format. A total of 129 unique lipid molecular species across 16 lipid types were identified in negative ion mode, of which 122 had appropriate internal standards for quantification

PAGE 121

121 (with phosphatidylinositol not having a class specific internal standard). In pos itive ion mode 225 unique lipid molecular species across 20 lipid types were identified, with 185 quantified using appropriate class representative internal standards. The output tables with concentrations calculated for SRM 1950 data acquired in positive and negative mode using LMQ and peak areas can be found in the supplementary file under Example_Files. These outputs are the .csv files generated via LMQ, and include the LMQ quantification score, and the internal standard specie s and adduct used for quantification for each feature. Annotations in column 9 of the tables were obtained from LipidMatch, with an annotation beginning with "1_" representing identifications by targeted MS/MS, and "2_" by AIF. The majority of annotations were obtained using all ion fragmentation (AIF), with the remainder identified using targeted MS/MS. In AIF, the precursor fragment relationship is lost due to the wide isolation window, which allows all ions within the m/z range of interest to be fragment ed. This can lead to a drastic increase in false positives. LipidMatch filters fragments using a correlation cutoff obtained from a linear regression of the elution profile of the precursor against the fragment ions. This AIF algorithm is advantageous in c orrectly annotating closely eluting peaks, as compared to using data dependent scans (Supplemental Figure S 4 2). Due to the high number of fragmentation scans in AIF for any precursor, the elution profile of the reconstructed mass chromatogram of the fragm ent specific to one overlapping isomer, but not the other, can be used to annotate the closely eluting peaks (Figure S 4 2). Example elution profiles of precursors and respective fragments are shown in Figure 4 3 for PC(16:0_20:4) and PC(18:2_20:4) identifi ed in positive and

PAGE 122

122 negative ion mode. For PC(16:0_20:4) all precursor and fragment peaks elute with a similar profile at 7.6 minutes, while for PC(18:2_20:4) all precursors and fragments elute with a similar profile at 7.1 minutes. Overlapping elution prof iles in the AIF reconstructed mass chromatograms are due to numerous lipids of different precursor mass containing the same fatty acyl constituents. For example, note that the shared arachidonic acid (20:4) leads to the same fragmentation elution profile f or NL R1COOH, LPC(R2)+H, and R2COO in Figure 4 3. This indicates why it is important to employ correlation of elution profiles of precursors and fragments in AIF to reconstruct the precursor fragment relationship, rather than only identifying lipids based on the occurrence of their respective fragments in the retention time region they elute. Of all lipids identified in negative ion mode, 98 features were uniquely identified by AIF, 20 uniquely identified using target MS/MS, and 11 identified by both AIF a nd the targeted MS/MS. In positive ion mode, 85 features were uniquely identified by AIF, 88 by targeted MS/MS, and 52 by both. Of the features annotated both by AIF and targeted MS/MS, 100 % had the same annotation (top ranked, considering plasmenyl and p lasmanyl species differing by one saturation the same) in negative ion mode, and 87 % had the same annotation in positive ion mode. Of those in positive ion mode with differing annotations between AIF and targeted MS/MS, the annotations only differed by fa tty acid composition, not by lipid class and total carbons and degrees of unsaturations. The majority (6 of 7) of the features with differing annotations using AIF versus targeted MS/MS were annotated as TGs, which are known to be difficult to annotate due to significant overlap of isomers in the retention time regime. These results suggest that annotation of AIF data using LipidMatch provides similar results to traditional MS/MS

PAGE 123

123 approaches, and has low false positives, especially at the level of sum compo sition and lipid class. A table of features annotated with unique molecular lipids, their quantitative values, and the internal standards used for each feature, can be found in the supplementary zip file containing the LMQ software. Comparisons of the conc entrations calculated using different ions and data processing strategies were made for each of the quantified lipids. For the comparison of different ions and polarities, only those lipid molecules which were represented by both ions, or both polarities, were used. Different data processing methodologies and ions for quantification were measuring three replicate injections. The q uantification comparisons were as follows: (1) s moothed versus non smoothed peak heights, (2) smoothed versus non smoothed peak areas, (3) peak area versus peak height, (4) negative versus positive polarity (peak areas), and (5) major adducts versus sodium adducts (peak areas). The number of features us ed for each comparison, percent difference, and log two of the fold change, are summarized in Supplementary Table S 4 3. Comparisons of smoothed versus non smoothed peak heights, peak area versus peak height, and quantification on positive versus negative i ons, all had an r 2 above 0.9 and slopes about equal to 1 in log log plots shown in Figure 4 4. In addition, a significant proportion of relative percent differences were at or lower than 50 % for comparisons (Figure 4 5), with p values of a two sided sign test less than p < 0.05. Smoothing had the least impact on final concentration, with none of the 185 lipids above 50 % difference, and only two above 25 % difference. Peak height versus peak area also provided relatively similar

PAGE 124

124 concentrations with only a bout 13 % of the 185 lipids above 50 % difference. Of these three comparisons, polarity had the greatest effect on concentration, with 25 % of lipids having percent differences above 50 % (in this case only lipids common between polarities were utilized). This has major implications for which polarity is chosen as "correct" for a given set of lipids, with the greater concentration not always correct for a host of reasons. For example the greater intensity or concentration for one adduct over another could b e due to overlap of similar peaks, not because of better ionization efficiency. For comparisons of peak area versus height, the greatest percent difference was for triglycerides, with concentrations calculated in peak area much greater than those calculat ed by peak height. For ten of the triglycerides, the concentrations calculated using peak area were more than 2 fold higher than those calculated by peak height (over 100 % percent difference; Figure 4 5 B ). A closer look at extracted ion chromatograms (EIC s) and integration using MZmine 2 of these peaks showed a common trend (Figure 4 6). For the triglycerides with minimal difference between peak height and peak area (less than 5 % in Figure 4 6 B and supplementary Figure S 4 3 B ), the peaks were well defined (Gaussian shaped and baseline resolved) without any visual overlap. For the triglycerides with major differences between peak height and peak area (over 100 % in Figure 4 6 A and Supplemental Figure S 4 3 A ), there were overlapping isomers without complete de convolution. Therefore, the integration of multiple overlapping isomers as one peak (improper deconvolution and/or poor chromatographic separation) was the major cause explaining why concentrations calculated using peak areas were much greater than those u sing peak height. In

PAGE 125

125 addition, the number of isomers integrated as one peak varied across samples (Figure 4 6 A and Supplemental Figure S 4 3 A ). This led to a large variation in concentrations calculated using peak areas in the case of overlapping peaks, and hence using peak height in lipidomics may be advantageous when a large portion of isomeric peaks overlap in retention time. The majority of lipid concentrations calculated in positive and negative polarity differed by less than 50 %. For those which diffe red by more than 50 %, there was no clear trend in EICs. For example, the EICs of PC(16:0_20:5) and PC(18:0_20:4) had similar elution profiles between species and as protonated and formate ions (Supplemental Figure S 4 4). While EICs looked similar, concent rations calculated in negative and positive polarity for PC(16:0_20:5) differed by over 2 fold (over 100 %), while for PC(18:0_20:4) concentrations differed by less than 10 %. This data suggest that certain species may have very different ionization effici encies compared to the internal standard and response curves for negative and positive polarity, while others do not. Indeed, Zacarias et al. [154] showed non linearity in intensity versus concentration in negative ion mode irrespective of instrumental parameters, while lipid intensity versus co ncentration in positive ion mode was relatively linear in comparison. While adducts determined in negative ion polarity correlated well and gave similar quantitative values as adducts in positive polarity, sodiated adducts gave very different concentration s (Figure 4 5 D ) and did not correlate with their corresponding adducts in positive polarity (Figure 4 4 D ). For comparison of q uantification using major ions versus sodium ions, a targeted list for sodium was developed by copying retention times and changin g the masses of the [M+H] + and [M+NH 4 ] + ions detected. This

PAGE 126

126 conversion of protonated and ammoniated species to a sodiated m/z was automated by pasting the molecular species into LipidPioneer [157] The targeted peak list was then uploaded and the data were reprocessed using MZmine as descr ibed in the methods section. No trends were observed in EICs between sodiated species and their corresponding adducts ( [M+H] + or [M+NH 4 ] + ). This is potentially due to sodium not being added to solution, and hence concentrations of sodiated species could be impacted by the number of sodium ions dissolved in the mobile phase at the point of elution, the number of competing ions forming sodiated species, co eluting isomers, and the concentration of the analyte. As shown by lack of correlation to major adducts, the concentration of analyte seems to be a minimal factor in the intensity of sodium adducts of the analyte. It is possible that adding signal intensities of all adducts for the same molecular species and the associated standard could improve q uantificati on When adding [M+Na ] + to [M+H] + there was a slight increase in the relative percent difference between the concentrations calculated in positive ion mode compared to negative ion mode for LPCs and PCs and a significant decrease in the percent difference for ceramides. But due to the instability of the sodium adducts intensities across injections, it is not recommended to include them in calculations of concentration and hence we recommend removal of sodiated features from the dataset for quantitative an alysis In addition to the overall difference between concentrations calculated using different data processing methods and ions, the residual standard deviation (RSD) between three replicate injections of SRM 1950 was calculated for each method. For all m ethods, the average RSD was less than 20 % (Table 4 2). Concentrations calculated using positive polarity, peak area, and non smoothed data were more reproducible

PAGE 127

127 across multiple injections when compared to concentrations calculated using negative polarit y, peak height, and smoothed data, respectively, as indicated by a two tailed sign test and lower RSDs (Table 4 2). These results may not be generalizable to all datasets and workflows, and further experiments should be done comparing the effect of these p arameters on RSD. Conclusion s : LMQ is a Flexible Tool for Applying Current Relative Quantification Methods to Various Worfkflows LipidMatch Quant (LMQ) employs internal standards to quantify lipids in UHPLC HRMS/MS open source workflows. The flexibility in the input feature table format allows LMQ to be used as a backend to any lipid annotation software. LMQ utilizes a unique algorithm to select a standard which best represents the lipid being quantified by matching lipid class, adduct, and retention betwee n the feature and the internal standard in order of priority, respectively. LMQ allows for multiple internal standards per lipid class and provides a scoring system allowing for transparency, noting how each internal standard was chosen for each lipid clas s and adduct. Applying LMQ to compare quantitative values obtained using various data processing workflows and ions, we found that the ion chosen for quantification had the greatest effect on the resulting concentrations. Negative and positive ions showed slightly different concentrations, while sodium ions provided drastically different concentrations compared to all other ions. We suggest not to utilize sodium adducts in calculating lipid concentration, at least in cases where sodium is not intentionally added to the mobile phase and samples. Data processing had less of a significant effect, with the greatest difference in calculated concentrations being attributed to peak area versus peak height, when the feature consisted of multiple unresolved chromato graphic peaks.

PAGE 128

128 Additional features which could be employed for relative quantification, include response factors based on instrument response to lipid structure (carbons and degrees of unsaturation), and dialogue boxes to aid users in selecting internal st andards when class representative standards do not exist. Currently, only the relative quantification portion of LMQ is validated, but future work may provide interactive graphing packages to support in depth visualization and statistical analysis. The inc orporation of such packages would allow users the ability to export custom graphs from the LMQ software. Finally, a single interface is under development to incorporate MZmine, LipidMatch, LipidMatch Quant, and MetaboAnalyst [160] into a user friendly lipidomics work flow.

PAGE 129

129 Table 4 1 Comparison of different lipid quantification software which can be applied to UHPLC HRMS/MS data Output IS: Class Specific* Multiple IS per Class** Response Factors*** Vendor Specific License Lipid Data Analyzer Concentration Yes Yes No No Open Source MZmine 2 Normalized Peak Intensities No No No Open Source LipidMatch Quant Concentration Yes Yes No No Open Source SimLipid Concentration Yes Yes No No Purchase LipidSearch Concentration Yes No No No Purchase Can internal standard be matched to features for quantification based on lipid class? ** Can multiple internal standards for a single lipid class be used? *** Are response factors based on lipid structures and resulting ionization efficiencies employed? Table 4 2 Comparison of the relative standard deviation (RS D) of concentrations calculated using different methods or ions Test RSD (Avg) RSD (# >)* Sign Test [M+H/NH4 ] + 5 3 % 31 p = 0.057 [M+Na ] + 10 10 % 49 Pos 4 5 % 10 p < 0.0001 Neg 12 15 % 42 Height 7 5 % 126 p < 0.0001 Area 6 7 % 59 Smoothed 7 6 % 103 p < 0.0001 Not Smoothed 6 5 % 82 *The number of species with RSDs greater in the respective method or ion. Note that comparison for ions were made using peak areas, while those for smooth v ersus not smoothed utilized peak heights.

PAGE 130

130 Figure 4 1 Open source lipidomics workflow employed in this study Figure 4 2 Simplified schematic of LipidMatch Quant (LMQ) algorithm.

PAGE 131

131 Figure 4 3 Examples of extracted mass chromatograms for the precursors and fragments of PC(16:0_20:4) and PC(18:2_20:4) as protonated and formate adducts in positive and negative mode, respecti vely. Fragmentation was obtained by AIF and shows correlation of the elution profile of precursor and fragments for both species. The bold retention time values represent the precursor or precursor fragments, while non bold represent fragments from the pre cursor in the adjacent panel. A) protonated PC(16:0_20:4), B) protonated PC(18:2_20:4), C) PC(16:0_20:4) as a formate adduct, and D) PC(18:2_20:4) as a formate adduct.

PAGE 132

132 Figure 4 4 Linear regression comparing the log 10 of concentrations calculated using d ifferent workflows and ions. A slope of 1 and R 2 close to 1 are expected if the methods or ions both result in similar concentrations. The panels show : A) concentrations calculated using smoothed versus non smoothed peak heights (smoothing was done as the f inal step in MZmine) B) peak are a versus peak heigh C) positive versus negative pola rity using peak area and D) sodium adducts versus the major adduct observed in positive pola rity using peak area. S odium adducts were compared to protonated adducts except in the case of neutral lipids which formed ammoniated adducts.

PAGE 133

133 Figure 4 5 Bland Altman type plots showing differences in concentrations calculated using different methods and ions. See Formula 1 for relative percent difference calculation. Blue a rrows delineate the direction of difference. The panels show : A) the percent differences in concentrations calculated using smoothed versus non smoothed peak heights (smoothing was done as the final ste p in MZmine) B) peak are a versus peak height C) posi tive versus negative pola rity using peak area and D) sodium adducts versus the major adduct observed in positive pola rity using peak area *Note that the differences between major adducts and [M+Na ] + were drastic and ranged over several orders of magnitud e. Therefore, the log of the absolute percent difference was used and then multiplied by 1 when the [M+Na ] + concentration was calculated higher than the major ion.

PAGE 134

134 Figure 4 6 Extracted ion chromatograms (EICs) and peak integration by MZmine of the triglycerides (TGs) : A) with the most, B) and least percent difference when comparing q uantification using peak height versus peak area

PAGE 135

135 CHAPTER 5 EXAMINING HEAT TREATMENT FOR STABIL IZATION OF THE LIPIDOME The Case for Heat Treatment to Improve Lipid Stability in Tissues Over the last decade, lipidomics has steadily gained status as an established strategy for human health and disease research. Inherently linked to this emergence have been the substantial advance s in the lipidomics workflow, specifically advan ce s in lipid extraction, [161] separation, mass spectrometric detection, [27] data processing, and biochemical interpretation/analysis. [162] Success of lipidomics research lies in the unders tanding of the fundamentals and sources of bias/variability at each step of the lipidomics workflow. Perhaps the most overlooked aspects of the lipidomics workflow are the pre analytical steps, those steps or decisions made regarding samples prior to analy sis. Despite community wide awareness of these issues [163 165] the current gap between the perceived gravity of pre analytical influence and the efforts to address these concerns remains large. [166] The preservation of the native omic profile from the time of collection (t 0 ) is an ongoing pre analytical challenge. Flash freezing samples in liquid nitrogen immediately af ter collection is considered the gold standard for halting metabolic activity. Sample collection in the natural environment (field studies) poses a difficulty in this regard, as the availability of liquid nitrogen and other methods for maintenance of ultra cold environments may be limited, and thus analyzed samples may not exhibit a true t 0 profile. Laboratory studies are not exempt from issues using flash freezing. The integrity of samples is maintained by remaining in a frozen state until chemical extract ion; however, it is often necessary to weigh samples prior to extraction where thawing is undoubtedly occurring. At this point, certain lipids can be susceptible to changes via

PAGE 136

136 enzymatic activity, [30] chemical degradation due to pH, and oxidation, [29] thus complicating the interpretation of the observed lipid profile. A study by Wang et al. analyzed the lipidome in mouse and rat plasma and noted that several lipids sign ificantly increased or decreased after 4 hours on a benchtop. [30] To better preserve the lipidome, an enzyme inhibitor (phenylmethylsulf onyl fluoride) was added to the plasma in the study, thus creating a more native profile. For oxidized lipids, chemical stabilizers, such as butylated hydroxytoluene, are commonly used to prevent oxidation. [31] While chemical stabilizers can be applied to matrices such as plasma, stabilizing solid matrices, such as tissues, at the tim e of collection requires alternative approaches. Heat treatment is an alternative approach which has been used as early as the 1940s, for example, to reduce lipid byproducts produced by phospholipase C and D in the presence of alcohols used for extraction. [32] Jerneren et al. examined the application of heat treatment on lipid stability us ing recent technology that precisely controls temperature and creates a vacuum to reduce oxidation in an effort to study post mortem effects on free fatty acid composition in brain and liver tissues. It was found that heat was effective in reducing phospho lipase activity and ex vivo lipolysis in comparison to tissues that were not treated with heat. [167] This idea of heat treatment, as a means to preserve the lipidome in tissues, was expanded upon by Saigusa et al. where it was shown that heat treatment was able to retard me tabolic changes in certain sphingolipids. [16 8] While advantages have been shown for specific lipids, a comprehensive study of the heat treatment effect on the lipidome using state of the art heat treatment equipment, ultra high pressure liquid chromatography (UHPLC), and

PAGE 137

137 high resolution mass spec trometry (HRMS), is missing. In addition, to our knowledge, true t 0 studies, where heat treatment is used as the method of metabolic arrest, have not been shown. In this regard, we aimed to evaluate heat treatment as an approach to improve lipid stability using high resolution tandem mass spectrometric approaches coupled to a UHPLC system. We examined the specific effects of heat treatment on over 40 lipid types including oxidized lipids using LipidMatch lipid identification software [131] in three invertebrate species: the earthworm ( Eisenia fetida ), the house cricket ( Acheta domestica ), and the ghost shrimp ( Palaemonetes paludosus ). The selection of the three invertebrate species was based on the fact that each species has been used previously as bioindicators for environmental monitoring [169 171] and their small size allows them to be directly placed in the heat treatment cartridge for a true t 0 study. Additionally, it has been previously noted that some invertebrate species, like the earthworm, have high enzymat ic content. [172] In metabolomics based studies of earthworms, it was shown that heating whole body metabolite ex tracts was beneficial for improving metabolite and total lipid stability, [172, 173] thus making them ideal candidates for this examination. The current study was performed both to highlight the potential lipid changes that occur immediately following animal sacrifice, and to propose heat tr eatment as a means to reduce this change. We examined whether heat completely stabilizes the lipid profile by allowing heat treated samples to sit for one hour prior to lipid extraction. Based on trends in lipid profiles, with and without heat treatment, w e discuss the underlying enzymatic pathways leading to

PAGE 138

138 the largest lipid transformations, and the benefit of heat treatment to prevent these actions. Materials and Methods Materials The three invertebrate species investigated in this work included earthwor ms ( Eisenia fetida ) (n = 18) ( Haddrell 's Point Tackle, Mount Pleasant, SC), house crickets ( Acheta domestica ) (n = 18) ( Haddrell 's Point Tackle, Mount Pleasant, SC), and ghost shrimp ( Palaemonetes paludosus ) (n = 10) (PetSmart, North Charleston, SC). Female crickets were excluded from analysis based on identification of the ovipositor, and shrimp containing eggs were excluded. Worms, crickets, and shrimp were selected at similar sizes and weights. Optima methanol and HPLC grade chloroform were purchase d from Thermo Fisher Scientific (Waltham, MA, USA). HPLC grade 2 propanol resistivity (Millipore Milli Q Gradient A10; EMD Millipore, Billerica, MA, USA) was used for sample p reparation. Ammonium acetate and analytical grade formic acid were purchased from Fisher Scientific. All mobile phase solvents were Fisher Optima LC/MS grade (acetonitrile, isopropanol, and water). All internal standards used in the mixtures were purchased from either Avanti Polar Lipids (Alabaster, Alabama) or Nu Chek Prep (Elysian, MN). The mixture consisted of the following gravimetrically weighed exogenous internal standards in g lipid per g solvent: CE(17:0) (45.1 g/g), Cer(d18:1/17:0) (5.8 g/g), DG (20:0/20:0) (43.1 g/g), FA(18:0 d 35 ) (34.1 g/g), GalCer(d18:1/12:0) (7.8 g/g), LPC(17:0) (11.4 g/g), LPE(14:0) (15.5 g/g), PA(14:0/14:0) (45.0 g/g), PC(14:1/14:1) (46.0 g/g), PE(15:0/15:0) (17.1 g/g), PG(15:0/15:0) (20.4 g/g), PS(14:0/14:0) (6.6 g/g),

PAGE 139

139 SM(d18:1/17:0) (15.2 g/g), ST(16:0) (3.4 g/g), and TG(17:1/17:1/17:1) (257.5 g/g). About 250 L of the internal standard mixture was gravimetrically weighed and added to each cricket and worm sample prior to extraction, while 150 L of the interna l standard cocktail was added to each shrimp sample. Heat Treatment A schematic of the experimental design is shown in Figure 5 1 Samples were immediately euthanized for heat treatment by placing the whole invertebrate into a Denator Maintainor cartridg e (Supplemental Figure S5 1) which was placed in the nine worms, nine crickets, and five shrimp were euthanized by heat treatment followed by immediate freezing until extracti on. Based on internal measurements employed by 2.9 seconds; maximum temperature, 95.1 0.2 C; minimum temperature, 94.0 0.2 C; and pressure, 4.5 0.9 mbar. Trea tment parameters were significantly less variable for shrimp, likely owing to smaller body mass than worms and crickets. Alternatively, samples were euthanized by rapidly freezing in aluminum foil in a f the samples analyzed, nine worms, nine crickets, and five shrimp were euthanized by rapid flash freezing. All common house crickets and earthworms (heat treated and flash frozen) were then cryo pulverized using a Retsch cryomill (Retsch, Haan, Germany), and the resulting powder was then transferred to a 2 mL cryovial and subsequently placed in a 80 C freezer overnight. Whole shrimp were placed in 2 mL cryovials without cryo pulverization because of their small size. All samples were stored for less tha n 12 h in a 80 C freezer before extraction.

PAGE 140

140 Lipid Extraction Shrimp immediately weighed (average mass of 0.11 g, n = 10) and homogenized in a Bullet Blender (Next Advance, Averill Park, NY USA) with 1 mL cold methanol using 0.5 mm zirconium beads (Next Advance, Averill Park, NY) for 4 min cycles at speed 10. The shrimp homogenate was then transferred to a new vial for lipid extraction. Cricket and earthworm The cricket and earthworm powd ered samples were extraction. The tissue powder (cricket: average mass, 0.089 g (n = 18); earthworm: average mass, 0.10 g (n = 18)) were then transferred into new vials. To evaluat e lipid stabilization as a result of the euthanization method (heat treated or flash frozen), 10 cricket and 10 earthworm samples (5 heat treated and 5 flash frozen per organism) were incubated on ice for 1 h prior to lipid extraction. Lipid extraction fol lowed the Bligh Dyer extraction protocol; [38] three mL of a methanol/chloroform mixture (2:1, v:v ) were added to all of the samples. Then, 250 L of the internal standard mixture was added to the cricket and earthworm homogenates, while 150 L was added to the shrimp homogenates. Following the addition of the internal standard, 1.8 mL of water was added to the cricke t and worm homogenates, while 1.7 mL was added to the ghost shrimp homogenates. One milliliter of chloroform was then added to all of the homogenates and vortexed. The samples were subsequently centrifuged (IEC Centra CL3, Thermo Fisher Scientific, Waltham MA, USA) at 2000 rpm for 10 min, and the chloroform layers were transferred to new vials. An additional 2 mL of chloroform were added to the homogenate samples for re extraction and samples were vortexed and centrifuged again. The chloroform layers

PAGE 141

141 were combined with the previous chloroform layers. Samples were dried (Biotage TurboVap LV, Charlotte, NC, USA) and reconstituted in 1 mL of 2 propanol. Then, samples were transferred to autosampler vials, dried, and reconstituted in 200 L of 2 propanol to co ncentrate them for mass spectrometric analysis. Lipidomic Analysis via Mass Spectrometry Cricket, earthworm, and ghost shrimp reconstituted extracts were kept at 4 C in the autosampler. Five L was analyzed using reversed phase liquid chromatography with a Waters Acquity BEH C18 column (50 mm 2.1 mm, 1.7 um, Waters, Milford, MA) maintained at 30 C on a Dionex Ultimate 3000 RS UHPLC+ system (Thermo Scientific, San Jose, CA). The gradient ramp was performed using mobile phase A (60:40 acetonitrile:water, v:v) and mobile phase B (90:8:2 isopropanol:acetonitrile:water, v:v:v), both with 10 mmol/L ammonium formate and 0.1 % formic acid (Supplemental Table S5 1). Mass spectra were acquired using a Q Exactive Orbitrap (Thermo Scientific, San Jose, CA) equipped with a heated electrospray ionization (HESI II) probe, in polarity switching and data dependent top 10 (ddMS 2 top10) modes. In positive ion mode, diisooctyl phthalate ( m/z 391.2842) and polysiloxanes ( m/z 371.1012 and m/z 445.1200) were used as lock masses No lock masses were used in negative ion mode. The Q Exactive was externally calibrated before data acquisition. The S lens voltage, skimmer voltage, inject flatapole offset voltage, and bent flatapole DC voltage were 35 V, 25 V, 15 V, 8 V, and 6 V, resp ectively. All samples were analyzed in batches (each invertebrate constituted a batch) with at least three solvent blanks analyzed per batch. Samples were randomized within batches. Four ddMS 2 top10 analyses were conducted

PAGE 142

142 for each invertebrate for lipid i dentification (two in positive polarity and two in negative polarity). Scanning parameters are provided in Supplemental Table S5 2. Data Processing A list of features ( m/z and retention time) and peak areas were obtained using MZmine 2.2. [100] Chromatograms were smoothed and deconvoluted using a local minimum search; isotopic peaks were grouped, features were aligned, and missing features were gap filled. Peaks with peak heights below 1 x 10 5 intensity were removed. An MZmine batch file contain ing exact parameters can be found in the Supplementary Information. Features were then identified using a proprietary software LipidSearch (Thermo Scientific, San Jose, CA) [99] and an open source software Lipi dMatch [131] which can be accessed at . These software have different confirmation criteria and fragmentation libraries, thereby adding additional confidence if features were identified as the same lipid by bot h software and expanding the total number of identified lipids. Resulting identifications from both LipidSearch and LipidMatch were aligned with code available in LipidMatch to the MZmine outputted feature list. A script was also used to combine duplicate features using a retention time window of 0.1 and an m/z window of 0.006, retaining the maximum value of all the replicates. Identified lipids were semi quantified using class representative standards, and if no standards were analyzed for the lipid class in question, the standard with the greatest similarity in structure and retention time was used (Supplemental Table S5 3). Quantified lipids were normalized to tissue weight (g lipid per g of wet tissue). A list of defined acronyms and the observed adduct s in LipidMatch are provided in Supplemental Table S5 4.

PAGE 143

143 After quantification, features in negative and positive polarity were combined, using negative mode data if molecular species were represented in both polarities. Lipids quantified in negative mode w ere prioritized due to lower background noise and more accurate annotation due to high intensity fatty acyl fragments. [174] Molecular species represented by multiple adducts were quantified using the adduct with the highest calculated concentration. Tentative identifications, such a s identifications by exact mass only or identification of sphingomyelin (SM) or phosphatidylcholine (PC) solely by the m/z 184 fragment ion, were removed. Sodium adducts were also removed from the final dataset, as q uantification on the sodium ion without excess sodium is problematic. For oxidized lipids, which are both present in LipidMatch and LipidSearch databases, only lipids identified by MS/MS in both negative and positive polarity were kept to reduce the probability of false positives. Therefore, oxi dized lipids that only occur in positive polarity, for example TGs, were excluded from analysis; oxidized lipidomics is an emerging field without proper guidelines for proper annotation, and therefore only the most confident identifications were retained. Statistical Analysis After quantification and annotation of features, both univariate and multivariate statistical methods were applied. Mean centering and a generalized log transformation (Glog) was used prior to principal component analysis (PCA) using Metaboanalyst 3.0. [160] For univariate methods, analysis of variance (ANOVA) was used to investigate the following questions: (1) euthanization method: heat treated versus flash frozen (D vs N), (2) heat stabilization: direct extraction versus 1 h ice incubation then extraction (D vs D1hr), (3) flash frozen stabilization: direct extraction versus 1 h ice incubation then

PAGE 144

144 extraction (N vs N1hr), and (4) compare stabilizations: heat treate d versus flash frozen samples (D1hr vs N1hr). For shrimp, only the comparison between the euthanization method (D vs. N) was made. The p values from ANOVA across all comparisons were adjusted using Benjamini and Hochberg's false discovery rate (FDR) method [175] Analyses across different orga nisms, were treated as separate experiments, and hence FDR was calculated separately for each of these instances. To determine individual lipids that were significant, an FDR adjusted p value of 0.05 was used as the cutoff. To determine if there were trend s by lipid class, a Fisher's exact test was used with a p value cutoff of 0.05 using the Fisher.test R function. A one sided Fisher's exact test was used to determine if a significant number of lipids within a class were upregulated or down regulated compa red to lipids across all classes. A list of potentially upregulated or downregulated lipids for Fisher's exact test was determined using an FDR adjusted p value of 0.25. Note that an FDR adjusted p value of 0.25, means that about one of four significant co mpounds will be a false positive. Because the Fisher's exact test was used to determine general trends across lipid classes, we are not interested in whether a particular lipid is significant at this FDR rate, but whether lipids increase or decrease within a class compared to across all classes using this FDR rate. Results and Discussion : Evidence for Deactivation of Lipases Lipid identifications from extracted common house crickets, earthworms, and ghost shrimp, using LipidSearch and LipidMatch generally a greed (Supplemental Table S5 5), especially at the level of lipid class, total carbons, and degrees of unsaturation. There was better agreement between software in negative ion mode than positive ion mode, and therefore when combining polarities, if a mole cular species was represented by both polarities, negative ion mode data was retained. Using these software, a total of

PAGE 145

145 426 (common house cricket), 593 (earthworm), and 429 (ghost shrimp) unique lipids were annotated by MS/MS (Supplemental Table S5 5). A t otal of 40 unique lipid types were identified when combining lipids from all sampled organisms. For an overview of the lipidome coverage for each organism, distributions of lipid concentrations and the number of species annotated per class are presented in pie charts in Supplemental Figure S5 2. Multivariate statistics were used to determine general changes in the lipid profile across samples and treatments. PCA scores plots showed different patterns of enzymatic degradation or enzymatic transformation of lipids depending on organism ( Figure 5 2 ). All heat treated (D) and flash frozen samples (N) were separated along the first principal component (PC1) with t test p values of the scores less than 0.05, and PC1 explained variances of 25 % (earthworm), 66 % ( cricket), and 62 % (shrimp) ( Figure 5 2 ). In crickets, PC2 better separated heat treated and flash frozen samples, with a p value of 0.01 compared to 0.03 for PC1 scores of all heat treated versus frozen samples. To determine whether lipid degradation con tinued following euthanization, select samples were incubated on ice for 1 h (D1hr and N1hr) ( Figure 5 1 ). For crickets and worms, the PCA scores plot suggests the heat treated samples (D) and flash frozen samples (N) had the least amount of variance, whil e samples after 1 h incubation on ice (D1hr and N1hr) showed higher variance ( Figure 5 2 A and Figure 5 2B ). This suggests lipid changes occurred after an hour on ice, and that heat treatment did not completely stabilize the lipids. The earthworms had disti nct separation between flash frozen (N and N1hr) and heat treatment (D and D1hr) ( Figure 5 2 B ), unlike the crickets ( Figure 5 2 A )

PAGE 146

146 where the variability of the ice incubated samples (D1hr and N1hr) resulted in less distinct groupings. The top 30 features indicative of the lipids differentiating between flash frozen (N) and heat treated samples (D) in PCA and their loadings are shown in Supplemental Table S5 6. Of the top 15 features influencing heat treated samples, for crickets, worms, and shrimp, 100 % consisted of intact glycerophospholipids and oxidized glycerophospholipids, suggesting minimal enzymatic degradation compared to flash frozen samples. Of the top 15 features indicative of flash frozen samples over 60 % consisted of lysoglycerophospholipids for crickets and 100 % consisted of lysoglycerophospholipids for worms (Supplemental Table S5 6a and Supplemental Table S5 6b), indicating enzymatic degradation of intact glycerophospholipids to lysoglycerop hospholipids. For shrimp, 87 % of the top 15 loadings indicative of flash frozen samples consisted of phosphatidylmethanol (PMe), an enzymatic product produced when using methanol for extraction (Supplemental Table S5 6c). These enzymatic products are lik ely generated in the pre analytical steps of lipid analysis, such as homogenization or freeze/thawing, which can cause lipid membranes to rupture, resulting in stored calcium in the mitochondria and endoplasmic reticulum lumen to be released. Calcium activ ates proteases, lipases, and kinases, which in turn can lead to degradation of lipid species. [176] For example, glycerophospholip ids (GPL) in the presence of phospholipase A, phospholipase C in concert with various kinases, and phospholipase D in concert with PA phospho hydrolase are cleaved, producing products including diglycerides (DGs), lysophosphatidylcholines (LPCs),

PAGE 147

147 lysophosp hatidylethanolamines (LPEs), and LPC and LPE plasmanyl and plasmenyl corollaries, as shown in Figure 5 3 In a univariate analysis of the euthanization effect on lipids, a Fisher's exact test was used to determine trends across lipid classes. The Fisher's exact test p values ( Table 5 1 and Supplementary Table S5 7) show which lipid classes were the most significantly upregulated or downregulated in comparison of euthanization techniques. One sided Fisher's exact test on heat treated (D) and flash frozen (N) shrimp and worm samples showed an overall increase in LPC and LPE species in flash frozen samples ( Table 5 1 b and Table 5 1 c), with p values less than 0.005. The general lower concentration of lyso glycerophospholipids (lyso GPL) in heat treated samples ( Table 5 1 ), provides evidence that phospholipase A 2 activity [177] ( Figure 5 3 ) was reduced. For crickets, LPC and LPE species did not differ in heat treated (D) and flash frozen samples (N) that were not incu bated on ice. While no significant difference in LPC and LPE was observed between heat treated (D) versus flash frozen (N) cricket samples, which were immediately extracted, after samples were incubated on ice for one hour, there was a significant differe nce between heat treated (D1hr) and flash frozen (N1hr) cricket samples (p value < 0.005; Table 5 1 a). This is due to an increase in LPC and LPE concentration only in samples without heat treatment after one hour incubation (N1hr versus N). On the contrary one hour incubation on ice (N versus N1hr and D versus D1hr) did not increase enzymatic production of LPC and LPE species in worm samples, except ether LPC and ether LPE species for flash frozen samples (N versus N1hr) ( Table 5 1 b). Ether linked lipids a re more resistant to enzymatic degradation by phospholipase A 1 [178] and therefore

PAGE 148

148 slower degradation may explain the continued increase of ether LPC and e ther LPE after one hour in samples without heat treatment. This data indicates that the amount, and rate of enzymatic activity are both dependent on lipid class and the sample organism. LPCs incorporating very long chain fatty acids with over 22 carbons w ere detected in shrimp and worm by LipidSearch using MS/MS ( Table S5 5). For LPC species with 22 or less carbons, 14 out of 16 LPC species in worms, and 8 of the 10 LPC species in shrimp, had FDR p values less than 0.05 for heat treated (D) versus flash fr ozen (N) samples. For species with over 22 carbons, of the 8 LPC species in worms, and 6 LPC species in shrimp, none had FDR corrected p values less than 0.05 ( Table S5 5). These results suggest that due to the unique enzymatic synthesis and degradation of very long chain fatty acids, [179] either heat treatment is ineffective at preventing enzymatic activity, which generates LPC species with over 22 carbons, or significant enzymatic activity generating LPCs for species with over 22 carbons does not occur during sample preparation. Hence, not only is enzymatic activity dependent on the organism and lipid class, but may also be dependent on fatty acyl constituents. While low concentrations of lyso GPL in heat treated organisms (Supplemental Table S5 5) support that heating reduced phospholipase A activity for species with 22 or less carbons, intact glycerophospholipids would be expected to be higher in heat treated organisms, as these lipids would not be cleaved by phospholipase A ( Figure 5 3 ). Howeve r, glycerophospholipids were not significant according to the Fisher's exact test (Supplemental Table S5 7), potentially owing to the small sample size used in this study. In this case, trends in GPL may be less likely to be discerned against biological

PAGE 149

149 va riation and other sources of noise because of the relatively small percent change in concentration compared to their enzymatic products, lyso GPLs. For example, the total LPE concentration in shrimp was 417 % higher (p value = 0.0000009) in flash frozen sh rimp (11.8 0.8 g/g) compared to heat treated shrimp (2.3 1.1 g/g), while the average total PE concentration was 27 % lower (p value = 0.032) in flash frozen shrimp (647.4 44.3 g/g) compared to heat treated shrimp (820.8 123.6 g/g) (Supplemental Figure S5 3). Using the total sum of PC, PE, and respective plasmanyl and plasmenyl species of earthworms (including samples with and without the additional 1h incubation) suggested that heat treatment reduced degradation of PE to LPE, PC to LPC, and ether linked species to their respective lyso species (Supplemental Figure S5 3 and Supplemental Figure S5 4). Our results agree with the findings of Wang et al. where lyso GPL were lower and GPL higher in heat treated rat and mouse plasma samples compared to control samples stored on a benchtop. [30] Glycerophospholipids can also be cleaved by phospholipase D to form phosphatidic acids (PA), which can further be converted into DGs by PA phosphohydrolase ( Figure 5 3 ). While no PAs were detected in samples, DGs were detected in crickets and shrimp (Supplemental Table S5 5; Supplemental Table S5 7). In shrimp, all DGs detected were a n order of magnitude lower in heat treated samples, with FDR corrected p values < 0.005, and Fisher's exact test p values < 0.005 (Supplemental Table S5 5, Table 5 1 c). This trend was not observed for crickets, but, when summing total DG concentration incl uding the samples incubated for 1 h, DGs were 122 % greater (p value = 0.041) in flash frozen compared to heat treated crickets. Further evidence that heat treatment denatures, and hence deactivates, phospholipase

PAGE 150

150 D is shown by the presence of phosphatidyl methanol (PMe) in frozen shrimp ( Figure 5 4 A ). It has been shown previously that water can be replaced with alcohols as acceptors in transphosphatidylation via phospholipase D [34] ( Figure 5 3 ), and as shrimp were homogenized in methanol, PMe are expected. In the flash frozen samples, 14 PMe species were detected as deprotonated ions, while no PMe ions were found in heat treated samples. It is important to note that PMe was not detected in crickets or earthworms, as these organisms were large enough for cryopulverization, eliminating the need for an additional homogenization step using methanol. Therefore, it is important to note, that in addition to lipid structure and sample organism affecting enzymatic activity, the sample homogenization and extraction procedure employed also affects enzymatic activity. Phospholipase D has multiple isoforms that hydrolyze PC, PE, phosphatidylglycerol ( PG), phosphatidylserine (PS), phosphatidylinositol (PI), LPC, cardiolipin, and plasmalogen head groups. Certain lipid classes and f atty acyl constituent constituents will preferentially be hydrolyzed depending on the organism. [180] In shrimp, PE(20:5_22:6), PS(20:5_22:6), PC(20:5_22:6), and potential enzymatic products PMe(20:5_22:6), DG(20:5_22:6), LPE(20:5), LPC(20:5), LPE(22:6), and LPC(22:6), suggest PE, PS and PC conversion by phospholipase D into PMe and DG at least for GPL s containing 20:5 and 22:6 ( Figure 5 4 A I ). Potential fatty acid preferences for enzymatic reaction of phospholipase D existed. For example, in all 13 DGs generated by enzymatic reaction in shrimp (Supplemental Table S5 5), each DG contained either a 20:4, 20:5, 22:5, or 22:6 f atty acyl constituent despite high intensity species such as PC(16:0_18:1) and PE(16:0_18:1).

PAGE 151

151 Phosphatidylserine (PS) lipid species can be substrates for a specific lipase: PS specific PLA 1 For shrimp, a closer investigation of PS( 20:5_22:6) ( Figure 5 4 B ) suggests that the lower concentration in frozen samples is mainly because of phospholipase D and not PS specific PLA 1 or phospholipase A The difference in the amount of degradation products in frozen shrimp compared to heat treat ed shrimp was found to be similar to the difference in the product source assuming phospholipase D activity. While PC(20:5_22:6) and PE(20:5_22:6) are only 11.7 g/g lower in frozen shrimp compared to heat treated shrimp, their likely degradation products in the presence of phospholipase D, PMe(20:5_22:6) and DG(20:5_22:6), are 74.8 g/g higher in frozen shrimp. This suggests another source of these degradation products. When PS(20:5_22:6) is included as a source of DG(20:5_22:6) and PMe(20:5_22:6) via phos pholipase D activity, the concentration becomes 66.9 g/g ( Figure 5 4 A C ) for sources of the 74.8 g/g enzymatic products ( Figure 5 4 D K ). This suggests that in shrimp, phospholipase D degradation of PS species predominate, versus PS specific PLA 1, which would lead to lysophosphatidylserines (LPS), which were not detected. Other sources of PMe(20:5_22:6) could be PI(20:5_22:6), PG(20:5_22:6), or PA(20:5_22:6), but none of these species were detected. In addition, DG(20:5_22:6) could be a product of triglyc eride lipase activity for TG(16:0_20:5_22:6), TG(18:0_20:5_22:6), and TG(18:1_20:5_22:6) lipids, but together these only accounted for less than 5 g/g in concentration, and hence could not be a major factor in the 45.1 g/g increase of DG(20:5_22:6) obser ved in flash frozen shrimp. This study supports previous evidence that lipase activity depends on f atty acyl constituents, which have been shown for numerous lipases including neutral

PAGE 152

152 lipases [181, 182] and phospholipase D. [180] Oxidized lipids have potential to also show various trends depending on acyl chain composition, as they can be produced both via enzymatic and chemical routes ow ing to exposure to catalysts, electromagnetic radiation, and/or oxygen. [183] On average, all 9 oxidized PEs (OxPE) and oxidized PCs (OxPC) were higher in concentration when incubated on ice for 1 h compared to those immediately extracted after weighing for both heat treated (except 1) and frozen crickets, but were not significan tly different based on FDR corrected p values and Fisher's exact test (Supplemental Table S5 5a and Supplemental Table S5 7a). The only significant change in oxidized lipids was a general increase in OxPE after one hour in the heat treatment samples (Fishe r's exact test p value = 0.016; Supplemental Table S5 7a). Measurement of oxidative markers such as malondialdehyde could be used in the future to determine if heat treatment increases oxidation. This manuscript investigated heat treatment as a mechanism to prevent enzyme degradation of lipids. Untargeted studies often are not concerned with absolute q uantification but instead investigate profile changes in response to the experimental design. If all samples degrade similarly, and compounds are detectable the occurrence of degradation (within acceptable experimental design) may not be a hindrance for a biomarker type assessment. Based on this data, it is not conclusive whether heat treatment reduces variance introduced from enzymatic activity. RSDs across all features (Supplemental Table S5 5) did not follow any trend between heat treatment and flash frozen samples (t test). Surveying RSDs of total LPC and LPE concentrations across earthworms and crickets showed higher variability in flash frozen samples c ompared to heat treated samples when incubated and non incubated samples were

PAGE 153

153 combined ( Table 5 2 ). When RSD's for incubated and non incubated samples were compared without combining euthanization methods (D vs N and D1hr vs N1hr compared separately) there was no clear trend in RSD between heat treated and flash frozen samples. This suggests heat treatment reduces variation, when samples are left on a benchtop for varying amounts of time, but may not reduce variance if all samples are left on the benchtop f or the same amount of time prior to extraction. If the amount of degradation is consistent across all samples, then for comparative studies where only relative amounts of lipids are of concern, heat treatment may not be necessary. A future study with a lar ger sample size that minimizes biological variability, such as aliquots of monoclonal cells grown under the same conditions, could provide more conclusive evidence for using heat treatment for stabilization of the lipidome. Conclusion : Heat Treatment Warr ants Further Investigation Our study provides evidence that heat treatment deactivates enzymes in the early pre analytical phase, which may provide better qualitative and quantitative results for lipids. Based on higher concentrations of PE and PCs, and lo wer concentrations of LPE, LPC, DG, and PMe species in heat treated organisms, heat treatment reduced or prevented enzymatic degradation of important lipid types in common house cricket, earthworm, and ghost shrimp extracts. This evidence supports reductio n in the activity of phospholipase A and phospholipase D, specifically. However, non enzymatic processes such as oxidation may continue to occur in both heated and frozen samples. We believe that heat treatment could help minimize variance within tr eatment groups that can occur as a result of freeze thaw cycles, variable time between sample collection and freezing, or other factors that affect the rate or duration of enzyme activity. To compare and integrate lipidomes characterized across the interna tional

PAGE 154

154 community, sample stabilization is extremely important, and heat treatment may be a simple solution. This is especially noteworthy for solid matrices, where the inclusion of additives to slow lipid degradation is impractical. Additional studies with larger samples sizes and a homogenous replicated sample would be beneficial in determining whether heat treatment stabilizes the lipid pro file during sample preparation.

PAGE 155

155 Table 5 1. L ipid enzymatic products observed for worm, cricket, and shrimp, respectiv ely, and the p values of the Fisher's exact test. Bold p values are less than 0.05. The Fisher's exact test was used to determine if the lipid species within a particular lipid class significantly increased or decreased as compared to other lipid species. Comparisons are for flash frozen (non Denator) versus heat treated (using the Denator device) samples (N vs D), flash frozen samples which sat on ice for one hour prior to extraction versus heat treated samples which sat on ice for one hour prior to extrac tion (N1hr vs D1hr), flash frozen versus flash frozen samples which sat on ice for one hour (N vs N1hr), and heat treated versus heat treated samples which sat on ice for one hour. Tables are for A) earthworms, B) common house cricket, and C) ghost shrimp. Table 5 1 a) Earthworm LPC LPE ether LPC ether LPE N vs D 0.000 0.000 1.000 1.000 N1hr vs D1hr 0.000 0.000 0.000 0.000 N vs N1hr 0.315 1.000 0.003 0.000 D vs D1hr 0.694 1.000 1.000 1.000 Table 5 1 b) Common H ouse C ricket LPC LPE ether LPE DG N vs D 1.000 0.142 1.000 1.000 N1hr vs D1hr 0.000 0.000 0.028 1.000 N vs N1hr 0.000 0.000 0.012 1.000 D vs D1hr 1.000 1.000 1.000 1.000 Table 5 1 c) Ghost Shrimp LPC LPE PMe DG N vs D 0.006 0.001 0.000 0.000

PAGE 156

156 Table 5 2. Residual standard deviations (RSD) of lysophosphatidylcholine (LPC) and lysophosphatidylethanolamine (LPE) measured in heat treated (D All) and flash frozen samples (N All) acros s all samples, including those which were immediately extracted and those whi ch sat on ice for one hour. Cricket Worm N All D All N All D All LPC RSD (%) 72 29 23 20 LPE RSD (%) 72 41 44 32 Figure 5 1 Experimental design. Earthworms and crickets were utilized to study the comparison of euthanization by heat treatment or flash freezing, followed by extraction of the homogenized tissue. Additionally, these organisms were used to assess the stabilization of the lipidome by the euthanization method with a 1 h ice incubation. Ghost shrimp were used to test the same euthanization methods but instead incorporated a methanol homogenization step during extraction.

PAGE 157

157 Figure 5 2 Principal components anal ysis (PCA) of based on (N) flash frozen, (N1hr) flash frozen and 1 h ice incubation, (D) heat treatment, and (D1hr) heat treatment followed by 1 h ice incubation (next page) PCA plots are for: A) Crickets, B) Worms, and C) Shrimp

PAGE 158


PAGE 159

159 Figure 5 3 Schematic of enzymatic degradation of glycerophospholipids ( G PL) and triglycerides (TG). Products and intermediates include fatty acids (FA), lysoglycerophospholipids, phosphatidic acid (PA), and diglycerides (DG). Enzymes involved include phospholipase A1 and A2 (PLA1 and PLA2, respectively), phospholipase C (PLC), phospholipase D (PLD), and triglyceride lipase (TGL). The FA and LPL products from cleavage via PLA1 are not shown.

PAGE 160

160 Figure 5 4 Box plots of lipid species concentrations in flash frozen (N) and heat treated (D) shrimp Lyso lipid (LPC and LPE) concentrations are from protonated ions, glycerophospholipids PE and PMe are deprotonated ions, PC are formate adducts, and DG are ammonium adducts. P values are Hochberg FDR corrected with three significance levels: p < 0.05 (*), p < 0.005 (**), and p < 0.0005 (***) Panels show: g lycerophospholipids containing 20:5 and 22:6 ( A C ) and potential enzymatic products ( D I ).

PAGE 161

161 CHAPTE R 6 CONCLUSION AND FUTURE PROSPECTIVES While lipidomics has significant potential in aiding biomarker discovery and our understanding of health and disease, the lipidomics workflow is tedious and there is no community wide co nsensus on the proper data pro cessing protocols Without consensus on protocols, untargeted lipidomics is unable to provide r eproducible annotations and measurements of lipids that are needed for a daptation to the clinical field Therefore we introduce modular lipid data processing to ols which are available to the wider lipidomics community These tools cover all the major steps of the lipidomics workflow (Figure 1 7) including data conversion to open source format, peak picking and processing (feature finding), blank filtration, anno tation, quantification, and removal of redundant adducts Increasing the throughput and reproducibility of lipidomics data pro cessing, these tools increase the coverage of the lipidome and introduce new protocols for lipid feature finding and q uantificatio n Specifically, we introduce data acquisition methods (iterative exclusion) and lipid annotation strategies (LipidMatch) which drastically increase the coverage of the lipidome The expanded coverage allows for characterization of species that are often un identified, such as oxidized lipids and ammoniated adducts of cardiolipin. We achieve this by expanding the current high resolution in silico MS/MS libraries for lipids and developing software to match in silico fragmentation to experimental spect ra acquired using data dependent and data independent acquisition. In contrast to data dependent acquisition, data independent acquisition provides MS/MS of less abundant lipids, which are important in signaling and other important lipid functions. In data independent analysis specifically all ion fragmentation, precursor fragment

PAGE 162

162 relatio nships are lost due to isolating ions in a window that spans the entire mass range of interest ; therefore, fragments could originate from any precursor. T he algorithm empl oyed in LipidMatch reconstructs precursor fragment relationships by correlati ng precursor chromatographic elution profiles to fragment elution profiles This correlation dramat ically reduces false positives by only using fragments from the precursor in que stion for identification and excluding fragments from other coeluting ions However, the correlation of elution profiles drastically increases false negatives lipids which truly exist but are left unidentified False negatives occur because overlapping el ution profiles for fragments or precursors reduc e correlations below the minimum threshold and as a result, the se fragments are excluded from use in identification even though they came from the precursor of interest Therefore, to better annotate low abundance lipids, we designed a script to apply iterative exclusion data dependent tandem mass spectrometry. The script which we have called IE Omics generates exclusion lists from ions selected in previous acquisitions After applying the exclusion list to a sequential injection only unique ions unselected in the previous acquisition are fragmented. W hen a pplying this method iteratively, over 60 % more lipids can be annotated This technique provides a drastic increase in coverage as compared to tra ditional data dependent analysis and AIF When all three techniques are used together the greatest coverage of lipid fragmentation and hence annotations are obtained. In addition to increasing lipid coverage, it is important to report only the structura l details known (referred to as structural resolution) based on experimental data LipidMatch addresses this issue by us ing an annotation style which provides users with

PAGE 163

163 only the structural resolution that is known based on their experimental data. While L ipidMatch's accurate reporting of structural resolution is an advantage over other open source software, traditional lipidomics experiments employing UHPLC HRMS/MS do not provide exact lipid molecular structure However, m ass spectrometry techniques have b een developed to report more detailed structural resolution of lipids, including fatty acyl positional isomers, double bond position, and double bond cis / trans isomerism. P ositional isomers such as s n 1 and sn 2 isomers can be distinguished in tandem mass spectrometric approaches using the relative ratios of the fatty acyl fragments. However, b ecause these relative ratios can vary between instruments, fragmentation method, and lipid classes, internal standards with varyin g ratios of sn 1 and sn 2 isomers must be used for quantitative approaches. Standards are often impure and therefore must first be characterized by measuring the ratio of the fatty acid concentrations after treatment with phos pholipase A2, which removes fat ty acids only from the sn 2 position [109] Additionally the lack of synthetic lipid standards to represent the diversity of lipid structures prohibits absolute certainty in the intensity of lipid fragments derived from the glycerol backbone [121] However, one promising technique f or identification of double bond positions is ozone induced dissociation (OzID) [184] although specialized equipment for onsite generation of ozone and flow control is needed. In OzID, a traditional tandem mass spectrom etric approach to characterize lipids by fatty acyl constituents is followed by the introduction of ozone, which induces fragmentation indicative of double bond positions. Another method for determining double bond location as well as cis and trans isoform s is the use of silver ion liquid chromatography, where

PAGE 164

164 double bonds of unsaturated lipids with the silver particles in the stationary ph ase This technique often re quires chromatogr aphic analyses on the order of hours to separate out all isomers, and hence is impractical for high throughput studies with large sample sizes [185] Another promising method for identification of both sn 1 and sn 2 fatty acyl positions, double bond positions, and cis and trans isoforms is to apply ion mobility spectrometry (IMS), a rapid and predictable separation device, in tandem with UHPLC HRMS/MS studies [110] Currently, these lipid isomers are rarely baseline resolved using IMS, but the resolving power of IMS is expected to increase with technological advances such as stru ctures for lossless ion manipulation (SLIM) [186] Because IMS is e asily combined with various liquid chromatographic and mass spectrometric techniques, it could revolutionize molecular characterization of lipids in the near future. In addition to achieving improved structural resolution of lipid annotations, databases mu st be designed to incorporate the various levels of structural resolution obtained by mass spectrometry. Otherwise, determination of the biological relevance of down re gulated and up regulated lipids cannot be accomplished using pathway mapping. This is du e to the fact that subtle differences in lipid structure can have dramatic influences on the lipid species biology. Currently, biochemical databases (e.g., Kyoto Encyclopedia of Genes and Genomes (KEGG) ) are unable to capture the varying lipid structural resolution conferred by mass spectrometry. Furthermore, i t is important to establish identifiers that query only biological information pertaining to known structural motifs. For example, the ideal case for PC(16:0_18:1) would be an identifier specific to the lipid class and to the fatty acyl

PAGE 165

165 constituents, but not to the sn 1 and sn 2 positions or the double bond position. While t here is a general KEGG entry for the phosphatidylcholine class, C00157, there is no KEGG entry for specific PCs. In this case, searching KEGG reduces the scope of biological inference to mechanisms general to all PCs. For the Human Metabolome Database (HMDB), identifiers exist for the specific lipid molecule, for example PC(16:0/18:1( 9Z)). However, these biological inferences can be too specific (i.e., based on sn 1 and sn 2 position) and thus lead to false interpretation of the data. It is important to note that currently, while specific lipid molecules exist in databases such as LIPID MAPS and HMDB, the curated pathways predominantly contain general lipid class biology. Therefore, current biological inference in lipidomics relies either on expert opinion or on lipid class and fatty acid profile based trends. Universal chemical identifie rs, which can convert a chemical structure into a machine readable string, and vice versa, such as the widely used International Chemical Identifier (InChI) [187] would be extremely useful for electronic record finding of mass spectrometric based lip id annotations. InChI consists of layers, each containing additional information about the molecular structure. Many of these layers can be omitted, hence allowing for some flexibility in structural resolution. For example, layers signifying cis versus tra ns double bonds, or chirality of the lipid molecule, can be omitted, in which case any lipid isomers will be found. However, current ly InChI requires a minimum of 3 layers, one o f which is absolute bond connectivity; therefore the position of a double bond and fatty acid on the backbone cannot be left undetermined limiting application to lipidomics Chemical query languages, such as SYBYL [188] could be used, wi th the possibilities of storing Boolean logic, wild cards (unknown atoms and R

PAGE 166

166 groups), and other functionalities allowing lipid annotations to be stored in a machine readable string which can cover all the different levels of structural resolution provid ed by mass spectrometry. These identifiers are not widely implemented; in order for them to be useful they should be implemented in annotation software, databases (such as LIPID MAPS), and by chemical manufacturers. In addition to accurate lipid annotatio ns, it is important to measure lipid concentrations reproducibly in order that measurements can be compared across laboratories. Therefore, LipidMatch Quant is introduced to automate the relative quantification process. Lipid quantification is problematic due to the cost prohibitive nature and una vailability of lipid standards to cover the diverse species within a given lipidome Therefore, strategies to select the best internal standards for each lipid class are employed, in what is termed relative quant ification. LipidMatch Quant can be employed to help take into account ion ization efficiencies and ion suppression effects ; L ipidMatch Quant accomplishes this by selecting internal standards based on lipid class, lipid adduct, and lipid retention time. Following selection of internal standards, a table is generated which include s the concentration of each lipid across each sample and the respective internal standard ion used for each lipid While these algorithms are currently of great utility to the li pidomics community, t hese algorithms are not intended as a final solution to a robust and comprehensive lip idomics workflow. Furthermore, because of the complexity of the lipidome, advances in instrumental and data processing strategies in lipidomics will continue to increase the coverage and accuracy of lipid measurements. Additionally community wide effort s to advance portions of lipidomics metabolomics, and proteomics workflow s will continue

PAGE 167

167 to p rovide valuable tools which can be integrated with or re place certain tools presented here By utilizing multiple user input parameters, the tools presented here can readily be integrated with other software outputs Therefore the workflows presented in this dissertation can utilize community accepted lipidomi cs software such as MZmine 2, XCMS, LipidSearch, MS DIAL, and Metaboanalyst The flexibility of the workflow also allows for integration with various data acquisition approaches and vendor formats For example LipidMatch has been successfully employed with Agilent, Thermo, and ScieX data files for imaging, liquid chromatography, and direct infusion based high resolution mass spectrometry. As with data processing strategies, there are numerous sample preparati on protocols employed in the lipidomics community Many factors in sample preparation reduce the accuracy and precision of lipid measurements, and yet are not fully understood. For example in this work it is shown that enzyme activity can drastically alte r the final lipid concentrations measured Furthermore it was determined that enzymatic transformations are highly dependent on lipid class, fatty acyl composition, the organism or substrate studied, and sample storage and preparation protocols. Therefore for lipidomics to be implemented in a clinical setting, one must account for these factors perturbing lipid measure ments However, m ost researchers do not sufficiently account for enzymatic degradation in their sample preparation. Adaptation of technolog ies which employ heat treatment, sample processing entirely at cryogenic temperatures, or other strategies for preserving sample integrity, are needed for robust and accurate lipid measurements

PAGE 168

168 M uch work is needed to further improve accuracy of lipid mea surements. Lipid concentrations measured across labs are often drastically different and annotations using even the most widely accepted software often contain false positives Therefore, in order to benchmark the progress of lipid measurements we need q uality controls, certified reference materials and inter laboratory exercises. As lipidomics continues to show promise in the clinical field, advancement in standardization and development of robust workflows will continue to meet the growing demand.

PAGE 169

169 APPENDIX SUPPLEMENTAL INFORMATION C hapter 2 Object 2 1. Additional supplemental information for Chapter 2 (Table S 2 4, Table S 2 5, and Table S 2 6 and all supplemental Figures and Tables in power point) ( .xlsx and .pptx files 9 KB) Table S 2 1 Gradient for reverse phase liquid chromatography of lipids. Mobile phase C consisted of 60:40 acetonitrile:water and mobile phase D consisted of 90:8:2 isopropanol:acetonitrile:water, with both contai ning 0.1% formic acid 10 mM ammonium formate. The flow rate was 500L/min. Time (min) 0 1 3 4 6 8 10 13 15 16 16.5 17 21 C (%) 80 80 70 55 40 35 35 20 20 10 10 80 80 D (%) 20 20 30 45 60 65 65 80 80 90 90 20 20 Table S 2 2 Mass spectrometric parameters. Abbreviations are: Res resolution, AGC automatic gain control, IT injection time, NCE normalized collision energy (stepped), ddMS 2 data dependent tandem mass spectrometry, Iso isolation width, Apex apex trigger and Dyn Excl dynamic exclusion. Res AGC IT (ms) Range (m/z) NCE Full Scan 70k 5x10 6 256 200 1200 NA ddMS 2 70k 5x10 6 256 80 900 20 5* Iso (m/z) Intensity Threshold Appex (s) Dyn Excl (s) top N ddMS 2 1 5x10 4 5 to 20 6 5 *For Red Cross plasma the stepped NCE was 20 4

PAGE 170

170 Table S 2 3 Source parameters (electrospray ionization (ESI)) Omics analysis Lipidomics Metabolomics Polarity Neg Pos Neg Pos Sheath gas flow rate 25 30 45 45 Auxilary gas flow rate 15 5 10 10 Sweep gas flow rate 0 1 1 1 Spray voltage (kV) 3.5 3.5 3 3.5 Capillary temperature (C) 250 300 325 325 S lens RF level 35 35 30 30 Aux gas heater temp (C) 350 300 350 350 Figure S 2 1 The retention time window for both determining the MS/MS scan under a chromatographic feature and for exclusion of ions previously selected, was 0.3 min, which was close to the median of FWHM values. Distribution of chromatographic peaks determined by MZmine full widths at half maximum (FWHM) for : A) Red Cross plasma in po sitive and negative polarity and B) Substantia nigra in po sitive and negative polarity

PAGE 171

171 Figure S 2 2 A higher density of selected precursor ions in substantia nigra extracts analyzed with IE was observed compared to the traditional ddMS 2 approach. Selected precursor ions m/z and retention times for 6 repetitive injections using : A) the traditional ddMS 2 approach and B) iterative based exclusion ddMS 2 (IE ddMS 2 ) for substantia nigra lipid extracts analyzed in positive mode.

PAGE 172

172 Figure S 2 3 Selected precursor ions m/z and retention times for : A) 6 repetitive injections using the traditional ddMS 2 approach and B) iterative based exclusion ddMS 2 (IE ddMS 2 ) for substantia nigra lipid extracts analyzed in positive mode, zoomed into the glycerophospholipi d (GPL) region. Identified lipid molecules by LipidMatch are also shown in red.

PAGE 173

173 Figure S 2 4 Cumulative unique lipid molecular identifications using LipidSearch software across multiple data acquisitions. Iterative exclusion based data dependent top5 (IE ddMS 2 top5) described in this paper is compared with with traditional ddMS 2 top5 for : A) lipi d extracts of Re d Cross plasma in positive mode and B) negative mode and C) lipid extracts of su bstantia nigra in positive mode and D) negative mode

PAGE 174

174 Figure S 2 5 Number of precursors selected for fragmentation across sequential injections after apply ing IE to negative and positive mode analysis of Red Cross plasma lipid extracts. Figure S 2 6 Boxplots of log transformed peak heights from MZmine for lipids identified in the first ddMS 2 top5 acquisition using LipidMatch (IE1) and after applying an ex clusion list using the algorithm described in this paper (IE2, IE3, IE4, IE5, and IE6). Comparisons are made for lipid extracts of Red Cross plasma in positive mode. Differences between IE1 and IE2, were highly significant with a p value for a student t te st less than 0.0000005. Differences in the following injections did not have significantly lower intensities (p value > 0.05).

PAGE 175

175 Figure S 2 7 Graphs are shown representing MS/MS spectral quality over sequential injections with and without applying IE. The dependent variable is the percent of lipids identified by LipidSearch which were graded A (high confidence and characterization of structural detail) over all grades (A + B + C). B and C grades indicate that there were less fragments identified for those lipids. Iterative exclusion based data dependent top5 (IE ddMS 2 top5) described in this paper is compared with traditional ddMS 2 top5 for lipid extracts of : A) Red Cross plasma lipid extracts in positive mode and B) negative mode and C) lipid extracts of su bstantia nigra in positive mode and D) negative mode

PAGE 176

176 Figure S 2 8 Boxplots of log transformed peak heights from MZmine for diglycerides (DGs) identified in the first ddMS 2 top5 acquisition using LipidMatch (IE1) and after applying an exclusion list using the algorithm described in this paper (IE2, IE3, IE4, IE5, and IE6). Comparisons are made for Red Cross plasma lipid extracts in positive mode.

PAGE 177

177 Figure S 9 Distribution of lipids identified using LipidMatch by lipid class using iterative exclusion based data dependent top5 (IE ddMS 2 top5) acquisitions in negative ion mode. The lipid class distribution of all identifications across sequential injections using the traditional ddMS 2 top5 approach is shown for : A) Red Cross plasma and B) substantia nigr a tissue lipid extracts In addition, the distribution of additional unique lipid molecular identifications after applying iterative exclusion (IE) across lipid classes are shown for : C) Red Cross plasma and D) sub stantia nigra lipid extracts

PAGE 178

178 Figure S 2 10 Multilinear regression for predicting retention times of diglycerides (DGs) based on DG total carbons and degrees of unsaturation in the fatty acid constituents. DGs fatty acid constituents were determined using tandem mass spectrometry and an in hou se identification software LipidMatch. Models explain the majority of the variance (97%) as expected. Therefore, the majority of LipidMatch identifications are verified by orthogonal separation, retention time, at least at the level of total carbons and do uble bonds. Figure S 2 11 Multilinear regression for predicting retention times of triglycerides (TGs) based on TG total carbons and degrees of unsaturation in the fatty acid constituents. TGs fatty acid constituents were determined using tandem mass sp ectrometry and an in house identification software LipidMatch. Models explain the majority of the variance (97%) as expected. Therefore, the majority of LipidMatch identifications are verified by orthogonal separation, retention time, at least at the level of total carbons and double bonds.

PAGE 179

179 C hapter 3 Object 3 1. Additional supplemental information for Chapter 3 (Table S 3 4 as .xlsx all supplemental Figures and Tables in power point and LipidMatch software, video tutorials, manual, and example files ) ( .zip, .xlsx and .pptx files 36 9 M B)

PAGE 180

180 Table S 3 1 LipidMatch lipids as of 10/1/2016 Class Species Adducts Ac2PIM1 78 [M H] Ac2PIM2 78 [M H] Ac3PIM2 1728 [M H] Ac4PIM2 20736 [M H] AcCa 53 [M+H] + BA_Glycine 16 [M H] BA 34 [M H] BA_Taurine 13 [M H] CE 38 [M+NH 4 ] + Cer_ADS 784 [ M+HCO 2 ] ; [M H] Cer_AP 2352 [ M+HCO 2 ] ; [M H] Cer_AS 1568 [ M+HCO 2 ] ; [M H] Cer_BDS 784 [ M+HCO 2 ] ; [M H] Cer_BS 2352 [ M+HCO 2 ] ; [M H] Cer_EODS 4368 [ M+HCO 2 ] ; [M H] Cer_EOS 8736 [ M+HCO 2 ] ; [M H] Cer 1444 [M+H] + ;[M+HCO2 ] Cer_NDS 784 [M+H] + ;[ M+HCO 2 ] ; [M H] Cer_NP 2352 [ M+HCO 2 ] ; [M H] CerP 168 [M+H] + ; [M H] CL 1596 [M 2H+3Na ] + ;[M 2H] 2 ; [M H] ; [M+NH 4 ] + CoQ 5 [M+NH 4 ] + DG 741 [M+NH 4 ] + DGDG 1176 [ M+HCO 2 ] ; [M+NH 4 ] + DGTS 741 [M+H] + DMPE 741 [M H] Ganglioside 1352 [M H] GlcADG 1176 [M H] ; [M+NH 4 ] + GlcCer_AP 2352 [M+FA H ] ; [M H] GlcCer_AS 1568 [M+FA H ] ; [M H] GlcCer_NDS 784 [M+H] + ;[M+FA H ] ; [M H] GlcCer_NP 2352 [M+FA H ] ; [M H] GlcCer 1568 [M+H] + ;[M+HCO 2 ] lanosteryl 2 [M+NH 4 ] + LDGTS 231 [M+H] + LipidA PP 425 [M H] ; [M H] LPC 38 [M+H] + ;[M+Na ] + ;[M+HCO 2 ] LPE 38 [M+H] + ;[M+Na ] + ; [M H] LPI 38 [M H] MG 49 [M+NH 4 ] + MGDG 2304 [M+H] + ;[M+Na ] + ;[M+HCO 2 ] ; [M+NH 4 ] + ; [M+NH 4 CO ] +

PAGE 181

181 Table S3 1. Continued Class Species Adducts MMPE 741 [M H] OxLPC 214 [M+H] + ;[M+Na ] + ;[M+HCO 2 ] OxLPE 214 [M+H] + ;[M+Na ] + ; [M H] OxPC 31112 [M+H] + ;[M+Na ] + ;[M+HCO 2 ] OxPE 31112 [M+H] + ;[M+Na ] + ; [M H] OxTG 115520 [M+NH 4 ] + ; [M+NH 4 ] + PA 741 [M+H] + ; [M H] PC 741 [M+H] + ; [M+H] + ;M+Na;[M+HCO 2 ] PE 741 [M+H] + ;[M+Na ] + ; [M H] PG 741 [M+H] + ; [M H] ; [M+NH 4 ] + PI 741 [M H] Plasmanyl LPC 11 [M+H] + ;[M+HCO 2 ] Plasmanyl LPE 3 [M+H] + ; [M H] Plasmanyl PC 418 [M+H] + ;[M+HCO 2 ] Plasmanyl PE 114 [M+H] + ; [M H] Plasmanyl PS 114 [M+H] + ; [M H] Plasmanyl TG 4446 [M+NH 4 ]; [M+NH 4 ] Plasmenyl LPC 11 [M+H] + ;[M+HCO 2 ] Plasmenyl LPE 11 [M+H] + ; [M H] Plasmenyl PC 418 M+H;[M+HCO 2 ] Plasmenyl PE 228 [M+H] + ; [M H] Plasmenyl PS 228 [M+H] + ; [M H] Plasmenyl TG 4446 [M+NH 4 ]; [M+NH 4 ] PS 741 [M+H] + ;[M+Na ] + ; [M H] SM 1444 [M+H] + ;[M+HCO 2 ] So 13 [M+H] + SQDG 1473 [M H] Sulfatide 196 [M+H] + ; [M H] TG 9880 [M+Na ] + ; [M+NH 4 ] + ; [M+NH 4 ] + zymosteryl 2 [M+NH 4 ] + 71 274558 136

PAGE 182

182 Table S 3 2 LipidMatch lipid acronyms as of 10/1/2016 Acronym Definition Ac2PIM1 Diacylated phosphatidylinositol monomannoside Ac2PIM2 Diacylated phosphatidylinositol dimannoside Ac3PIM2 Triacylated phosphatidylinositol dimannoside Ac4PIM2 Tetraacylated phosphatidylinositol dimannoside AcCa Acylcarnitine BA Bial Acid: Beta BA_Glycine Bial Acid Glycine Conjugate BA_Taurine Bial Acid Taurine Conjugate CE Cholesterol Ester Cer_NDS Backbone: Dihydrosphingosine Fatty acid: non hydroxy fatty acid Cer_NP Backbone: Phytosphingosine Fatty acid: Fatty acid: non hydroxy fatty acid Cer Ceramide (Nonhydroxyacyl sphingosine) Cer_ADS Backbone: Dihydrosphingosine Fatty acid: a hydroxy fatty acid Cer_AP Backbone: Phytosphingosine Fatty acid: a hydroxy fatty acid Cer_AS Backbone: Sphingosine Fatty acid: a hydroxy fatty acid Cer_BDS C eramide Backbone: Dihydrosphingosine Cer_BS C eramide Backbone: Sphingosine Cer_BS C eramide Backbone: Sphingosine Cer_EODS Backbone: Dihydrosphingosine Fatty acid: esterified w hydroxy fatty acid Cer_EOS Backbone: Sphingosine Fatty acid: esterified w hy droxy fatty acid CerP Ceramide 1 phosphate CL Cardiolipin CoQ Coenzyme Q DG D iglyceride or D iglyceride DGDG Digalactosyld iglyceride DGTS Diacylglyceryltrimethylhomo Ser DMPE Dimethyl Phosphatidylethanolamine Ganglioside Ganglioside or glycan ceramides GlcADG Glucuronosyld iglyceride GlcCer_NDS G lucosyl ceramide Backbone: Dihydrosphingosine Fatty acid: non hydroxy fatty acid GlcCer Glucosyl Nonhydroxyacyl sphingosine GlcCer_AP G lucosyl ceramide Backbone: Phytosphingosine Fatty acid: a hydro xy fatty acid GlcCer_AS G lucosyl ceramide Backbone: Sphingosine Fatty acid: a hydroxy fatty acid lanosteryl L anosteryl LDGTS Lysodiacylglyceryltrimethylhomo Ser LipidA_PP Diphosphorylated hexaacyl Lipid A LPC Lysophosphatidylcholine LPE Lysophosphatidylethanolamine LPI Lysophosphatidylinositol

PAGE 183

183 Table S3 2. Continued Acronym Definition MG Monoacylglycerol MGDG Monogalactosyld iglyceride MMPE Monomethyl Phosphatidylethanolamine OxLPC Oxidized lysophosphatidylcholine OxLPE Oxidized lysophosphatidylethanolamine OxPC Oxidized phosphatidylcholine OxPE Oxidized phosphatidylethanolamine OxTG Oxidized tr iglyceride PA Phosphatidic acid PC Phosphatidylcholine PE Phosphatidylethanolamine PG Phosphatidylglycerol PI Phosphatidylinositol Plasmanyl LPC Plasmanyl lysophosphatidylcholine Plasmanyl LPE Plasmanyl lysophosphatidylethanolamine Plasmanyl PC Plasmanyl phosphatidylcholine Plasmanyl PE Plasmanyl phosphatidylethanolamine Plasmanyl PS Plasmanyl phosphatidylserine Plasmanyl TG Plasmanyl tr iglyceride Plasmenyl LPC Plasmenyl lysophosphatidylcholine Plasmenyl LPE Plasmenyl lysophosphatidylethanolamine Plasmenyl PC Plasmenyl phosphatidylcholine Plasmenyl PE Plasmenyl phosphatidylethanolamine Plasmenyl PS Plasmenyl phosphatidylserine Plasmenyl TG Plasmenyl tr iglyceride PS Phosphatidylserine SM Sphingomyelin So Sphingosine SQDG Sulfoquinovosyld iglyceride Sulfatide Sulfatide TG T r iglyceride zymosteryl zymosteryl

PAGE 184

184 Figure S 3 1. Simplified flow diagram of LipidMatch operations. The first panel is input files, the second represents operations performed by LipidMatch, and the third panel illustrates procedures using data from red cross plasma. For the first panel, green boxes with folded top right corners are input csv files. Purple boxes with diagonal tops are input parameters. The third panel uses identification of PC(38:6) [M+HCO 2 ] in negative polarity as an example.

PAGE 185

185 Figure S 3 2 Set overlap for LipidMatch, MS DIAL, and GREAZY in negative polarity analy sis of Red Cross plasma. Visualization of sets based on UpSet . Dots and lines represent which software (sets) overlap, and bars represent the total lipid species contained within each overlap. For example the first ver tical bar represents the number of features with the same identification for all 3 software. Color codes show the lipid types making up a specific overlap or set. Sets or sorted by number of lipid species contained within. Species included in other were PI P, PIP2, and GlcCer. Horizontal bars represent the total number of feature identified by each respective software.

PAGE 186

186 Figure S 3 3 Set overlap for LipidMatch, MS DIAL, and GREAZY in positive polarity analysis of Red Cross plasma. Dots and lines represent w hich software (sets) overlap, and bars represent the total lipid species contained within each overlap. Color codes show the lipid types making up a specific overlap or set. Species included in other were ether linked LPC, Co, So, Sulfatide, PIP3, PE Cer, PG, PA, PS, ether linked PS, PE, CE, and LPE, which all had less than 15 lipids in any given overlap between sets.

PAGE 187

187 Figure S 3 4 Pie chart of lipid classes and the number of each identified by LipidMatch in negative polarity Figure S 3 5 Pie chart of lipid classes and the number of each identified by LipidMatch in positive polarity C hapter 4 Table S 4 1 Liquid c hromatography g radient Time (min) 0 1 3 4 6 8 10 15 17 18 19 23 C (%) 80 80 70 55 40 35 35 10 2 2 80 80 D (%) 20 20 30 45 60 65 65 90 98 98 20 20

PAGE 188

188 Table S4 2 Mass s pectrometry s can p arameters Parameter Full Scan AIF scan Targeted MS 2 Resolution 70,000 70,000 17,500 AGC 3 x 10 6 3 x 10 6 3 x 10 6 Injection time (ms) 256 256 256 Range ( m/z ) 120 1,200 80 1,200 automatic NCE NA 25 25 Isolation window ( m/z ) NA 1120 1

PAGE 189

189 Table S4 3 Comparisons of LMQ derived concentrations using various data processing techniques and ions for quantification Concentrations calculated after smoothing versus not smoothing peak heights a) Species Both* Avg % Diff** Med % Diff*** log 2 fold change **** Cer 11 5 6 2.6 0 0.1 LPC 15 6 5 5.4 0.1 0.1 OxTG 4 2 1 2.6 0 0 PC 37 8 6 7.3 0.1 0.1 PE 14 9 7 8.1 0.1 0.1 SM 3 9 3 7.7 0 0.2 TG 59 4 3 3.5 0 0.1 ether LPC 5 11 9 14 0.2 0.1 ether PC 15 4 3 3.9 0.1 0 ether TG 15 4 3 4.4 0.1 0.1 ether PE 6 6 2 6.9 0.1 0 Concentrations calculated after smoothing versus not smoothing peak areas b) Cer 11 2 2 1.2 0 0.1 LPC 15 1 1 0.3 0.1 0.1 OxTG 4 3 2 2.6 0 0 PC 37 1 1 0.2 0.1 0.1 PE 14 2 2 1 0.1 0.1 SM 3 1 1 0.3 0 0.2 TG 59 2 2 1 0 0.1 ether LPC 5 0 0 0.6 0.2 0.1 ether PC 15 1 1 0.3 0.1 0 ether TG 15 3 2 2.8 0.1 0.1 ether PE 6 1 1 0.4 0.1 0

PAGE 190

190 Table S4 3. Continued Concentrations calculated using peak height versus peak area c) Cer 11 25 32 26 0.2 0.3 LPC 15 24 18 13 0.1 0.3 OxTG 4 78 25 29 0.3 0.2 PC 37 28 19 18 0.1 0.3 PE 14 28 24 32 0.1 0.5 SM 3 45 31 54 0.5 0.3 TG 59 53 44 42 0.5 0.4 ether LPC 5 31 18 40 0.3 0.4 ether PC 15 17 13 28 0.3 0.4 ether TG 15 16 9 18 0.3 0.3 ether PE 6 16 15 15 0.2 0.2 Concentrations calculated using positive versus negative polarity data d) Cer 7 53 23 31 0.6 0.3 ether PC 4 41 7.4 40 0.5 0.1 ether PE 4 33 30 22 0.3 0.4 LPC 14 43 18 41 0.5 0.2 PC 22 27 24 20 0.2 0.4 Concentrations calculated using major versus [M+Na ] + adducts e) Cer 7 570 210 180 3 0 LPC 5 220 140 220 1.6 0.6 PC 26 180 320 120 1 1 TG 38 4300 11300 260 1 4

PAGE 191

191 A B Figure S 4 1 A comparison of fold change (greater than 1) versus percent difference for the traditional equation for percent difference and the one used here A ) Fold change (greater than 1) versus percent difference calculated using the average in the formula: B ) Fold change (greater than 1) versus percent difference calculated using the minimum in the formula:

PAGE 192

192 Figure S 4 2 Depiction of MS/MS scans using AIF (red and yellow) and using DDA (white) for two lipid isomers (A and B). An advantage of AIF over data dependent MS/MS for annotation is that via deconvolution, the correct peak can be assigned to the correct lipid (Figur e S 4 1 A ). In DDA in in Figure S 4 1 A if only one MS/MS scan is obtained, there is not enough information to correctly assign which lipid isomer belongs to which peak. If both isomers completely overlap, AIF cannot be used to distinguish the isomers.

PAGE 193

193 Figure S 4 3 Extracted ion chromatograms (EICs) and peak integration by MZmine of the triglycerides (TGs) with : A) the most and B) least percent difference when comparing q uantification using peak height versus peak area.

PAGE 194

194 Figure S 4 4 Extracted ion chromatograms (EICs) of PC(16:0_20:5) and PC(18:0_20:4) (the predominant peak at 7.16 and 8.34, respectively). Peaks had similar looking EICs in negative and positive mode, but very different percent differences in concentration between the two polarities, although the same molecular lip id was used for quantification. C hapter 5 Object 5 1. Additional supplemental information for Chapter 5 (Table S 5 3 through Table S5 7 as .xlsx and all suppl emental Figures and Tables in power point and LipidMatch software, 11.2 M B) Table S 5 1 U ltra high performance liquid chromatography (U H PLC) gradient Retention Time (min) 0 1 3 4 6 8 10 15 17 18 19 23 % A (60:40 ACN:H 2 O) 80 80 70 55 40 35 35 10 2 2 80 80 % B (90:8:2 IPA:ACN:H 2 O) 20 20 30 45 60 65 65 90 98 98 20 20

PAGE 195

195 Table S 5 2 Q Exactive scanning parameters Res AGC IT (ms) Range (m/z) NCE Full Scan 70k 5x10 6 256 200 1200 NA ddMS2 35k 5x10 6 175 80 1200 25 5 Iso (m/z) Underfill (%) Appex (s) Dyn Excl (s) top N ddMS2 1 1 10 20 4 10

PAGE 196

196 Figure S 5 1 Denator Maintainor cartridges containing: A) common house crickets before and B) after heat treatment, C) earthworm after heat treatment and D) ghost shrimp after heat treatment.

PAGE 197

197 Figure S 5 2 Relative concentrations and numbers of species of each lipid type in earthworm, ghost shrimp, and common house cricket. Lipid class/type acronyms and definitions are provided in Supplementary Table S 6. Ether stands for either plasmanyl or plasmenyl sp ecies, or both. The panels are: A) Concentration of each lipid class for earthworm, B) ghost shrimp, and C) common house cricket, and D) number of lipid species per class for earthworm, E) ghost shrimp, and F) common house cricket.

PAGE 198

198 Figure S 5 3 Total P E and LPE [M H] and PC and LPC [M+HCO 2 ] concentrations in cricket, earthworm, and shrimp samples that are flash frozen (N) and with heat treatment (D), including samples that were incubated for one hour on ice. Three significance levels are: p < 0.05 (*) p < 0.005 (**), and p < 0.0005 (***).

PAGE 199

199 Figure S 5 4 Total ether linked (plasmanyl and plasmenyl species) PE and LPE [M H] and PC and LPC [M+HCO 2 ] concentrations in frozen (N) and heat treated (D). The three significance levels are: p < 0.05 (*), p < 0 .005 (**), and p < 0.0005 (***).

PAGE 200

200 LIST OF REFERENCES 1. data sets. Nat. Rev. Mol. Cell Biol. 7, 198 210 (2006). doi:10.1038/nrm1857 2. Kaddurah Daouk, R., Kristal, B.S., Weinshilboum, R.M.: Metabolomics: A Global Biochemical Approach to Drug Response and Disease. Annu. Rev. Pharmacol. Toxicol. 48, 653 683 (2008). doi:10.1146/annurev.pharmtox.48.113006.094715 3. Va n Assche, R., Broeckx, V., Boonen, K., Maes, E., De Haes, W., Schoofs, L., Temmerman, L.: Integrating Omics: Systems Biology as Explored Through C. elegans Research. J. Mol. Biol. doi:10.1016/j.jmb.2015.03.015 4. van Meer, G., Voelker, D.R., Feigenson, G .W.: Membrane lipids: where they are and how they behave. Nat. Rev. Mol. Cell Biol. 9, 112 124 (2008). doi:10.1038/nrm2330 5. Pablo, M.A. de, Puertollano, M.A., Cienfuegos, G.. de: Biological and Clinical Significance of Lipids as Modulators of Immune Sy stem Functions. Clin. Diagn. Lab. Immunol. 9, 945 950 (2002). doi:10.1128/CDLI.9.5.945 950.2002 6. Cooper, G.M.: The Cell: A Molecular Approach. Sinauer Associates, Sunderland, MA, Sunderland, MA (2000) 7. Edwards, P.A., Ericsson, J.: Sterols and Isopren oids: Signaling Molecules Derived from the Cholesterol Biosynthetic Pathway. Annu. Rev. Biochem. 68, 157 185 (1999). doi:10.1146/annurev.biochem.68.1.157 8. Martin, T.F.J.: Phosphoinositide lipids as signaling molecules: Common Themes for Signal Transduct ion, Cytoskeletal Regulation, and Membrane Trafficking. Annu. Rev. Cell Dev. Biol. 14, 231 264 (1998). doi:10.1146/annurev.cellbio.14.1.231 9. Simons, K., Toomre, D.: Lipid rafts and signal transduction. Nat. Rev. Mol. Cell Biol. 1, 31 39 (2000). doi:10.1 038/35036052 10. Sies, H.: Oxidative stress: oxidants and antioxidants. Exp. Physiol. 82, 291 295 (1997) 11. Veldhuizen, R., Nag, K., Orgeig, S., Possmayer, F.: The role of lipids in pulmonary surfactant. Biochim. Biophys. Acta BBA Mol. Basis Dis. 1408 90 108 (1998). doi:10.1016/S0925 4439(98)00061 1 12. Imokawa, G., Kuno, H., Kawai, M.: Stratum Corneum Lipids Serve as a Bound Water Modulator. J. Invest. Dermatol. 96, 845 851 (1990). doi:10.1111/1523 1747.ep12474562

PAGE 201

201 13. Bron, A.J., Tiffany, J.M., Gou veia, S.M., Yokoi, N., Voon, L.W.: Functional aspects of the tear film lipid layer. Exp. Eye Res. 78, 347 360 (2004). doi:10.1016/j.exer.2003.09.019 14. Ridker, P., Rifai, N., Cook, N.R., Bradwin, G., Buring, J.E.: NOn hdl cholesterol, apolipoproteins a i and b100, standard lipid measures, lipid ratios, and crp as risk factors for cardiovascular disease in women. JAMA. 294, 326 333 (2005). doi:10.1001/jama.294.3.326 15. Oberg, B.P., Mcmenamin, E., Lucas, F.L., Mcmonagle, E., Morrow, J., Ikizler, T.A., Him melfarb, J.: Increased prevalence of oxidant stress and inflammation in patients with moderate to severe chronic kidney disease. Kidney Int. 65, 1009 1016 (2004). doi:10.1111/j.1523 1755.2004.00465.x 16. Fernandis, A.Z., Wenk, M.R.: Lipid based biomarkers for cancer. J. Chromatogr. B. 877, 2830 2835 (2009). doi:10.1016/j.jchromb.2009.06.015 17. Dalle Donne, I., Rossi, R., Colombo, R., Giustarini, D., Milzani, A.: Biomarkers of Oxidative Damage in Human Disease. Clin. Chem. 52, 601 623 (2006). doi:10.1373/ clinchem.2005.061408 18. Dowhan, W.: Molecular Basis for Membrane Phospholipid Diversity: Why Are There So Many Lipids? Annu. Rev. Biochem. 66, 199 232 (1997). doi:10.1146/annurev.biochem.66.1.199 19. Watanabe, K., Yasugi, E., Oshima, M.: How to Search t Glycosci. Glycotechnol. 12, 175 184 (2000). doi:10.4052/tigg.12.175 20. Dove, A.: Greasing the Wheels of Lipidomics, s/lst_20150213.xhtml, (2015) 21. Schmelzer, K., Fahy, E., Subramaniam, S., Dennis, E.A.: The Lipid Maps Initiative in Lipidomics. Elsevier, Amsterdam, Netherlands (2007) 22. Spener, F.: European Commission funds lipidomics project. Eur. J. Lipid Sci. Tec hnol. 107, 1 2 (2005). doi:10.1002/ejlt.200590000 23. Sud, M., Fahy, E., Cotter, D., Brown, A., Dennis, E.A., Glass, C.K., Merrill, A.H., Murphy, R.C., Raetz, C.R.H., Russell, D.W., Subramaniam, S.: LMSD: LIPID MAPS structure database. Nucleic Acids Res. 35, D527 532 (2007). doi:10.1093/nar/gkl838 24. Sud, M., Fahy, E., Subramaniam, S.: Template based combinatorial enumeration of virtual compound libraries for lipids. J. Cheminformatics. 4, 23 (2012). doi:10.1186/1758 2946 4 23

PAGE 202

202 25. Yetukuri, L., Ekroos, K., Vidal strategies for the study of lipids. Mol. Biosyst. 4, 121 127 (2008). doi:10.1039/B715468B 26. van Meer, G.: Cellular lipidomics. EMBO J. 24, 3159 3165 (2005). doi:10.1038/sj.emboj.7600798 27. Wenk, M.R.: The emerging field of lipidomics. Nat. Rev. Drug Discov. 4, 594 610 (2005). doi:10.1038/nrd1776 28. Shevchenko, A., Simons, K.: Lipidomics: coming to grips with lipid diversity. Nat. Rev. Mol. Cell Biol. 11, 593 598 (2010). doi:10.1038/nrm2934 29. Patterson, N.H., Thomas, A., Chaurand, P.: Monitoring time dependent degradation of phospholipids in sectioned tissues by MALDI imaging mass spectrometry. J. Mass Spectrom. 49, 622 627 (2014). doi:10.1002/jms.3382 30. Wang, X., Gu, X., Song, H., Song, Q., Gao, X., Lu, Y., Chen, H.: Phenylmethanesulfonyl fluoride pretreatment stabilizes plasma lipidome in lipidomic and metabolomic analysis. Anal. Chim. Acta. 893, 77 83 (2015). doi:10.1016/j.aca.2015.08.049 31. Jentzsch, A.M., Bachmann, H., Frst, P., Biesalski, H.K.: Improved analysis of malondialdehyde in human body fluids. Free Radic. Biol. Med. 20, 251 256 (1996). doi:10.1016/0891 5849(95)02043 8 32. Stumpf, P.K.: Lipids: Structure and Function: The Biochemistry of Plants. Elsevier, Amsterdam, Netherlands (2014) 33. Zhao, Z., Xu, Y.: An extremely simple method for extraction of lysophospholipids and phospholipids from blood samples. J. Lipid Res. 51, 652 659 (2010). doi:10.1194/jlr.D001503 34. Roughan, P.G., Slack, C.R., Holland, R.: Generatio n of phospholipid artefacts during extraction of developing soybean seeds with methanolic solvents. Lipids. 13, 497 503. doi:10.1007/BF02533620 35. Manirakiza, P., Covaci, A., Schepens, P.: Comparative Study on Total Lipid Determination using Soxhlet, Roe se Gottlieb, Bligh & Dyer, and Modified Bligh & Dyer Extraction Methods. J. Food Compos. Anal. 14, 93 100 (2001). doi:10.1006/jfca.2000.0972 36. Lee, J. Y., Yoo, C., Jun, S. Y., Ahn, C. Y., Oh, H. M.: Comparison of several methods for effective lipid extr action from microalgae. Bioresour. Technol. 101, S75 S77 (2010). doi:10.1016/j.biortech.2009.03.058

PAGE 203

203 37. Patterson, R.E., Ducrocq, A.J., McDougall, D.J., Garrett, T.J., Yost, R.A.: Comparison of blood plasma sample preparation methods for combined LC MS l ipidomics and metabolomics. J. Chromatogr. B. 1002, 260 266 (2015). doi:10.1016/j.jchromb.2015.08.018 38. Bligh, E.G., Dyer, W.J.: A Rapid Method of Total Lipid Extraction and Purification. Can. J. Biochem. Physiol. 37, 911 917 (1959). doi:10.1139/o59 099 39. Folch, J., Lees, M., Sloane Stanley, G.H.: A simple method for the isolation and purification of total lipides from animal tissues. J. Biol. Chem. 226, 497 509 (1957) 40. Holland, W.L., Stauter, E.C., Stith, B.J.: Quantification of phosphatidic acid and lysophosphatidic acid by HPLC with evaporative light scattering detection. J. Lipid Res. 44, 854 858 (2003). doi:10.1194/jlr.D200040 JLR200 41. Bromke, M.A., Hochmuth, A., Tohge, T., Fernie, A.R., Giavalisco, P., Burgos, A., Willmitzer, L., Brotman, Y.: Liquid chromatography high resolution mass spectrometry for fatty acid profiling. Plant J. Cell Mol. Biol. 81, 529 536 (2015). doi:10.1111/tpj.12739 42. Han, X., Gross, R.W.: Global analyses of cellular lipidomes directly from crude extracts of biolog ical samples by ESI mass spectrometry a bridge to lipidomics. J. Lipid Res. 44, 1071 1079 (2003). doi:10.1194/jlr.R300004 JLR200 43. Houjou, T., Yamatani, K., Imagawa, M., Shimizu, T., Taguchi, R.: A shotgun tandem mass spectrometric analysis of phospholi pids with normal phase and/or reverse phase liquid chromatography/electrospray ionization mass spectrometry. Rapid Commun. Mass Spectrom. RCM. 19, 654 666 (2005). doi:10.1002/rcm.1836 44. Okazaki, Y., Kamide, Y., Hirai, M.Y., Saito, K.: Plant lipidomics b ased on hydrophilic interaction chromatography coupled to ion trap time of flight mass spectrometry. Metabolomics. 9, 121 131 (2013). doi:10.1007/s11306 011 0318 z 45. Hines, K.M., Herron, J., Xu, L.: Assessment of Altered Lipid Homeostasis by HILIC Ion M obility Mass Spectrometry Based Lipidomics. J. Lipid Res. jlr.D074724 (2017). doi:10.1194/jlr.D074724 46. Han, X., Gross, R.W.: Shotgun lipidomics: Electrospray ionization mass spectrometric analysis and quantitation of cellular lipidomes directly from cr ude extracts of biological samples. Mass Spectrom. Rev. 24, 367 412 (2005). doi:10.1002/mas.20023 47. Murphy, R.C., Fiedler, J., Hevko, J.: Analysis of nonvolatile lipids by mass spectrometry. Chem. Rev. 101, 479 526 (2001). doi:10.1021/cr9900883

PAGE 204

204 48. Ca i, S. S., Syage, J.A.: Comparison of Atmospheric Pressure Photoionization, Atmospheric Pressure Chemical Ionization, and Electrospray Ionization Mass Spectrometry for Analysis of Lipids. Anal. Chem. 78, 1191 1199 (2006). doi:10.1021/ac0515834 49. Leinonen A., Kuuranne, T., Kostiainen, R.: Liquid chromatography/mass spectrometry in anabolic steroid analysis optimization and comparison of three ionization techniques: electrospray ionization, atmospheric pressure chemical ionization and atmospheric pressure photoionization. J. Mass Spectrom. 37, 693 698 (2002). doi:10.1002/jms.328 50. Cech, N.B., Enke, C.G.: Practical implications of some recent studies in electrospray ionization fundamentals. Mass Spectrom. Rev. 20, 362 387 (2001). doi:10.1002/mas.10008 51. Fernandez de la Mora, J.: Electrospray ionization of large multiply charged species 104 (2000). doi:10.1016/S0003 2670(99)00601 7 52. Konermann, L., Ahadi, E., Rodriguez, A.D., Vah idi, S.: Unraveling the Mechanism of Electrospray Ionization. Anal. Chem. 85, 2 9 (2013). doi:10.1021/ac302789c 53. Fernandez de la Mora, J.: Electrospray ionization of large multiply charged species im. Acta. 406, 93 104 (2000). doi:10.1016/S0003 2670(99)00601 7 54. Beveridge, R., Phillips, A.S., Denbigh, L., Saleem, H.M., MacPhee, C.E., Barran, P.E.: Relating gas phase to solution conformations: Lessons from disordered proteins. Proteomics. 15, 2872 2883 (2015). doi:10.1002/pmic.201400605 55. Cech, N.B., Enke, C.G.: Practical implications of some recent studies in electrospray ionization fundamentals. Mass Spectrom. Rev. 20, 362 387 (2001). doi:10.1002/mas.10008 56. Yang, K., Han, X.: Accurate Quantification of Lipid Species by Electrospray Ionization Mass Spectrometry Meets a Key Challenge in Lipidomics. Metabolites. 1, 21 40 (2011). doi:10.3390/metabo1010021 57. Han, X., Yang, K., Gross, R.W.: Multi dimensional mass spectrometry based shotg un lipidomics and novel strategies for lipidomic analyses. Mass Spectrom. Rev. 31, 134 178 (2012). doi:10.1002/mas.20342 58. Jebanathirajah, J., Lin, C., Moyer, S., Zhao, C.: A n ew hybrid electrospray Fourier transform mass spectrometer: design and performance characteristics. Rapid Commun. Mass Spectrom. 20, 259 266 (2006). doi:10.1002/rcm.2307

PAGE 205

205 59. Makarov, A., Denisov, E., Kholomeev, A., Balschun, W., Lange, O., Strupat, K., Ho rning, S.: Performance Evaluation of a Hybrid Linear Ion Trap/Orbitrap Mass Spectrometer. Anal. Chem. 78, 2113 2120 (2006). doi:10.1021/ac0518811 60. Schwudke, D., Hannich, J.T., Surendranath, V., Grimard, V., Moehring, T., Burton, L., Kurzchalia, T., She vchenko, A.: Top Down Lipidomic Screens by Multivariate Analysis of High Resolution Survey Mass Spectra. Anal. Chem. 79, 4083 4093 (2007). doi:10.1021/ac062455y 61. Olsen, J.V., Godoy, L.M.F. de, Li, G., Macek, B., Mortensen, P., Pesch, R., Makarov, A., L ange, O., Horning, S., Mann, M.: Parts per Million Mass Accuracy on an Orbitrap Mass Spectrometer via Lock Mass Injection into a C trap. Mol. Cell. Proteomics. 4, 2010 2021 (2005). doi:10.1074/mcp.T500030 MCP200 62. Schwudke, D., Schuhmann, K., Herzog, R. Bornstein, S.R., Shevchenko, A.: Shotgun Lipidomics on High Resolution Mass Spectrometers. Cold Spring Harb. Perspect. Biol. 3, (2011). doi:10.1101/cshperspect.a004614 63. Biocompare: Talking About a Revolution: FT ICR Mass Spectrometry Offers High Reso lution and Mass Accuracy for Pr, Articles/41589 Talking About a Revolution FT ICR Mass Spectrometry Offers High Resolution and Mass Accuracy for Pr/ 64. Ahlf, D.R., Compton, P.D., Tran, J.C., Early, B.P., Thomas, P.M., Kelleher, N.L.: Evaluation of the Compact High Field Orbitrap for Top Down Proteomics of Human Cells. J. Proteome Res. 11, 4308 4314 (2012). doi:10.1021/pr3004216 65. Hardman, M., Makarov, A.A.: Interfacing the Orbitrap Mass Analyzer to an Electrospray Io n Source. Anal. Chem. 75, 1699 1705 (2003). doi:10.1021/ac0258047 66. Gorshkov, M.V., Fornelli, L., Tsybin, Y.O.: Observation of ion coalescence in Orbitrap Fourier transform mass spectrometry. Rapid Commun. Mass Spectrom. 26, 1711 1717 (2012). doi:10.100 2/rcm.6289 67. Lange, O., Damoc, E., Wieghaus, A., Makarov, A.: Enhanced Fourier transform for Orbitrap mass spectrometry. Int. J. Mass Spectrom. 369, 16 22 (2014). doi:10.1016/j.ijms.2014.05.019 68. Hu, Q., Noll, R.J., Li, H., Makarov, A., Hardman, M., Cooks, G.R.: The Orbitrap: a new mass spectrometer. J. Mass Spectrom. JMS. 40, 430 443 (2005). doi:10.1002/jms.856 69. Performance Technique of Mass Analysis. Anal. Chem. 72, 1156 1162 (2000). doi:10.1021/ac991131p

PAGE 206

206 70. Denisov, E., Damoc, E., Lange, O., Makarov, A.: Orbitrap mass spectrometry with resolving powers above 1,000,000. Int. J. Mass Spectrom. 325 327, 80 85 (2012). doi:10.1016/j.ijms.2012.06.009 71. Michalski, A., Damoc, E., Hauschild, J. P., Lange, O., Wieghaus, A., Makarov, A., Nagaraj, N., Cox, J., Mann, M., Horning, S.: Mass Spectrometry based Proteomics Using Q Exactive, a High performance Benchtop Quadrupole Orbitrap Mass Spectrometer. Mol. Cell. Proteomics. 10, M111.01 1015 (2011). doi:10.1074/mcp.M111.011015 72. Olsen, J.V., Macek, B., Lange, O., Makarov, A., Horning, S., Mann, M.: Higher energy C trap dissociation for peptide modification analysis. Nat. Methods. 4, 709 712 (2007). doi:10.1038/nmeth1060 73. Olsen, J.V ., Macek, B., Lange, O., Makarov, A., Horning, S., Mann, M.: Higher energy C trap dissociation for peptide modification analysis. Nat. Methods. 4, 709 712 (2007). doi:10.1038/nmeth1060 74. Kim, D. H., Achcar, F., Breitling, R., Burgess, K.E., Barrett, M.P .: LC MS based absolute metabolite quantification: application to metabolic flux measurement in trypanosomes. Metabolomics. 11, 1721 1732 (2015). doi:10.1007/s11306 015 0827 2 75. Ivanova, P.T., Milne, S.B., Myers, D.S., Brown, H.A.: Lipidomics: a mass sp ectrometry based systems level analysis of cellular lipids. Curr. Opin. Chem. Biol. 13, 526 531 (2009). doi:10.1016/j.cbpa.2009.08.011 76. Kelley, N.S., Hubbard, N.E., Erickson, K.L.: Conjugated Linoleic Acid Isomers and Cancer. J. Nutr. 137, 2599 2607 (2 007) 77. Tricon, S., Burdge, G.C., Kew, S., Banerjee, T., Russell, J.J., Jones, E.L., Grimble, R.F., Williams, C.M., Yaqoob, P., Calder, P.C.: Opposing effects of cis 9,trans 11 and trans 10,cis 12 conjugated linoleic acid on blood lipids in healthy human s. Am. J. Clin. Nutr. 80, 614 620 (2004) 78. Churruca, I., Fernndez Quintela, A., Portillo, M.P.: Conjugated linoleic acid isomers: Differences in metabolism and biological effects. BioFactors. 35, 105 111 (2009). doi:10.1002/biof.13 79. Shinzawa Itoh, K., Aoyama, H., Muramoto, K., Terada, H., Kurauchi, T., Tadehara, Y., Yamasaki, A., Sugimura, T., Kurono, S., Tsujimoto, K., Mizushima, T., Yamashita, E., Tsukihara, T., Yoshikawa, S.: Structures and physiological roles of 13 integral lipids of bovine hear t cytochrome c oxidase. EMBO J. 26, 1713 1725 (2007). doi:10.1038/sj.emboj.7601618

PAGE 207

207 80. Kanehisa, M., Goto, S., Hattori, M., Aoki Kinoshita, K.F., Itoh, M., Kawashima, S., Katayama, T., Araki, M., Hirakawa, M.: From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 34, D354 D357 (2006). doi:10.1093/nar/gkj102 81. Fahy, E., Subramaniam, S., Murphy, R.C., Nishijima, M., Raetz, C.R.H., Shimizu, T., Spener, F., van Meer, G., Wakelam, M.J.O., Dennis, E.A.: Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. 50, S9 S14 (2009). doi:10.1194/jlr.R800095 JLR200 82. Knittelfelder, O.L., Weberhofer, B.P., Eichmann, T.O., Kohlwein, S.D., Rechberger, G.N.: A versatile ultra high performance LC MS method for lipid profiling. J. Chromatogr. B. 951 952, 119 128 (2014). doi:10.1016/j.jchromb.2014.01.011 83. Andrews, G.L., Dean, R.A., Hawkridge, A.M., Muddiman, D.C.: Improving Proteome Coverage on a LTQ Orbitrap Using Design of Experiments. J. Am. Soc. Mass S pectrom. 22, 773 783 (2011). doi:10.1007/s13361 011 0075 2 84. K Hirasawa, Nishizuka, and Y.: Phosphatidylinositol Turnover in Receptor Mechanism and Signal Transduction. Annu. Rev. Pharmacol. Toxicol. 25, 147 170 (1985). doi:10.1146/ .001051 85. Tang, X., Edwards, E.M., Holmes, B.B., Falck, J.R., Campbell, W.B.: Role of phospholipase C and diacylglyceride lipase pathway in arachidonic acid release and acetylcholine induced vascular relaxation in rabbit aorta. Am. J. Physiol. Heart C irc. Physiol. 290, H37 H45 (2006). doi:10.1152/ajpheart.00491.2005 86. Bendall, S.C., Hughes, C., Campbell, J.L., Stewart, M.H., Pittock, P., Liu, S., Bonneil, E., Thibault, P., Bhatia, M., Lajoie, G.A.: An Enhanced Mass Spectrometry Approach Reveals Huma n Embryonic Stem Cell Growth Factors in Culture. Mol. Cell. Proteomics. 8, 421 432 (2009). doi:10.1074/mcp.M800190 MCP200 87. Rudomin, E.L., Carr, S.A., Jaffe, J.D.: Directed Sample Interrogation Utilizing an Accurate Mass Exclusion Based Data Dependent A cquisition Strategy (AMEx). J. Proteome Res. 8, 3154 3160 (2009). doi:10.1021/pr801017a 88. Rolland, A.D., Lavigne, R., Dauly, C., Calvel, P., Kervarrec, C., Freour, T., Evrard, B., Rioux Leclercq, N., Auger, J., Pineau, C.: Identification of genital trac t markers in the human seminal plasma using an integrative genomics approach. Hum. Reprod. 28, 199 209 (2013). doi:10.1093/humrep/des360 89. Seo, J., Jeong, J., Kim, Y.M., Hwang, N., Paek, E., Lee, K. J.: Strategy for Comprehensive Identification of Post translational Modifications in Cellular Proteins, Including Low Abundant Modifications: Application to Glyceraldehyde 3 phosphate Dehydrogenase. J. Proteome Res. 7, 587 602 (2008). doi:10.1021/pr700657y

PAGE 208

208 90. Hughes, C.S., Postovit, L.M., Lajoie, G.A.: Matr igel: A complex protein mixture required for optimal growth of cell culture. Proteomics. 10, 1886 1890 (2010). doi:10.1002/pmic.200900758 91. Vahidi, S., Stocks, B.B., Liaghati Mobarhan, Y., Konermann, L.: Mapping pH Induced Protein Structural Changes Und er Equilibrium Conditions by Pulsed Oxidative Labeling and Mass Spectrometry. Anal. Chem. 84, 9124 9130 (2012). doi:10.1021/ac302393g 92. Lmmerhofer, M., Weckwerth, W.: Metabolomics in Practice: Successful Strategies to Generate and Analyze Metabolic Dat a. John Wiley & Sons (2013) 93. Edmands, W.M., Ferrari, P., Rothwell, J.A., Rinaldi, S., Slimani, N., Barupal, D.K., Biessy, C., Jenab, M., Clavel Chapelon, F., Fagherazzi, G., Boutron Ruault, M. C., Katzke, V.A., Khn, T., Boeing, H., Trichopoulou, A., L agiou, P., Trichopoulos, D., Palli, D., Grioni, S., Tumino, R., Vineis, P., Mattiello, A., Romieu, I., Scalbert, A.: Polyphenol metabolome in human urine and its association with intake of polyphenol rich foods across European countries. Am. J. Clin. Nutr. 102, 905 913 (2015). doi:10.3945/ajcn.114.101881 94. Nazari, M., Muddiman, D.C.: Enhanced Lipidome Coverage in Shotgun Analyses by using Gas Phase Fractionation. J. Am. Soc. Mass Spectrom. 27, 1735 1744 (2016). doi:10.1007/s13361 016 1446 5 95. Schwudke D., Oegema, J., Burton, L., Entchev, E., Hannich, J.T., Ejsing, C.S., Kurzchalia, T., Shevchenko, A.: Lipid Profiling by Multiple Precursor and Neutral Loss Scanning Driven by the Data Dependent Acquisition. Anal. Chem. 78, 585 595 (2006). doi:10.1021/ac 051605m 96. Lin, L., Yu, Q., Yan, X., Hang, W., Zheng, J., Xing, J., Huang, B.: Direct infusion mass spectrometry or liquid chromatography mass spectrometry for human metabonomics? A serum metabonomic study of kidney cancer. The Analyst. 135, 2970 2978 (2 010). doi:10.1039/c0an00265h 97. R Development Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2016) 98. Kessner, D., Chambers, M., Burke, R., Agus, D., Mallick, P.: ProteoWiz ard: open source software for rapid proteomics tools development. Bioinforma. Oxf. Engl. 24, 2534 2536 (2008). doi:10.1093/bioinformatics/btn323 99. Taguchi, R., Ishikawa, M.: Precise and global identification of phospholipid molecular species by an Orbit rap mass spectrometer and automated search engine Lipid Search. J. Chromatogr. A. 1217, 4229 4239 (2010). doi:10.1016/j.chroma.2010.04.034

PAGE 209

209 100. Pluskal, T., Castillo, S., Villar framework for processing, visuali zing, and analyzing mass spectrometry based molecular profile data. BMC Bioinformatics. 11, 395 (2010). doi:10.1186/1471 2105 11 395 101. Turner, J.D., Rouser, G.: Precise quantitative determination of human blood lipids by thin layer and triethylaminoeth ylcellulose column chromatography. Anal. Biochem. 38, 437 445 (1970). doi:10.1016/0003 2697(70)90468 9 102. Quehenberger, O., Armando, A.M., Brown, A.H., Milne, S.B., Myers, D.S., Merrill, A.H., Bandyopadhyay, S., Jones, K.N., Kelly, S., Shaner, R.L., Sul lards, C.M., Wang, E., Murphy, R.C., Barkley, R.M., Leiker, T.J., Raetz, C.R.H., Guan, Z., Laird, G.M., Six, D.A., Russell, D.W., McDonald, J.G., Subramaniam, S., Fahy, E., Dennis, E.A.: Lipidomics reveals a remarkable diversity of lipids in human plasma,. J. Lipid Res. 51, 3299 3305 (2010). doi:10.1194/jlr.M009449 103. Wang, M., Hayakawa, J., Yang, K., Han, X.: Characterization and quantification of diacylglycerol species in biological extracts after one step derivatization: a shotgun lipidomics approach. Anal. Chem. 86, 2146 2155 (2014). doi:10.1021/ac403798q 104. Jenkins, B., West, J.A., Koulman, A.: A Review of Odd Chain Fatty Acid Metabolism and the Role of Pentadecanoic Acid (C15:0) and Heptadecanoic Acid (C17:0) in Health and Disease. Molecules. 20, 2425 2444 (2015). doi:10.3390/molecules20022425 105. Gilbertson, J.R., Karnovsky, M.L.: Nonphosphatide fatty acyl esters of alkenyl and alkyl ethers of glycerol. J. Biol. Chem. 238, 893 897 (1963) 106. Kind, T., Liu, K. H., Lee, D.Y., DeFelice, B., Meis sen, J.K., Fiehn, O.: LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat. Methods. 10, 755 758 (2013). doi:10.1038/nmeth.2551 107. Liebisch, G., Vizcano, J.A., Kfeler, H., Trtzmller, M., Griffiths, W.J., Schmitz, G., Spener, F., Wakelam, M.J.O.: Shorthand notation for lipid structures derived from mass spectrometry. J. Lipid Res. 54, 1523 1530 (2013). doi:10.1194/jlr.M033506 108. Koelmel, J.P., Ulmer, C.Z., Jones, C.M., Yost, R.A., Bowden, J.A.: Common cases of improp er lipid annotation using high resolution tandem mass spectrometry data and corresponding limitations in biological interpretation. Biochim. Biophys. Acta BBA Mol. Cell Biol. Lipids. 1862, 766 770 (2017). doi:10.1016/j.bbalip.2017.02.016 109. Ekroos, K., Ejsing, C.S., Bahr, U., Karas, M., Simons, K., Shevchenko, A.: Charting molecular composition of phosphatidylcholines by fatty acid scanning and ion trap MS3 fragmentation. J. Lipid Res. 44, 2181 2192 (2003). doi:10.1194/jlr.D300020 JLR200

PAGE 210

210 110. Kyle, J.E., Zhang, X., Weitz, K.K., Monroe, M.E., Ibrahim, Y.M., Moore, R.J., Cha, J., Sun, X., Lovelace, E.S., Wagoner, J., Polyak, S.J., Metz, T.O., Dey, S.K., Smith, R.D., Burnum Johnson, K.E., Baker, E.S.: Uncovering biologically significant lipid isomers wi th liquid chromatography, ion mobility spectrometry and mass spectrometry. Analyst. 141, 1649 1659 (2016). doi:10.1039/C5AN02062J 111. IUPAC IUB Joint Commission on Biochemical Nomenclature (JCBN) Nomenclature of glycolipids. Eur. J. Biochem. 257, 293 298 (1998). doi:10.1046/j.1432 1327.1998.2570293.x 112. The nomenclature of lipids (recommendations 1976). IUPAC IUB Commission on Biochemical Nomenclature. J. Lipid Res. 19, 114 128 (1978) 113. IUPAC IUB Commission on Biochemical Nomenclature (CBN): The No menclature of Lipids. Eur. J. Biochem. 2, 127 131 (1967). doi:10.1111/j.1432 1033.1967.tb00116.x 114. Fahy, E., Subramaniam, S., Brown, H.A., Glass, C.K., Merrill, A.H., Murphy, R.C., Raetz, C.R.H., Russell, D.W., Seyama, Y., Shaw, W., Shimizu, T., Spener F., van Meer, G., VanNieuwenhze, M.S., White, S.H., Witztum, J.L., Dennis, E.A.: A comprehensive classification system for lipids. J. Lipid Res. 46, 839 861 (2005). doi:10.1194/jlr.E400004 JLR200 115. Gerspach, C., Imhasly, S., Gubler, M., Naegeli, H., Ruetten, M., Laczko, E.: Altered plasma lipidome profile of dairy cows with fatty liver disease. Res. Vet. Sci. 110, 47 59 (2017). doi:10.1016/j.rvsc.2016.10.001 116. Smith, R., Mathis, A.D., Ventura, D., Prince, J.T.: Proteomics, lipidomics, metabolomics view. BMC Bioinformatics. 15, S9 (2014). doi:10.1186/1471 2105 15 S7 S9 117. Ding, J., Sorensen, C.M., Jaitly, N., Jiang, H., Orton, D.J., Monroe, M.E., Moore, R.J., Smith, R.D., Metz, T. O.: Application of the accurate mass and time tag approach in studies of the human blood lipidome. J. Chromatogr. B Analyt. Technol. Biomed. Life. Sci. 871, 243 252 (2008). doi:10.1016/j.jchromb.2008.04.040 118. Liu, Y., Chen, Y., Momin, A., Shaner, R., W ang, E., Bowen, N.J., Matyunina, L.V., Walker, L.D., McDonald, J.F., Sullards, M.C., Merrill, A.H.: Elevation of sulfatides in ovarian cancer: An integrated transcriptomic and lipidomic analysis including tissue imaging mass spectrometry. Mol. Cancer. 9, 1 86 (2010). doi:10.1186/1476 4598 9 186 119. Jin, R., Li, L., Feng, J., Dai, Z., Huang, Y. W., Shen, Q.: Zwitterionic hydrophilic interaction solid phase extraction and multi dimensional mass spectrometry for shotgun lipidomic study of Hypophthalmichthys n obilis. Food Chem. 216, 347 354 (2017). doi:10.1016/j.foodchem.2016.08.074

PAGE 211

211 120. Axelsen, P.H., Murphy, R.C.: Quantitative analysis of phospholipids containing arachidonate and docosahexaenoate chains in microdissected regions of mouse brain. J. Lipid Res. 51, 660 671 (2010). doi:10.1194/jlr.D001750 121. Liebisch, G., Ejsing, C.S., Ekroos, K.: Identification and Annotation of Lipid Species in Metabolomics Studies Need Improvement. Clin. Chem. 61, 1542 1544 (2015). doi:10.1373/clinchem.2015.244830 122. Nar vez Rivas, M., Vu, N., Chen, G. Y., Zhang, Q.: Off line mixed mode liquid chromatography coupled with reversed phase high performance liquid chromatography high resolution mass spectrometry to improve coverage in lipidomics analysis. Anal. Chim. Acta. 954 140 150 (2017). doi:10.1016/j.aca.2016.12.003 123. Li, Q., Zhao, Y., Zhu, D., Pang, X., Liu, Y., Frew, R., Chen, G.: Lipidomics profiling of goat milk, soymilk and bovine milk by UPLC Q Exactive Orbitrap Mass Spectrometry. Food Chem. 224, 302 309 (2017) doi:10.1016/j.foodchem.2016.12.083 124. Skotland, T., Ekroos, K., Kauhanen, D., Simolin, H., Seierstad, T., Berge, V., Sandvig, K., Llorente, A.: Molecular lipid species in urinary exosomes as potential prostate cancer biomarkers. Eur. J. Cancer. 70, 12 2 132 (2017). doi:10.1016/j.ejca.2016.10.011 125. Gallego, S.F., Sprenger, R.R., Neess, D., Pauling, J.K., Frgeman, N.J., Ejsing, C.S.: Quantitative lipidomics reveals age dependent perturbations of whole body lipid metabolism in ACBP deficient mice. Bio chim. Biophys. Acta BBA Mol. Cell Biol. Lipids. 1862, 145 155 (2017). doi:10.1016/j.bbalip.2016.10.012 126. Zhong, H., Xiao, M., Zarkovic, K., Zhu, M., Sa, R., Lu, J., Tao, Y., Chen, Q., Xia, L., Cheng, S., Waeg, G., Zarkovic, N., Yin, H.: Mitochondrial control of apoptosis through modulation of cardiolipin oxidation in hepatocellular carcinoma: A novel link between oxidative stress and cancer. Free Radic. Biol. Med. 102, 67 76 (2017). doi:10.1016/j.freeradbiomed.2016.10.494 127. Lerner, R., Post, J., L och, S., Lutz, B., Bindila, L.: Targeting brain and peripheral plasticity of the lipidome in acute kainic acid induced epileptic seizures in mice via quantitative mass spectrometry. Biochim. Biophys. Acta BBA Mol. Cell Biol. Lipids. 1862, 255 267 (2017). doi:10.1016/j.bbalip.2016.11.008 128. Tsugawa, H., Cajka, T., Kind, T., Ma, Y., Higgins, B., Ikeda, K., Kanazawa, M., VanderGheynst, J., Fiehn, O., Arita, M.: MS DIAL: data independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Metho ds. 12, 523 526 (2015). doi:10.1038/nmeth.3393

PAGE 212

212 129. Kochen, M.A., Chambers, M.C., Holman, J.D., Nesvizhskii, A.I., Weintraub, S.T., Belisle, J.T., Islam, M.N., Griss, J., Tabb, D.L.: Greazy: Open Source Software for Automated Phospholipid Tandem Mass Spe ctrometry Identification. Anal. Chem. 88, 5733 5741 (2016). doi:10.1021/acs.analchem.6b00021 130. Zemski Berry, K.A., Murphy, R.C.: Electrospray ionization tandem mass spectrometry of glycerophosphoethanolamine plasmalogen phospholipids. J. Am. Soc. Mass Spectrom. 15, 1499 1508 (2004). doi:10.1016/j.jasms.2004.07.009 131. Koelmel, J.P., Kroeger, N.M., Gill, E.L., Ulmer, C.Z., Bowden, J.A., Patterson, R.E., Yost, R.A., Garrett, T.J.: Expanding lipidome coverage using LC MS/MS data dependent acquisition wit h automated exclusion list generation. J. Am. Soc. Mass Spectrom. Manuscript Submitted, (2017) 132. Gil, A., Siegel, D., Permentier, H., Reijngoud, D. J., Dekker, F., Bischoff, R.: Stability of energy metabolites? An often overlooked issue in metabolomics studies: A review. Electrophoresis. 36, 2156 2169 (2015). doi:10.1002/elps.201500031 133. Lorenz, M.A., Burant, C.F., Kennedy, R.T.: Reducing Time and Increasing Sensitivity in Sample Preparation for Adherent Mammalian Cell Metabolomics. Anal. Chem. 83, 3406 3414 (2011). doi:10.1021/ac103313x; 134. Plueckthun, A., Dennis, E.A.: Acyl and phosphoryl migration in lysophospholipids: importance in phospholipid synthesis and phospholipase specificity. Biochemistry (Mosc.). 21, 1743 1750 (1982). doi:10.1021/bi0 0537a007 135. R Development Core Team: R: a language and environment for statistical 900051 07 0. Available online at http://www.R (2011) 136. Fahy, E., Sud, M., Cotter, D., Subramaniam, S.: LIPID MAPS online tools for lipid research. Nucleic Acids Res. 35, W606 612 (2007). doi:10.1093/nar/gkm324 137. Kind, T., Okazaki, Y., Saito, K., Fiehn, O.: LipidBlast Templates As Flexible Tools for Creating New in Silico Tandem Mass Spectral Libraries. Anal. Chem. 86, 11024 11027 (2014). doi:10.1021/ac502511a 138. Tautenhahn, R., Patti, G.J., Rinehart, D., Siuzdak, G.: XCMS Online: a web based platform to process untargeted metabolomic data. Anal. Chem. 84, 5035 5039 (20 12). doi:10.1021/ac300698c 139. Husen, P., Tarasov, K., Katafiasz, M., Sokol, E., Vogt, J., Baumgart, J., Nitsch, R., Ekroos, K., Ejsing, C.S.: Analysis of Lipid Experiments (ALEX): A Software Framework for Analysis of High Resolution Shotgun Lipidomics D ata. PLOS ONE. 8, e79736 (2013). doi:10.1371/journal.pone.0079736

PAGE 213

213 140. Herzog, R., Schuhmann, K., Schwudke, D., Sampaio, J.L., Bornstein, S.R., Schroeder, M., Shevchenko, A.: LipidXplorer: A Software for Consensual Cross Platform Lipidomics. PLoS ONE. 7, e29851 (2012). doi:10.1371/journal.pone.0029851 141. Sabareesh, V., Singh, G.: Mass spectrometry based lipid(ome) analyzer and molecular platform: a new software to interpret and analyze electrospray and/or matrix assisted laser desorption/ionization mass spectrometric data of lipids: a case study from Mycobacterium tuberculosis. J. Mass Spectrom. JMS. 48, 465 477 (2013). doi:10.1002/jms.3163 142. Haimi, P., Chaithanya, K., Kainu, V., Hermansson, M., Somerharju, P.: Instrument independent software tools f or the analysis of MS MS and LC MS lipidomics data. Methods Mol. Biol. Clifton NJ. 580, 285 294 (2009). doi:10.1007/978 1 60761 325 1_16 143. Collins, J.R., Edwards, B.R., Fredricks, H.F., Van Mooy, B.A.S.: LOBSTAHS: An Adduct Based Lipidomics Strategy fo r Discovery and Identification of Oxidative Stress Biomarkers. Anal. Chem. 88, 7154 7162 (2016). doi:10.1021/acs.analchem.6b01260 144. Hartler, J., Trtzmller, M., Chitraju, C., Spener, F., Kfeler, H.C., Thallinger, G.G.: Lipid Data Analyzer: unattended identification and quantitation of lipids in LC MS data. Bioinformatics. 27, 572 577 (2011). doi:10.1093/bioinformatics/btq699 145. Song, H., Hsu, F. F., Ladenson, J., Turk, J.: Algorithm for Processing Raw Mass Spectrometric Data to Identify and Quantit ate Complex Lipid Molecular Species in Mixtures by Data Dependent Scanning and Fragment Ion Database Searching. J. Am. Soc. Mass Spectrom. 18, 1848 1858 (2007). doi:10.1016/j.jasms.2007.07.023 146. Ahmed, Z., Mayr, M., Zeeshan, S., Dandekar, T., Mueller, M.J., Fekete, A.: Lipid Pro: a computational lipid identification solution for untargeted lipidomics on data independent acquisition tandem mass spectrometry platforms. Bioinformatics. 31, 1150 1153 (2015). doi:10.1093/bioinformatics/btu796 147. Lintonen, T.P.I., Baker, P.R.S., Suoniemi, M., Ubhi, B.K., Koistinen, K.M., Duchoslav, E., Campbell, J.L., Ekroos, K.: Differential Mobility Spectrometry Driven Shotgun Lipidomics. Anal. Chem. 86, 9662 9669 (2014). doi:10.1021/ac5021744 148. Koivusalo, M., Haimi, P., Heikinheimo, L., Kostiainen, R., Somerharju, P.: Quantitative determination of phospholipid compositions by ESI MS: effects of acyl chain length, unsaturation, and lipid concentration on instrument response. J. Lipid Res. 42, 663 672 (2001) 149. Lam, S.M., Tian, H., Shui, G.: Lipidomics, en route to accurate quantitation. Biochim. Biophys. Acta BBA Mol. Cell Biol. Lipids. 1862, 752 761 (2017). doi:10.1016/j.bbalip.2017.02.008

PAGE 214

214 150. Saito, K., Ohno, Y., Saito, Y.: Enrichment of resolving power improve s ion peak quantification on a lipidomics platform. J. Chromatogr. B. 1055, 20 28 (2017). doi:10.1016/j.jchromb.2017.04.019 151. Haimi, P., Uphoff, A., Hermansson, M., Somerharju, P.: Software tools for analysis of mass spectrometric lipidome data. Anal. Chem. 78, 8324 8331 (2006). doi:10.1021/ac061390w 152. Fauland, A., Kfeler, H., Trtzmller, M., Knopf, A., Hartler, J., Eberl, A., Chitraju, C., Lankmayr, E., Spener, F.: A comprehensive method for lipid profiling by liquid chromatography ion cyclotron resonance mass spectrometry. J. Lipid Res. 52, 2314 2322 (2011). doi:10.1194/jlr.D016550 153. Koelmel, J.P., Kroeger, N.M., Ulmer, C.Z., Bowden, J.A., Patterson, R.E., Cochran, J.A., Beecher, C.W.W., Garrett, T.J., Yost, R.A.: LipidMatch: an automated wor kflow for rule based lipid identification using untargeted high resolution tandem mass spectrometry data. BMC Bioinformatics. 18, 331 (2017). doi:10.1186/s12859 017 1744 3 154. Zacarias, A., Bolanowski, D., Bhatnagar, A.: Comparative measurements of multi component phospholipid mixtures by electrospray mass spectroscopy: relating ion intensity to concentration. Anal. Biochem. 308, 152 159 (2002). doi:10.1016/S0003 2697(02)00209 9 155. Phinney, K.W., Ballihaut, G., Bedner, M., Benford, B.S., Camara, J.E., C hristopher, S.J., Davis, W.C., Dodder, N.G., Eppe, G., Lang, B.E., Long, S.E., Lowenthal, M.S., McGaw, E.A., Murphy, K.E., Nelson, B.C., Prendergast, J.L., Reiner, J.L., Rimmer, C.A., Sander, L.C., Schantz, M.M., Sharpless, K.E., Sniegoski, L.T., Tai, S.S. C., Thomas, J.B., Vetter, T.W., Welch, M.J., Wise, S.A., Wood, L.J., Guthrie, W.F., Hagwood, C.R., Leigh, S.D., Yen, J.H., Zhang, N. F., Chaudhary Webb, M., Chen, H., Fazili, Z., LaVoie, D.J., McCoy, L.F., Momin, S.S., Paladugula, N., Pendergrast, E.C., P feiffer, C.M., Powers, C.D., Rabinowitz, D., Rybak, M.E., Schleicher, R.L., Toombs, B.M.H., Xu, M., Zhang, M., Castle, A.L.: Development of a Standard Reference Material for Metabolomics Research. Anal. Chem. 85, 11732 11738 (2013). doi:10.1021/ac402689t 1 56. Matyash, V., Liebisch, G., Kurzchalia, T.V., Shevchenko, A., Schwudke, D.: Lipid extraction by methyl tert butyl ether for high throughput lipidomics. J. Lipid Res. 49, 1137 1146 (2008). doi:10.1194/jlr.D700041 JLR200 157. Ulmer, C.Z., Koelmel, J.P., Ragland, J.M., Garrett, T.J., Bowden, J.A.: Generated Exact Mass Template for Lipidomics. J. Am. Soc. Mass Spectrom. 28, 1 4 (2017). doi:10.1007/s13361 016 1579 6 158. Altman, D.G., Bland, J.M.: Measurement in Medicine : The Analysis of Method Comparison Studies. J. R. Stat. Soc. Ser. Stat. 32, 307 317 (1983). doi:10.2307/2987937

PAGE 215

215 159. Giavarina, D.: Understanding Bland Altman analysis. Biochem. Medica. 25, 141 151 (2015). doi:10.11613/BM.2015.015 160. Xia, J., Sinelnik ov, I.V., Han, B., Wishart, D.S.: MetaboAnalyst 3.0 making metabolomics more meaningful. Nucleic Acids Res. gkv380 (2015). doi:10.1093/nar/gkv380 161. Tang, S., Zhang, H., Lee, H.K.: Advances in Sample Extraction. Anal. Chem. 88, 228 249 (2016). doi:10.10 21/acs.analchem.5b04040 162. Checa, A., Bedia, C., Jaumot, J.: Lipidomic data analysis: Tutorial, practical guidelines and applications. Anal. Chim. Acta. 885, 1 16 (2015). doi:10.1016/j.aca.2015.02.068 163. Astarita, G., Ahmed, F., Piomelli, D.: Lipidom ic Analysis of Biological Samples by Liquid Chromatography Coupled to Mass Spectrometry. In: Armstrong, D. (ed.) Lipidomics. pp. 201 219. Humana Press (2009) 164. Han, X.: Lipidomics: Comprehensive Mass Spectrometry of Lipids. John Wiley & Sons (2016) 165. Teo, C.C., Chong, W.P.K., Tan, E., Basri, N.B., Low, Z.J., Ho, Y.S.: Advances in sample preparation and analytical techniques for lipidomics study of clinical samples. TrAC Trends Anal. Chem. 66, 1 18 (2015). doi:10.1016/j.trac.2014.10.010 166. Yin, P., Lehmann, R., Xu, G.: Effects of pre analytical processes on blood samples used in metabolomics studies. Anal. Bioanal. Chem. 407, 4879 4892 (2015). doi:10.1007/s00216 015 8565 x 167. Jernern, F., Sderquist, M., Karlsson, O.: Post sampling release o f free fatty acids effects of heat stabilization and methods of euthanasia. J. Pharmacol. Toxicol. Methods. 71, 13 20 (2015). doi:10.1016/j.vascn.2014.11.001 168. Saigusa, D., Okudaira, M., Wang, J., Kano, K., Kurano, M., Uranbileg, B., Ikeda, H., Yatom i, Y., Motohashi, H., Aoki, J.: Simultaneous Quantification of Sphingolipids in Small Quantities of Liver by LC MS/MS. Mass Spectrom. 3, S0046 S0046 (2014). doi:10.5702/massspectrometry.S0046 169. Kamthe, P.N., University, F.A.: Acute toxicity of the agri cultural chemicals endosulfan and copper sulfate to a freshwater shrimp, Palaemonetes paludosus. MyScienceWork. 170. Li, M. tetrachlorobiphenyl congeners in the house cricket. Ecotoxicol. Environ Saf. 24, 309 318 (1992). doi:10.1016/0147 6513(92)90007 P

PAGE 216

216 171. Beeby, A.: What do sentinels stand for? Environ. Pollut. 112, 285 298 (2001). doi:10.1016/S0269 7491(00)00038 5 172. Liebeke, M., Bundy, J.G.: Tissue disruption and extraction methods for m etabolic profiling of an invertebrate sentinel species. Metabolomics. 8, 819 830 (2011). doi:10.1007/s11306 011 0377 1 173. Schock, T.B., Strickland, S., Steele, E.J., Bearden, D.W.: Effects of heat treatment on the stability and composition of metabolomi c extracts from the earthworm Eisenia fetida. Metabolomics. 12, (2016). doi:10.1007/s11306 016 0967 z 174. Koelmel, J., Kroeger, N.M., Ulmer, C.Z., Bowden, J.A., Patterson, R.E., Cochran, J., Beecher, C., Garrett, T.J., Yost, R.A.: LipidMatch: an automate d workflow for rule based lipid identification using untargeted high resolution tandem mass spectrometry data. BMC Bioinformatics. (2017) 175. Benjamini, Y., Hochberg, Y.: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289 300 (1995) 176. Stevens, A., Lowe, J.S., Scott, I.: Core Pathology. Elsevier Health Sciences, Amsterdam, Netherlands (2008) 177. Six, D.A., Dennis, E.A.: The expanding superfamily of phospholipase A2 en zymes: classification and characterization. Biochim. Biophys. Acta BBA Mol. Cell Biol. Lipids. 1488, 1 19 (2000). doi:10.1016/S1388 1981(00)00105 0 178. Broniec, A., Klosinski, R., Pawlak, A., Wrona Krol, M., Thompson, D., Sarna, T.: Interactions of pla smalogens and their diacyl analogs with singlet oxygen in selected model systems. Free Radic. Biol. Med. 50, 892 898 (2011). doi:10.1016/j.freeradbiomed.2011.01.002 179. Sassa, T., Kihara, A.: Metabolism of Very Long Chain Fatty Acids: Genes and Pathophys iology. Biomol. Ther. 22, 83 92 (2014). doi:10.4062/biomolther.2014.017 180. Abdelkafi, S., Abousalham, A.: The substrate specificities of sunflower and soybean phospholipases D using transphosphatidylation reaction. Lipids Health Dis. 10, 196 (2011). doi :10.1186/1476 511X 10 196 181. Armand, M., Borel, P., Ythier, P., Dutot, G., Melin, C., Senft, M., Lafont, H., Lairon, D.: Effects of droplet size, triacylglycerol composition, and calcium on the hydrolysis of complex emulsions by pancreatic lipase: an in vitro study. J. Nutr. Biochem. 3, 333 341 (1992). doi:10.1016/0955 2863(92)90024 D

PAGE 217

217 182. Jenkins Kruchten, A.E., Bennaars Eiden, A., Ross, J.R., Shen, W. J., Kraemer, F.B., Bernlohr, D.A.: Fatty Acid binding Protein Hormone sensitive Lipase Interaction. J Biol. Chem. 278, 47636 47643 (2003). doi:10.1074/jbc.M307680200 183. Reis, A., Spickett, C.M.: Chemistry of phospholipid oxidation. Biochim. Biophys. Acta BBA Biomembr. 1818, 2374 2387 (2012). doi:10.1016/j.bbamem.2012.02.002 184. Brown, S.H.J., Mitc hell, T.W., Blanksby, S.J.: Analysis of unsaturated lipids by ozone induced dissociation. Biochim. Biophys. Acta BBA Mol. Cell Biol. Lipids. 1811, 807 817 (2011). doi:10.1016/j.bbalip.2011.04.015 185. Cajka, T., Fiehn, O.: Comprehensive analysis of lipi ds in biological systems by liquid chromatography mass spectrometry. Trends Anal. Chem. TRAC. 61, 192 206 (2014). doi:10.1016/j.trac.2014.04.017 186. Zhang, X., Garimella, S.V.B., Prost, S.A., Webb, I.K., Chen, T. C., Tang, K., Tolmachev, A.V., Norheim, R .V., Baker, E.S., Anderson, G.A., Ibrahim, Y.M., Smith, R.D.: Ion Trapping, Storage, and Ejection in Structures for Lossless Ion Manipulations. Anal. Chem. 87, 6010 6016 (2015). doi:10.1021/acs.analchem.5b00214 187. Heller, S.R., McNaught, A., Pletnev, I. Stein, S., Tchekhovskoi, D.: InChI, the IUPAC International Chemical Identifier. J. Cheminformatics. 7, 23 (2015). doi:10.1186/s13321 015 0068 4 188. Homer, R.W., Swanson, J., Jilek, R.J., Hurst, T., Clark, R.D.: SYBYL Line Notation (SLN): A Single Nota tion To Represent Chemical Structures, Queries, Reactions, and Virtual Libraries. J. Chem. Inf. Model. 48, 2294 2307 (2008). doi:10.1021/ci7004687

PAGE 218

218 BIOGRAPHICAL SKETCH Jeremy P Koelmel grew up in the borough of Queens in New York City. He was always passionate about the natural world, and it was his greatest pleasure to be in the woods of upstate New York or spend time in his backyard as a child. He would spend countless hours g etting lost in the worlds of the small creatures and plants. Jeremy flourished academically in h igh school due to the project based learning approach at the Baccalaureate School for Global Education (BSGE) and his passionate and invested teachers Jeremy K h igh s chool senior when he determined novel lichen species as indicators of vehicle pollution in his home county of Queens, NY C receiving the Young Naturalist Award from the American Museum of Natural History (2007). Jeremy focused on environmental chemistry during his years at Hampshire College (2007 2011) In the summer of 2010 Jeremy was awarded an NSF REU grant culminating in a paper (unpublished) on diurnal isoprene nitrate chemistry i n polluted and unpolluted air parcels as part of his work at the University of Michigan Biological Station The following year he completed a 122 page senior thesis modeling metal uptake and imaging metal distributions in ferns growing on shooting range so ils in Chesterfield, MA, under chemistry professor Dulasiri Amarasiriwardena and botany professor Lawrence Winship. Results were published in the journal Environment and Pollution In the summer of 2011 he continued research with Dula siri researching gol d nanoparticle uptake by rice plants (manuscript also published in Environment and Pollution ) and synthesizing iron nanoparticles for adsorption of trace metals. During Jeremy's time at Hampshire College, he began to practice Vipassana meditation as taugh t by S. N. Goenka and became very interested in Buddhist

PAGE 219

219 philosophy from various traditions Therefore he took part in the 5 college exchange program to the Central University of Tibetan Studies, in Sarnath, India, where he was shocked by the level of env ironmental pollution in India. After he graduated from Hampshire College, he applied and was accepted as a Fulbright Nehru scholar to study phytoremediation (low cost remediation of contamination using plants and bacteria) with an expert in the field, Dr. M. N. V. Prasad. Jeremy had reached out to Dr Prasad after being inspired after reading one of Dr. Prasad's many books on the subject. As a Fulbright Nehru scholar (2012) Jeremy authored two book chapters and one journal article on the topic of ph ytorem ediation, with emphasis on prospects for phytoremediation in India. As part of Jeremy's Fulbright scholarship, Jeremy used G oogle maps to map out possible pollution sources to the local water systems of Ranipet, Tamil Nadu, India. Jeremy hired a translator and retired Indian military person ne l as a driver, interviewing numerous villagers, taking industrial sludge and water samples for trace metal analysis, and investigating the flora and microbial organism growing in the most polluted locations. Ranipet is designated as one of the top most polluted locations on earth by Blacksmith Institute in 2006. Jeremy's experience in Ranipet and India made him realize that trace metal analysis was limited in scope, and if he was to better characterize and investigate polluted areas he would benefit from learning organic mass spectrometry. After meeting Dr. Richard Yost at the University of Florida, he was attracted by the freedom to follow his interest s under Dr. Yost's mentorship. Dr. Yost's similar interests in envir onmental chemistry also attracted Jeremy. In addition, Dr. Yost's expertise in mass spectrometry, and fina ncial and networking connections, were a valuable asset for Jeremy to realize

PAGE 220

220 h is career goals Soon after he joined the Yost group in 2013 Jeremy be gan working on investigating wildlife disease via lipidomics with a collaboration with Dr. John Bowden which culminated in a trip to South Africa to investigate the devastat ing spread of a disease called p ansteatitis After taking interest in lipids, Jere my quickly realized there were limited software tools to do the required data processing and analysis, which would be very tedious. Jeremy began to rekindle an interest in coding in R, and began writing scripts for collaborators and lab mates to help them with their lipidomics workflows. Inspired by the utility of the research to the broader lipidomics community, Jeremy developed an entire lipidomics open source workflow from data acquisition to relative quantification, working with a team of undergraduat e computer programmers (most importantly Jason Cochran and Nicholas Kroeger), UF faculty Richard Yost and Timothy Garret, and researchers at the National Institute for Standards and Technology (NIST) at Hollings Marine Lab (John Bowden, Candice Ulmer, and Jared Ragland). Lipidomics tools developed during his PhD in which he is first author or coauthor on respective manuscripts include LipidMatch, LipidMatch Quant, LipidPioneer, LipidQC, and IE Omics. Jeremy received his PhD from the University of Florida in the fall of 2017. Beyond scientific research, Jeremy is very active in the Gainesville community. He is trained in life coaching by the Satvatove Institute, and has facilitated numerous 3 4 hour communication course s and one on one coaching sessions, m ainly for undergraduates of University of Florida. He loves to join and teach dance (especially in the tradition of Barbara Mettler) meditation, transformative communication, and loves cooking vegan dishes. He is dating Harmony Miller, midwife, founder, a nd business

PAGE 221

221 owner of Rosemary Birthing Home, and enjoys spending time with her two boys Rio and Cairo Ortiz. As of 2018 Jeremy serve s as a consultant for designing both proprietary and open source lipidomics software. After completing the lipidomics soft ware tools, Jeremy plans on returning to his passion of environmental analytical chemistry, applying his knowledge acquired during his PhD on state of the art organic mass spectrometric techniques and data handling tools to develop techniques in the field of exposomics. Specifically, Jeremy is interested in designing software and developing data acquisition strategies for untargeted detection of anthropogenic chemicals released into the environment. These strategies would be important to detect releases in real time, and quickly assess environmental risk before the levels of persistent chemicals in the environment are raised to dangerous levels. This is important, as once persistent chemicals are released into the environment, the successful remediation of t he environment to pre release conditions is often cost prohibitive.