Mass Spectrometry-Based Lipidomics: Methods and Protocols 1071614096, 9781071614099

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Methods in Molecular Biology 2306

Fong-Fu Hsu Editor

Mass SpectrometryBased Lipidomics Methods and Protocols

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-by step fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

Mass Spectrometry-Based Lipidomics Methods and Protocols

Edited by

Fong-Fu Hsu Mass Spectrometry Resource, Division of Endocrinology, Metabolism, and Lipid Research, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA

Editor Fong-Fu Hsu Mass Spectrometry Resource Division of Endocrinology Metabolism, and Lipid Research Department of Medicine Washington University School of Medicine St. Louis, MO, USA

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-1409-9 ISBN 978-1-0716-1410-5 (eBook) https://doi.org/10.1007/978-1-0716-1410-5 © Springer Science+Business Media, LLC, part of Springer Nature 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface Over the past decade, there have been tremendous advancements in mass spectrometric techniques in lipid identification and quantification. This book presents an account of these advances. In this issue of the Methods in Molecular Biology series, 20 chapters are assembled covering conventional MS-based “shotgun lipidomics” by which samples are introduced by infusion or loop injection, as well as LC-MS-based lipidomics, which are becoming increasingly important due to the ever-increasing demand for a complete and precise lipid analysis of the complex and diversified lipids in nature. This book includes protocols applying chemical reactions, online photochemical reactions combined with various MS methods for comprehensive characterization of various lipid classes, and quantification of specific and rare lipids. Methodologies with hyphenated techniques, such as ion mobility, and techniques such as offline sample purification with TLC, SPE, and semi-preparative LC for separating lipids from various biological specimens are also included. Of importance, pertinent examples that highlight the key role of the state-of-the-art MS methods employing ESI high resolution MSn (n ¼ 2, 3) mass spectrometry to profile the lipidome of the model organism from eukaryotic and bacterial systems for biological researches, and the traditional triple quadrupole and MALDI-TOF-TOF mass spectrometry for quantitative and qualitative analysis of specific lipid classes, as well as MALDI-TOF for imaging, are all covered in the book. This book is a collection of the expertise of many contributors in their fields. It is hoped that the book is useful for biochemists and mass spectroscopists who are interested in lipid studies. It is noteworthy that various nomenclature systems have been used, and the abbreviations, nomenclatures, and lipid classification published by LIPID MAPS consortium [1] with/without modification are adopted in this book. St. Louis, MO, USA

Fong-Fu Hsu

Reference 1. Fahy E et al (2005) A comprehensive classification system for lipids. J Lipid Res 46(5):839–861

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Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1 Mass Spectrometry-Based Lipidomics: An Overview . . . . . . . . . . . . . . . . . . . . . . . . Fong-Fu Hsu 2 High Confidence Shotgun Lipidomics Using Structurally Selective Ion Mobility-Mass Spectrometry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bailey S. Rose, Katrina L. Leaptrot, Rachel A. Harris, Stacy D. Sherrod, Jody C. May, and John A. McLean 3 Global Lipidomics Profiling by a High Resolution LC-MS Platform . . . . . . . . . . ¨ llig, Martin Tro¨tzmu ¨ ller, and Harald C. Ko¨feler Thomas Zu 4 Comprehensive Structural Characterization of Lipids by Coupling Paterno`–Bu¨chi Reaction and Tandem Mass Spectrometry . . . . . . . . Qingyuan Hu, Yu Xia, and Xiaoxiao Ma 5 Chemical Derivatization-Aided High Resolution Mass Spectrometry for Shotgun Lipidome Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vinzenz Hofferek, Huaqi Su, and Gavin E. Reid 6 Quantitative Analysis of Polyphosphoinositide, Bis(monoacylglycero) phosphate, and Phosphatidylglycerol Species by Shotgun Lipidomics After Methylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meixia Pan, Chao Qin, and Xianlin Han 7 Mass Spectrometry-Based Shotgun Lipidomics Using Charge-Switch Derivatization for Analysis of Complex Long-Chain Fatty Acids . . . . . . . . . . . . . . Cheryl Frankfater and Fong-Fu Hsu 8 One-Pot Extractive Transesterification of Fatty Acids Followed by DMOX Derivatization for Location of Double Bonds Using GC-EI-MS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Charles H. Hocart, Abdeljalil El Habti, and Gabriel O. James 9 Ceramide Analysis by Multiple Linked-Scan Mass Spectrometry Using a Tandem Quadrupole Instrument . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fong-Fu Hsu 10 Comprehensive Mouse Skin Ceramide Analysis on a Solid-Phase and TLC Separation with High-Resolution Mass Spectrometry Platform . . . . . . Meei-Hua Lin, Jeffrey H. Miner, and Fong-Fu Hsu 11 Mass Spectrometric Analysis of Meibomian Gland Lipids . . . . . . . . . . . . . . . . . . . . Jianzhong Chen 12 Quantification of Plasma Oxylipins Using Solid-Phase Extraction and Reversed-Phase Liquid Chromatography-Triple Quadrupole Mass Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guan-yuan Chen and Qibin Zhang 13 Targeted Lipidomics of Drosophila melanogaster During Development . . . . . . . . Esther Xue Yi Goh and Xue Li Guan

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Shotgun Lipidomic Analysis of Leishmania Cells. . . . . . . . . . . . . . . . . . . . . . . . . . . . Kai Zhang and Fong-Fu Hsu Characterization of the Uncommon Lipid Families in Corynebacterium glutamicum by Mass Spectrometry. . . . . . . . . . . . . . . . . . . . . . Raju V. V. Tatituri and Fong-Fu Hsu Mass Spectrometric Analysis of Bioactive Sphingolipids in Fungi. . . . . . . . . . . . . . Ashutosh Singh and Maurizio Del Poeta Analytical Methodologies for Lipidomics in Hemp Plant . . . . . . . . . . . . . . . . . . . . Andrea Cerrato, Anna Laura Capriotti, Carmela Maria Montone, Sara Elsa Aita, Giuseppe Cannazza, Cinzia Citti, ` Aldo Susy Piovesana, and Lagana Shotgun Bacterial Lipid A Analysis Using Routine MALDI-TOF Mass Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ge´rald Larrouy-Maumus Imaging of Polar and Nonpolar Lipids Using Desorption Electrospray Ionization/Post-photoionization Mass Spectrometry . . . . . . . . . . . . Chengyuan Liu, Fei Qi, and Yang Pan Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging of Lipids in the Ischemic Rat Brain Section: A Practical Approach . . . . Hay-Yan J. Wang

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors ` di Roma “La Sapienza”, Rome, Italy SARA ELSA AITA • Department of Chemistry, Universita ` di Roma “La Sapienza”, Rome, Italy; LAGANA` ALDO • Department of Chemistry, Universita CNR NANOTEC, Campus Ecotekne, University of Salento, Lecce, Italy GIUSEPPE CANNAZZA • Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy; CNR NANOTEC, Campus Ecotekne, University of Salento, Lecce, Italy ` di Roma “La Sapienza”, ANNA LAURA CAPRIOTTI • Department of Chemistry, Universita Rome, Italy ` di Roma “La Sapienza”, ANDREA CERRATO • Department of Chemistry, Universita Rome, Italy GUAN-YUAN CHEN • Center for Translational Biomedical Research, University of North Carolina at Greensboro, Kannapolis, NC, USA; Graduate Institute of Forensic Medicine, National Taiwan University, Taipei, Taiwan JIANZHONG CHEN • Department of Optometry and Vision Science, The University of Alabama at Birmingham, Birmingham, AL, USA CINZIA CITTI • Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy; CNR NANOTEC, Campus Ecotekne, University of Salento, Lecce, Italy MAURIZIO DEL POETA • Department of Microbiology and Immunology, Stony Brook University, Stony Brook, NY, USA; Division of Infectious Diseases, Stony Brook University, Stony Brook, NY, USA; Veterans Affairs Medical Center, Northport, NY, USA ABDELJALIL EL HABTI • Research School of Biology, Australian National University, Canberra, ACT, Australia CHERYL FRANKFATER • Division of Endocrinology, Metabolism, and Lipid Research, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA ESTHER XUE YI GOH • Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore XUE LI GUAN • Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore XIANLIN HAN • Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Medicine— Diabetes, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA RACHEL A. HARRIS • Center for Innovative Technology, Department of Chemistry, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA CHARLES H. HOCART • Research School of Biology, Australian National University, Canberra, ACT, Australia; Isotopomics in Chemical Biology Group, School of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi’an, China

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VINZENZ HOFFEREK • School of Chemistry, The University of Melbourne, Parkville, VIC, Australia FONG-FU HSU • Mass Spectrometry Resource, Division of Endocrinology, Metabolism, and Lipid Research, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA QINGYUAN HU • State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China GABRIEL O. JAMES • Research School of Biology, Australian National University, Canberra, ACT, Australia HARALD C. KO¨FELER • Core Facility Mass Spectrometry, Medical University of Graz, Graz, Austria GE´RALD LARROUY-MAUMUS • MRC Centre for Molecular Bacteriology and Infection, Department of Life Sciences, Faculty of Natural Sciences, Imperial College London, London, UK KATRINA L. LEAPTROT • Center for Innovative Technology, Department of Chemistry, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA MEEI-HUA LIN • Division of Nephrology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA CHENGYUAN LIU • National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, China XIAOXIAO MA • State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China JODY C. MAY • Center for Innovative Technology, Department of Chemistry, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA JOHN A. MCLEAN • Center for Innovative Technology, Department of Chemistry, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA JEFFREY H. MINER • Division of Nephrology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA; Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, MO, USA ` di Roma “La Sapienza”, CARMELA MARIA MONTONE • Department of Chemistry, Universita Rome, Italy MEIXIA PAN • Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA YANG PAN • National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, China ` di Roma “La Sapienza”, SUSY PIOVESANA • Department of Chemistry, Universita Rome, Italy FEI QI • Key Laboratory for Power Machinery and Engineering of Ministry of Education, Shanghai Jiao Tong University, Shanghai, China CHAO QIN • Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

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GAVIN E. REID • School of Chemistry, The University of Melbourne, Parkville, VIC, Australia; Department of Biochemistry and Pharmacology, The University of Melbourne, Parkville, VIC, Australia; Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, VIC, Australia BAILEY S. ROSE • Center for Innovative Technology, Department of Chemistry, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA STACY D. SHERROD • Center for Innovative Technology, Department of Chemistry, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA ASHUTOSH SINGH • Department of Biochemistry, University of Lucknow, Lucknow, Uttar Pradesh, India HUAQI SU • Department of Biochemistry and Pharmacology, The University of Melbourne, Parkville, VIC, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia RAJU V. V. TATITURI • Division of Rheumatology, Immunology, and Allergy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA MARTIN TRO¨TZMU¨LLER • Core Facility Mass Spectrometry, Medical University of Graz, Graz, Austria HAY-YAN J. WANG • Department of Biological Sciences, National Sun Yat-Sen University, Kaohsiung, Taiwan; Mass Spectrometry Resource, Division of Endocrinology, Diabetes, Metabolism, and Lipid Research, Washington University School of Medicine, St. Louis, MO, USA YU XIA • Department of Chemistry, Tsinghua University, Beijing, China KAI ZHANG • Department of Biological Sciences, Texas Tech University, Lubbock, TX, USA QIBIN ZHANG • Center for Translational Biomedical Research, University of North Carolina at Greensboro, Kannapolis, NC, USA; Department of Chemistry & Biochemistry, University of North Carolina at Greensboro, Greensboro, NC, USA THOMAS ZU¨LLIG • Core Facility Mass Spectrometry, Medical University of Graz, Graz, Austria

Chapter 1 Mass Spectrometry-Based Lipidomics: An Overview Fong-Fu Hsu Abstract Over the last few decades, MS-based lipidomics has emerged as a powerful tool to study lipids in biological systems. This success is driven by the constant demand for complete and reliable data. The improvement of MS-based lipidomics will continue to be dependent on the advances in the technology of mass spectrometry and related techniques including separation and bioinformatics, and more importantly, on gaining insight into the knowledge of lipid chemistry essential to develop methodology for lipid analysis. It is hoped that the protocols in this book, collected from experts in their fields, can offer the beginner and the advanced user alike, useful tips toward successful lipidomic analysis. Key words Shotgun lipidomics, Tandem mass spectrometry, Overview

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Introduction Lipids are the major components of cellular membranes in the three domains of life: eukarya, bacteria, and archaea [1–5]. They are ubiquitous, comprising tens and hundreds of different lipid families and species. They are widely distributed in all the cellular organelles and play critical roles as structural components, and can also function as second messengers to transduce signals within cells. They also serve as important energy sources. In humans, dysregulation of lipid metabolism is known to contribute to the progression of various metabolic and neurodegenerative diseases including obesity, diabetes, hepatic steatosis, cardiovascular, Gaucher’s, and Barth syndrome [6–8]. In pathogenic microorganisms such as bacteria, viruses, fungi, and parasites that are the principal causes of infectious diseases including malaria, tuberculosis, leishmaniasis, and listeriosis, the microbial lipids in these microorganisms play very important roles in many processes involved in host-pathogen relations [9–13]. Therefore, exploring the changes in, and regulation of, the networks of lipids and their metabolic pathways is an increasingly important field that requires lipidomics methodology for

Fong-Fu Hsu (ed.), Mass Spectrometry-Based Lipidomics: Methods and Protocols, Methods in Molecular Biology, vol. 2306, https://doi.org/10.1007/978-1-0716-1410-5_1, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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identification and quantification of the full lipid repertoire of cells, tissues, and organisms. Because of the structural diversity and complexity of lipids, and a wide range of physical properties such as pH, polarity, among various lipid classes, even lipid extraction from various sources alone can be a daunting task. The advances of mass spectrometry techniques over the last decade have prompted the development of mass spectrometry (MS)-based lipidomics, which enables identification and quantification of hundreds of molecular lipid species in a short period of time, covering a wide range of lipid classes extracted from biological specimens. MS-based lipidomics combined with other advanced technologies such as genomics and bioinformatics provides insight into the networks of lipid regulation, metabolism, and other biological functions [14–20]. A wealth of literature regarding the role of lipids in a wide range of diseases, metabolism disorders, and nutrition, as well as lipid chemistry related to lipid handling, localization, structure and analysis, have been published over many decades. Protocols presenting these accounts are also available. However, the need for improvement of the technology for a faster, more precise and comprehensive lipid analysis continues.

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Lipidomics and Chromatographic Separation Lipid Extraction

2.2 Chromatographic Separation

A successful lipidomic analysis can only come with a successful sample preparation, starting with lipid extraction and isolation. Solvent-based methods have been largely used to perform lipid extraction from various biological specimens. Both the Folch method [21] and the Bligh and Dyer [22] method are the most commonly used methods for lipid extraction from a wide range of biological materials. To adapt to the high-throughput lipidomic workflow, in particular, robotic automation, lipid extraction using methyl-tert-butyl ether (MTBE), methanol, and H2O was later introduced [23]. There is no standardized extraction method for all lipids from various samples, therefore modifications based on the above extraction methods and development of novel procedures are often required depending on the requirements of the particular analysis. Han and Gross first defined the lipidomics approach without upfront chromatographic separation as “shotgun lipidomics” [24–26], which is unfortunately a confusing term as it is traced back to the earlier analogous terms such as shotgun genomics [27, 28], and shotgun proteomics [29, 30] coined for comprehensive genome and protein analysis, respectively, in which separation techniques including high pressure liquid chromatography (HPLC) were incorporated to complete the analysis. To obtain

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global lipid analysis without upfront separation such as HPLC in the conventional shotgun lipidomics, partial separation of complex lipid classes in a whole lipid extract is achieved by the so-called “intrasource separation” [20, 31, 32], which is another misleading term suggesting the physical separation of various lipid classes inside the ESI source without applying electric field. In fact, the lipid class separation in the approach is achieved by manipulation of the ionization efficiency by which the signals of different lipid classes are selectively suppressed or preferentially formed [15]. However, while ion suppression and aggregation among various lipid classes using shotgun lipidomics by either direct infusion or loop injection for sample introduction are common and can compromise the precision of the analysis, the method provides a powerful tool for rather comprehensive lipid quantitation and identification with simplicity and speed [15]. To improve the coverage and accuracy in lipidomic analysis, chromatography is introduced upfront for separation based on the chemistries of the lipids of interest [33–36]. While HPLC remains the most common separation method for electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) mass spectrometry (MS), the field of lipidomics has seen an increased number of applications using supercritical fluid chromatography (SFC) which significantly reduces the analytical time [37–39]. Nevertheless, implementation of chromatographic separation in the analysis can only come with the tradeoff of simplicity and speed. Another important innovation to increase the coverage and accuracy in lipid analysis is the implementation of ion mobility (IM) without HPLC in a shotgun lipidomic workflow, or with HPLC for another dimension of separation. Ion mobility is a gas-phase separation strategy, and the collision cross section (CCS) values obtained from the IM measurements can provide an additional molecular descriptor to achieve more confident structural identification and isomeric structure recognition [40–43]. IM separation of gas-phase lipid ions relies on differences in their mobility through a region filled with a buffer gas, normally N2, with separation taking place in a low gradient electric field, in the ion mobility drift cell tube [44]. The physical separation of lipid species achieved by IM is normally within a few milliseconds. This fact supports the earlier argument to reject the concept of the so-called “intrasource separation” inside the ESI source without drift cell tube and applying electric field. Other strategies adapted to the shotgun lipidomics workflow include fast offline isolation and purification using the conventional separation techniques such as TLC, solid phase extraction (SPE), and semi-preparative HPLC to divide samples into many fractions, followed by lipidomic analysis. This approach is extremely useful for untargeted lipidomics, in particular, the minor species in a mixture. The rather simple steps to fractionate the sample also benefit the

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follow-up HPLC for further separation if LC-MS based lipidomic analysis is to be performed because the crude sample has been simplified by the upfront non-HPLC fractionation step. More importantly, ion suppression across various lipid classes that is often seen when applying the conventional shotgun lipidomic method can be reduced, and more samples are available for lipid analysis. In the continuous infusion shotgun lipidomics, only one data set can be obtained in each time point, and those ions that are not recorded are lost. The fractionated samples contain fewer lipid species thus, there is less waste at each time point data collection and less signal suppression and interferences in the collected mass spectrometric data. Overall, incorporating upfront sample fractionation in the shotgun lipidomics workflow leads to a more comprehensive and more precise lipid analysis [45–47].

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Mass Spectrometric Analysis and Bioinformatics Lipid analyses are categorized into non-targeted and targeted approaches. For the non-targeted approach, high-resolution mass spectrometry (HRMS) using Q-TOF and FT-based instruments such as the orbitrap allows generation of lipid profiles with accurate mass and elemental compositions that are extremely valuable for structure recognition [48, 49]. Modern tandem mass spectrometry (MS/MS) instruments can be operated in either data-dependent acquisition (DDA) or data-independent acquisition (DIA) modes for high throughput structural identification. The MS and MSn (n ¼ 2) data can be processed by today’s fast growing open access or commercial software packages built on fragmentation rules and on the accumulated MS2 spectral databases including OpenMS [50], LDA2 [51], and MZMine2 [52], LipidCreator [53] as well as the packages XCMS [54, 55], and MS-DIAL [56] which incorporate nonlinear retention time alignment, matched filtration, and peak detection of high-resolution mass spectra for structural identification through database matching [57, 58]. This automation significantly accelerates the lipid species identification with more confidence. On the other hand, targeted lipidomics is often performed using a triple stage quadrupole (TSQ) instrument, which offers a greater sensitivity and certain specificity for quantitation, particularly in the multiple reaction monitoring (MRM) setting. For targeted analyses by MRM, the structural information of the lipids of interest is required to establish the precursor-product ion pair transition lists and the optimal collision energy for sensitive detection of the precursor-product ion pairs prior to acquisition. Quantitative analyses can be achieved with the addition of stable isotope labelled analogs or non-endogenous homologs, which are spiked into the samples as internal standards before analysis. Other

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targeted approaches using a TSQ instrument involve the application of linked scans, namely, precursor ion scan (PIS) and neutral loss scan (NLS) for fishing out specific lipid classes, and performing quantitation against a known amount of added internal standards [15, 59]. However, for global lipid quantitative analysis, it is not possible that all the stable isotope standards are available and can be added simultaneously. Hence, the stable isotope dilution strategy can only be suitable for very few targeted lipid species, and semiquantitation is by far the most achievable goal for modern MS-based lipidomics. More recently, MALDI-TOF has emerged as another MS-based lipidomic tool for, e.g., imaging lipids in tissue sections, due to its sensitivity and the abundance of lipids in the tissue that can be released by MALDI [60, 61]. MALDI-TOF and MALDI TOF/TOF also have been applied for structural characterization of lipid structure [62–65]; however, the technique is less applicable as compared to the ESI-MSn approaches, mainly due to lack of reproducibility and the universal presence of matrix. The approach is also hampered by the interference of matrixes and the limited resolving power of the instrument (modern MALDI TOF instrument, for example., Bruker Ultraflex, uses a software calculation for instrument resolution. Thus, a claimed resolution of 40,000 at a 50% valley is literally less than 10,000 using a 5% valley definition for an averaged spectrum, and even with internal calibration, the mass accuracy is insufficient for precise extraction of elemental composition. Another downside of the technique is that the practical precursor ion selection window for today’s tandem TOF instrument (except JOEL) is too wide to be useful for definition of the lipid structure in a shotgun lipidomics manner (e.g., a mixture),where analogous species with one or two double bonds often coexist and can’t be isolated by mass spectrometry for further fragmentation [15]. MALDI-TOF, nevertheless, has been widely used in the analysis of microbial lipids isolated from bacteria [66, 67] and mycobacteria [68, 69], providing a useful tool for screening lipid species, in particular, online or offline with TLC [70].

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Conclusion and Perspective Given the diverse chemical nature of lipids and their origin, it is generally accepted that various analytical strategies are complementary and the choice of methods depends on the lipids of interest and the research objectives to be pursued. It is expected that the use of mass spectrometry for lipid research will continue to grow, and that improvement in the techniques will continue (https://www. technologynetworks.com/proteomics/articles/lipidomics-arising-star-in-omics-research-315484#.X0ah-9Qtkv0.link)

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[71]. The technique of conventional “shotgun lipidomics” (no chromatography) as previously defined will be improved by the advances of the technologies such as the resolution of mass spectrometry. However, to constrain the use of chromatography, the scope of application and the precision of the analysis that are in growing demand in biological studies can be severely compromised. The advances in separation technologies implementing fast UHPLC, the ever-growing advances in computer technologies, particularly, in processing speed and in bioinformatics, and more importantly the ceaseless efforts of the researchers in the field, high throughput with chromatographic separation for more comprehensive lipidomic analysis can be a reality. I believe these advances will also drive the application of mass spectrometry-based lipidomics making it more amenable to individuals with different levels of experience. However, this is not to diminish the importance of the experience and expertise of the individual to a reliable and confident lipidomic analysis. It is also hoped that the inclusion of LC-MS based lipidomics to the conventional “shotgun lipidomics” as argued earlier would harmonise all the “-omics” techniques alike with or without applying chromatographic separation. This extension is reflected by the collection of several chapters in this book that apply the LC-MS based lipidomics, GC/MS, IM and upfront TLC/SPE fractionation for profiling the lipidome, and the analysis of various lipid families in biological specimens.

Acknowledgments This work was supported by NIH P30DK020579, P30DK056341, and R24GM136766 grants to Mass Spectrometry Resource of Washington University. References 1. Harayama T, Riezman H (2018) Understanding the diversity of membrane lipid composition. Nat Rev Mol Cell Biol 19(5):281–296. https://doi.org/10.1038/nrm.2017.138 2. Sohlenkamp C, Geiger O (2015) Bacterial membrane lipids: diversity in structures and pathways. FEMS Microbiol Rev 40 (1):133–159. https://doi.org/10.1093/ femsre/fuv008 3. Nakamura Y (2017) Plant phospholipid diversity: emerging functions in metabolism and protein–lipid interactions. Trends Plant Sci 22 (12):1027–1040. https://doi.org/10.1016/j. tplants.2017.09.002 4. Guschina IA, Harwood JL (2013) Chemical diversity of lipids. In: Roberts GCK

(ed) Encyclopedia of biophysics. Springer, Berlin, pp 268–279. https://doi.org/10.1007/ 978-3-642-16712-6_526 5. Jain S, Caforio A, Driessen AJM (2014) Biosynthesis of archaeal membrane ether lipids. Front Microbiol 5:641–641. https://doi.org/ 10.3389/fmicb.2014.00641 6. Zhao Y-Y, Miao H, Cheng X-L, Wei F (2015) Lipidomics: novel insight into the biochemical mechanism of lipid metabolism and dysregulation-associated disease. Chem Biol Interact 240:220–238. https://doi.org/10. 1016/j.cbi.2015.09.005 7. Conroy R, Mackie SA, Boney CM (2018) Disorders of lipid metabolism. In: Radovick S, Misra M (eds) Pediatric endocrinology: a

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41. Kliman M, May JC, McLean JA (2011) Lipid analysis and lipidomics by structurally selective ion mobility-mass spectrometry. Biochim Biophys Acta 1811(11):935–945. https://doi. org/10.1016/j.bbalip.2011.05.016 42. Papan C, Penkov S, Herzog R, Thiele C, Kurzchalia T, Shevchenko A (2014) Systematic screening for novel lipids by shotgun lipidomics. Anal Chem 86(5):2703–2710. https:// doi.org/10.1021/ac404083u 43. Kyle JE, Zhang X, Weitz KK, Monroe ME, Ibrahim YM, Moore RJ, Cha J, Sun X, Lovelace ES, Wagoner J, Polyak SJ, Metz TO, Dey SK, Smith RD, Burnum-Johnson KE, Baker ES (2016) Uncovering biologically significant lipid isomers with liquid chromatography, ion mobility spectrometry and mass spectrometry. Analyst 141(5):1649–1659 44. Lapthorn C, Pullen F, Chowdhry BZ (2013) Ion mobility spectrometry-mass spectrometry (IMS-MS) of small molecules: separating and assigning structures to ions. Mass Spectrom Rev 32(1):43–71. https://doi.org/10.1002/ mas.21349 45. Lin M-H, Hsu F-F, Crumrine D, Meyer J, Elias PM, Miner JH (2019) Fatty acid transport protein 4 is required for incorporation of saturated ultralong-chain fatty acids into epidermal ceramides and monoacylglycerols. Sci Rep 9(1):13254–13254. https://doi.org/10. 1038/s41598-019-49684-y 46. Wang HJ, Tatituri RVV, Goldner NK, Dantas G, Hsu FF (2020) Unveiling the biodiversity of lipid species in Corynebacteria- characterization of the uncommon lipid families in C. glutamicum and pathogen C. striatum by mass spectrometry. Biochimie 10 (20):30156–30155 47. Antonelli M, Benedetti B, Cannazza G, Cerrato A, Citti C, Montone CM, Piovesana S, Lagana` A (2020) New insights in hemp chemical composition: a comprehensive polar lipidome characterization by combining solid phase enrichment, high-resolution mass spectrometry, and cheminformatics. Anal Bioanal Chem 412(2):413–423 48. Almeida R, Pauling JK, Sokol E, HannibalBach HK, Ejsing CS (2015) Comprehensive lipidome analysis by shotgun lipidomics on a hybrid quadrupole-orbitrap-linear ion trap mass spectrometer. J Am Soc Mass Spectrom 26(1):133–148. https://doi.org/10.1007/ s13361-014-1013-x 49. Zu¨llig T, Ko¨feler HC (2020) High resolution mass spectrometry in lipidomics. Mass Spectrom Rev. https://doi.org/10.1002/mas. 21627 50. Pfeuffer J, Sachsenberg T, Alka O, Walzer M, Fillbrunn A, Nilse L, Schilling O, Reinert K,

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Chapter 2 High Confidence Shotgun Lipidomics Using Structurally Selective Ion Mobility-Mass Spectrometry Bailey S. Rose, Katrina L. Leaptrot, Rachel A. Harris, Stacy D. Sherrod, Jody C. May, and John A. McLean Abstract Ion mobility (IM) is a gas phase separation strategy that can either supplement or serve as a highthroughput alternative to liquid chromatography (LC) in shotgun lipidomics. Incorporating the IM dimension in untargeted lipidomics workflows can help resolve isomeric lipids, and the collision cross section (CCS) values obtained from the IM measurements can provide an additional molecular descriptor to increase lipid identification confidence. This chapter provides a broad overview of an untargeted ion mobility-mass spectrometry (IM-MS) workflow using a commercial drift tube ion mobility-quadrupoletime-of-flight mass spectrometer (IM-QTOF) for high confidence lipidomics. Key words Drift tube ion mobility spectrometry, Ion mobility spectrometry, Collision cross section, Tandem MS/MS, Lipidomics

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Introduction Since the emergence of lipidomics as an analytical field, one of the primary challenges of lipid analysis is grappling with the structural similarities inherent to this class of molecules. Lipid biosynthesis generates numerous lipids with shared chemical scaffolds and results in multiple forms of isomerism [1]. Differentiating isomeric lipids via mass spectrometry (MS) is challenging as isomers share the same chemical formulas which yield identical mass-to-charge (m/z) ratios, thereby necessitating the usage of complementary analytical techniques for elucidation. Often, this manifests as a combined liquid chromatography-mass spectrometry (LC-MS) approach whereby lipids are separated in the condensed phase via different retention times prior to mass analysis. However, LC approaches have some drawbacks including long analysis timescales and poor precision in reproducibility. Furthermore, widespread adoption of standardized LC methods which would otherwise benefit cross-laboratory collaboration have not been routinely

Fong-Fu Hsu (ed.), Mass Spectrometry-Based Lipidomics: Methods and Protocols, Methods in Molecular Biology, vol. 2306, https://doi.org/10.1007/978-1-0716-1410-5_2, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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achieved. Moreover, LC is unable to routinely resolve several classes of isomers common to lipids, such as sn-regioisomers and double bond positional isomers [2, 3]. Many of these limitations have the potential of being addressed with ion mobility (IM), which is a gas phase separation technique that is uniquely suited for combination with mass spectrometry [4]. In this chapter, we outline a protocol for utilizing a commercially available IM-MS/MS instrument for performing lipidomics studies without the use of LC, an approach generally referred to as shotgun lipidomics. While this shotgun IM-MS approach is designed for high throughput, it is important to note that this protocol is also fully compatible with LC when comprehensive lipid detection is preferred. 1.1 Challenges in Lipidomic Analyses

From the outset, the field of lipidomics has presented a myriad of analytical challenges to researchers looking to catalog the complete lipid profile of a given biological system. Prior to the development of electrospray ionization, most ionization techniques were not soft enough (i.e., resulted in extensive molecular fragmentation) to yield intact molecular species required for putative identification [5]. Other early common challenges include the development of optimal sample preparation techniques for lipid extraction and MS analysis and the frequently observed ion suppression of some classes of lipids (e.g., phosphatidylethanolamines, or PEs) by others (e.g., phosphatidylcholines, or PCs) [6]. Currently, one of the foremost issues in the field of lipidomics is the sheer magnitude of the isomer issue discussed above [2, 3, 7]. Although the total number of lipids that make up the lipidome is currently unknown, it is estimated to be in the tens to hundreds of thousands. Our research group has previously published an enumeration of the theoretical number of fatty acid (FA) double bond position isomers and cis/trans isomers that can exist for unmodified, straight chain fatty acids containing 18 carbon atoms which indicates that even the simplest FA lipids can exist as thousands of isomeric species [1]. Considering that many classes of lipids are comprised of FA building blocks, it is then no surprise that the latest Human Metabolome Database (HMDB) update in 2018 included over 90,722 results for lipids [8]. Many of these species exist as a series of isomeric structural variants, for which lipids have four main types: sn-regioisomers, double bond position isomers, cis/trans isomers, and stereoisomers [1]. As stated previously, due to identical mass-to-charge ratios, isomeric species cannot be resolved solely by MS. Thus, a combination of multiple analytical techniques is suited towards tackling the lipid isomer challenge.

1.2 Ion Mobility of Lipids

Ion mobility separation of lipid samples has yielded significant insight over the years [9, 10]. For those new to the technique, IM separates gas-phase ions based on differences in their mobility through a region filled with a buffer gas, typically nitrogen. When

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interfaced with a mass spectrometer, IM provides an orthogonal dimension of separation which leads to multiple analytical benefits including increased peak capacity and signal-to-noise [4]. Additionally, the measured drift time of a given analyte can be used to calculate its collision cross section (CCS), the rotationally averaged 2-D cross sectional area of the molecular ion. Therefore, IM is able to separate lipid compounds based on differences in their CCS [11]. Isomers, which have identical chemical formulas but different 3-dimensional structures, are able to be separated via IM provided that their differences in CCS are large enough to be resolved on a given instrument. Prior work from Dodds and coworkers has indicated that constitutional isomers are the easiest to separate, having the largest percentage difference in CCS, while stereoisomers are the most difficult to separate and have the smallest percentage difference in CCS [12]. In practice, studies have demonstrated the ability to distinguish lipid sn-regioisomers, cis/trans isomers, and in some cases double bond isomers. For example, Kyle and coworkers used IM to separate cis/trans isomers of PE(18:1(Δ9)) standards, and Groessl and coworkers were able to distinguish silver cationized sn-regioisomers of PC 34:1 in porcine brain lipid extracts [13, 14]. However, particularly in the case of double bond position isomers and stereoisomers, the differences in CCS are often too small to distinguish isomeric compounds, especially in a complex biological matrix. In addition to its ability to separate multiple kinds of lipid isomers, IM offers other analytical benefits for structural characterization of lipid molecules, particularly when coupled with MS analysis in IM-MS. Work from the McLean group has demonstrated that different classes of biomolecules exhibit unique mobility-mass correlations corresponding primarily to the efficiency of gas-phase structural packing for a given class [15, 16]. Lipids, for example, tend to have larger gas phase structures at a given m/z in comparison to carbohydrates and peptides [9]. Knowledge of these mobility-mass relationships and the specific conformational space a class of biomolecules occupies allows for using IM as a class-specific filter. Recent work from the authors has taken this idea a step further, developing specific regression models for 6 lipid classes and 14 subclasses, including glycerophospholipids, steroids and their derivatives, sphingolipids, and more [17]. Using these regression models and their associated confidence intervals, a CCS corresponding to an unknown m/z can be assigned first as a lipid, then to a more specific lipid subclass. For example, in a test study using human serum an unknown feature with a m/z of 744.49 and CCS 278.2 Å2 was assigned as a PE rather than a PC based on its deviation from the mean of the regression models for the PE and PC subclasses [17]. Interestingly, the specific, repeated structural motifs present in lipid molecules allow further interrogation to more subtle structural patterns based

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on the number of carbon atoms and degree of unsaturation in the acyl chain tail(s) of either a glycerophospholipid or a sphingolipid. In a shotgun IM-MS analysis of class-specific TLC fractions, it was observed that for a given carbon chain length, a series of lipids from any particular subclass (PE, PC, PS, etc.) displays a positive linear correlation in CCS as the number of double bonds in the acyl chain tail(s) decreases [18]. Conversely, when viewing a series of different phospholipids containing identical numbers of double bonds, the CCS increases as the carbon chain length of the acyl chain tail (s) increases [18]. These chemically specific CCS trends are enabled by IM-MS analysis and provide an additional layer of structural information that can aid in identifying unknown lipids derived from the untargeted analysis of complex biological samples. 1.3 Instrumentation and Software Overview

The methods described in this chapter were developed for use on a commercial drift tube ion mobility quadrupole time-of-flight instrument (6560 IM-QTOF, Agilent Technologies), which has been described in depth previously (Fig. 1) [16]. This instrument utilizes an ion transfer capillary and a two-stage ion funnel to transmit ions from atmospheric pressure to a reduced pressure regime of ca. 4 Torr, which is suitable for ion focusing and mobility separation. The first stage ion funnel provides high ion transmission to lower pressure, whereas the second stage ion funnel operates as an ion trap to accumulate and release ions in discrete pulses into the drift tube. IM separations in the drift tube occur under uniform

Fig. 1 A schematic representation of the LC system and IM-MS instrument used in this protocol. For a shotgun approach, the LC is used without a chromatography column for automated injection of samples

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Fig. 2 Overview of the general workflow for shotgun lipidomics using IM-MS/MS

electric field conditions in nitrogen gas at ambient temperature. Following mobility separation, radially disperse ions are refocused in a third ion funnel for transmission to the Q-TOF. The IM methods utilized in this chapter are based on prior work that optimizes the IM resolving power (ca. 60 t/Δt) while allowing for the calculation of CCS with high precision and accuracy ( All Features. 3.8 Export Features and Prepare a Targeted MS/MS List

This section describes the steps needed to generate a precursor ion inclusion list for data-dependent MS/MS fragmentation and specify the collision energies (lab frame) to use in the method. 1. Ensure the appropriate project is open in MP.

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Fig. 9 Mass Profiler table of extracted and annotated features accompanied by a visualization of each feature

2. Use the checkboxes in the Mark column of the feature table to select features to be targeted. 3. From the File menu, select Export Target MS/MS Inclusion List ! From Table—Marked Features. . . 4. Within the Export Inclusion List Options popup, check Most abundant, set the RT tolerance to 1.00 min, and click OK to save a .csv file with the targeted inclusion list suitable for import into the MassHunter Acquisition Method Editor. 5. Open the .csv file in spreadsheet software (e.g., Microsoft Excel or Google Sheets). 6. Edit the file as directed below to the desired specifications for the targeted fragmentation experiment (see Notes 27 and 28). (a) Add ten sequential rows for one feature of interest. Start with the lowest m/z. l Input “TRUE” in the On column in all ten rows. l

l

Copy the precise m/z to the Prec. m/z column in all ten rows. Input “1” as the charge in the Z column in all ten rows.

Ion Mobility for High Confidence Lipidomics l

l

l

l

l

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Input [0.1 + dead time] in the Ret. Time (min) column in all ten rows (see Note 29). For example, if analyte signal typically begins at 1.0 min, input 1.1. Input “0.18” in the Delta Ret. Time (min) column in all ten rows (see Note 30). Input “Narrow (~1.3 m/z)” in the Iso. Width column in all ten rows. Input the following values in sequential rows of the Collision Energy column: 0, 10, 10, 10, 20, 20, 20, 40, 40, 40 (see Notes 31 and 32). Leave the rows in the Acquisition Time (ms/spec) column blank.

(b) Repeat the steps of Subheading 3.8, step 6a for each m/z of interest, incrementing the retention time by 0.2 min for each feature. If the signal duration is 1.0 min, five m/z can be targeted in a method, at retention times of the dead time plus 0.1, 0.3, 0.5, 0.7, and 0.9 min (see Note 33). (c) Delete rows exported from MP. These were only used for convenience of carrying the m/z to the .csv file. 7. Save the file. 3.9 Targeted Fragmentation Methods Preparation

1. From the Method Editor window in the acquisition context of the MassHunter Data Acquisition software, load the positive polarity lipidomics method that was saved in Subheading 3.3.2, step 5. Save the method as a new file, adding an indication of fragmentation to the file name (e.g., yyyymmdd_AJSp_Lipids_MSMS.m). 2. For the 1290 system: In the HiP Sampler tab of the Method Editor window, set the Injection Volume to 10.00 μL. 3. Under the Q-TOF tab of the Method Editor window, in the Acquisition tab, choose the Mode Targeted MS/MS (Seg). (a) From the Spectral Parameters tab, disable Max Time between MS1 Spectra and Use PC for MS/MS decisions. (b) From the Collision Energy tab (Fig. 10), select Use Fixed Collision Energies and include a single time segment at 0 V collision energy in the table. (c) From the Targeted List tab, right-click on the empty table and select Import. Navigate to the edited .csv file from Mass Profiler and click Open. (d) Set the collision energy at 30 V (see Note 34). 4. Save the positive polarity fragmentation method.

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Fig. 10 The parameters table used for setting up an inclusion list for targeted MS/MS data acquisition

5. Save the developed method as a new file, replacing the indication for positive polarity in the file name with an indication of negative polarity (e.g., yyyymmdd_AJSn_Lipids_MSMS.m). 6. From the Q-TOF tab of the Method Editor window, under the General tab, select Negative Ion Polarity. 7. From the Q-TOF tab of the Method Editor window, under the Advanced Parameters tab, select Use Method and input the indicated Method Setting for the following [19]: (a) IM-FrontFunnel, Trap Funnel Delta: 200 V. (b) IM-DriftTube, Drift Tube Entrance Voltage: 1574 V. (c) IM-DriftTube, Drift Tube Exit Voltage: 224 V. (d) IM-RearFunnel, Rear Funnel Entrance: 217.5 V. (e) IM-RearFunnel, Rear Funnel Exit: 45 V. 8. Repeat step 3c, importing the negative mode targeted list from Mass Profiler. Save the negative polarity fragmentation method. 3.10 Targeted Fragmentation Qualitative Data Analysis

1. Open an experimental data file with targeted fragmentation in IM-MS Browser. 2. Ensure the Frame Information window is visible by selecting it from the View menu. This window will provide the experimental Fragmentation Energy for any selected frame (see Note 35). 3. In the File Overview window, select the green Gaussian peak icon to Display chromatogram for frame navigation. This will display the total ion chromatogram (TIC). 4. Double click on the TIC at the end of the dead time (see Note 29) where the first significant drop in signal occurs. This will

Ion Mobility for High Confidence Lipidomics

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display the frame of the first isolated m/z of interest without fragmentation (0 V collision energy). (a) Zoom in on the isolated m/z by right-click ! dragging to draw a zoom box within the heat map in the Frame Viewer window, and observe that the desired feature was successfully isolated. (b) Keep the zoomed drift time range but extend the m/z range to include lower values by right-click ! dragging from right to left along the m/z axis of the mass spectrum. (c) Navigate between frames with the left and right arrow icons in the File Overview window or with Alt + Left Arrow and Alt + Right Arrow. (d) Sum frames of common collision energy and/or all ten isolation frames for a feature by left click ! dragging on the TIC across the period of low signal and then double clicking on the TIC. 5. Observe mobility-aligned fragment ions (see Note 36). These correspond to the isolated lipid feature [28]. Of note, acquisition in both polarities is useful for comprehensive fragment analysis. Fatty acyl tail fragments can be observed most prominently in negative ion mode while some diagnostic glycerophospholipid headgroup fragments are observed only in positive ion mode [29, 30]. 6. Repeat Subheading 3.10, step 4 for the second significant drop in signal at ca. 0.2 min past the dead time to observe the second isolated m/z of interest. Continue in this way to observe all targeted lipids.

4

Notes 1. Any strong solvent suitable for cleaning may be used for the needle wash solution. 2. Sample introduction to the ionization source can also be performed via direct infusion using a syringe and syringe pump, though an LC system promotes efficiency and reproducibility with less sample consumption. A 500 μL glass syringe (e.g., 700 series, Hamilton) and a syringe pump capable of infusion rates of ~10 μL/min (e.g., KDS100, KD Scientific) are recommended. 3. Version 8.0 or later is sufficient, but screenshots in this work are taken from version 10.0. 4. If performing direct infusion via syringe and syringe pump, a minimum total volume of 600 μL is recommended. This allows for loading of 100 μL for each experiment for a ~10 μL/min

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infusion rate, with sufficient sample volume to acquire three replicates for IM-MS and three replicates for MS/MS analyses. 5. If performing direct infusion via syringe and syringe pump, store samples without the glass insert. 6. Pressures of the trap funnel region and drift tube region can also be displayed in the Actuals window, but with a lower refresh rate. 7. Parameters may be changed where applicable. For instance, it may be advisable to select the tune sub-setting for low mass (50–750 m/z) ions to optimize transmission for lower mass lipids such as fatty acids or sterols (see Note 9). 8. If the instrument has been tuned recently (e.g., within the past week), the full Transmission Tune of the Quadrupole and TOF that is outlined in the rest of this step can be skipped and a Mass Calibration/Check tune can be conducted instead. 9. When tuning the Quadrupole, the 50–1700 m/z TOF range is selected by default; however, there are several other TOF m/z range optimizations supported by the autotune that may be beneficial to use for lipidomic work, namely the 50–750 m/z range and the 50–250 m/z range, the latter having both standard (Stable) and Fragile ion transmission options. To access these other TOF transmission ranges, perform a second autotune with TOF, Transmission Tune, and the desired m/z range selected. Note that Fragile ion tune may require that the 50– 250 m/z range be tuned in Stable ion mode first, prior to completing successfully. 10. The autotune function will first attenuate the ion signal of each tune mix ion to the appropriate signal ranges for the TOF to achieve optimal mass measurement accuracy. The autotune utilizes a SWARM algorithm which samples a broad range of settings before converging on the optimal values for each ion optical element. 11. If IM resolutions of less than 50 are observed for specific tune mix ions, this may be acceptable if the ion counts for that ion are low compared to the other ions. If most of the tune mix ions are reporting IM resolutions below 50, this may be an indication of a poorly tuned IM stage, gas pressure instability, or some other factor contributing to poor IM measurement reproducibility, in which case repeating the autotune is recommended. 12. Methods can alternatively be developed in the Offline Method Editor (Agilent), which can be useful while the instrument is acquiring other data or when it is desirable to compare two methods side-by-side.

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13. Advanced autosampler parameters such as cleaning steps may be adjusted to further minimize contamination or sample-tosample carryover. 14. Source parameters may be optimized further for specific applications. 15. Multiplexed files are compatible with the steps outlined in this protocol and can provide an additional gain in sensitivity. Recommended settings are: 4-bit (or higher) and the maximum trap fill time allowed. If implemented, include a demultiplexing step when preprocessing the data (Subheading 3.5.1). In the PNNL PreProcessor, select Demultiplexing, and the chromatography/infusion (moving average) to three points. 16. The injection volume can be increased for lower concentration samples. 17. If your analyte plug (as visualized from the total ion chromatogram) does not elute within the window, lengthen the Stoptime here to accommodate. 18. Alternatively, a syringe pump can be used to perform direct infusion. Calibrate the syringe pump to the diameter of the syringe in use and use a flow rate of 0.035 mL/min. The order of sample acquisition should be the same as when using flow injection methods via an autosampler. The user should take care to flush the lines adequately between acquisitions to prevent carryover. The user must also watch the real-time spectra and start acquiring data after analyte signal is observed and has stabilized for ca. 10 s. 19. The PNNL PreProcessor is freely available on the PNNL website. It uses a simple moving average algorithm to smooth the drift dimension, effectively reducing artifacts which would otherwise be falsely detected from feature finding and increasing the number of detected features. The drift smoothing value refers to the number of data points averaged to calculate the smoothed value. Optimized values depend on the IM resolving power and should be adjusted as appropriate, as oversmoothing can result in poor isobaric separation. The PreProcessor program will generate separate files with the smoothed data in the same parent directory as the original, unmodified input files. 20. IM-MS spectra can be complex and contain signals of various origins, some not directly related to the analytes of interest. These various ion signals contribute to the chemical noise in the spectrum and can include nonspecific multimers (e.g., heteromultimers such as lipid-lipid species, as well as lipid

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aggregates with other non-lipid species in the sample, and these can be both singly- and multiply charged), and contaminants arising from the sample processing and sample injection steps, to name a few. Specifically for lipids, artifacts can appear prominently at the upper regions of the IM-MS spectrum, corresponding to high-abundance multimers dissociating into monomers. 21. Mass Profiler can perform basic differential statistics between two sample groups. If more advanced statistical comparison will be performed in an external program, group designation is not necessary. In this case, extract features as a single group. 22. Feature finding parameters may be changed and optimized based on acquisition parameters or sample composition. The parameters used here may be used as a starting point and adjusted as necessary. More options are available upon selecting the “All parameters” option. This includes statistics and filtering parameters that may be of interest for sample group comparisons. 23. Agilent Compound Database (.cdb) files are used to search for identifications. These files can be curated internally or adapted from freely available accurate databases such as METLIN or LIPID MAPS using the Personal Compound Database and Library (PCDL) Manager (Agilent). The McLean Unified CCS Compendium contains lipid CCS values empirically measured from the IM-QTOF instrumentation described in this protocol which has been preformatted as a .cdb file and can be downloaded freely from [17]. Investigators may also wish to incorporate theoretically predicted CCS values into their respective databases to improve annotation efforts. 24. The tolerance for CCS matching may be adjusted depending on the source of the database being used. CCS values acquired using an Agilent 6560 IM-QTOF using nitrogen as the drift gas and standardized methods have an average interlaboratory reproducibility of less than 0.5%. CCS values acquired using TWIMS systems have been shown to fall within 3% of DT measurements. 25. Common charge carrier types for lipids: Positive ion mode ¼ +H, +Na, +K, +NH4. Negative ion mode ¼ H, +Cl, +HCOO. Charge state range of 1, which is suitable for most lipidomic work. For lipids where multiple charging is expected, e.g., inositides, a higher charge state range is warranted. 26. Exported feature tables from MP may be used for further statistical analysis or manual identification workflows in external software such as Excel or R. A hallmark of ion mobility is the mobility-mass conformational correlations that emerge

Ion Mobility for High Confidence Lipidomics

35

from these measurements. The relationships are specific to lipid class and subclass structure, as well as smaller conformational changes resulting from degree of unsaturation or acyl chain length. Quantitative descriptions of these trends can be used to further support lipid identification in detailed analyses using exported feature measurements from MP. 27. Each row in the table translates to one frame in IM-MS Browser and will be performed in sequential order, starting at the top of the table. Retention time designation is prioritized over row order. 28. The timing in this section can be difficult and may require some method modification to fit each user’s system. The initial appearance and duration of spectral signal from the injected sample should match the data acquired with the lipidomics method from Subheading 3.3.2, step 5. The times observed by the user will likely differ from the model system and may be attributed to any variation including tubing length and diameter, solvent composition, flow rate, and temperature. The duration of signal should be 1.00 min or greater. 29. Consider the time from the start of acquisition to the appearance of analyte signal to be the dead time. 30. This value specifies the total range of time over which the associated rows will be looped through. 31. These suggested collision energies should be effective in characterizing head groups and tail compositions for a wide range of lipids. However, the user may choose to alter these values for improved performance in fragmenting specific lipids. 32. It is important that common collision energies for each massto-charge are listed sequentially in the table. IM-MS Browser allows sequential frames to be summed, and this will allow viewing of more signal for each experimental condition. 33. If there are more than five features of interest and the sample signal duration is 1.0 min, multiple .csv files can be generated with five features in each file. Each .csv will be used in two corresponding method files (one in positive ionization mode and one in negative ionization mode). Note that more sample volume should be prepared to correspond to the increase in injected volume. If the signal duration is greater than 1.0 min, one feature can be added for every additional 0.2 min. 34. It may be desirable to set multiple collision energies for each ion of interest. In this case, it is easiest to edit the targeted list spreadsheet externally. Insert a duplicate row for each feature with a different specified collision energy. 35. This information is also displayed in the Frame Viewer in the string of text above the mass spectrum.

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36. Mobility-aligned fragments exhibit different m/z values but share the same nominal drift times. This occurs when ion fragmentation occurs after the IM measurement (i.e., postIM CID). For singly charged lipid precursors, fragments should only appear at lower m/z values. If higher m/z values are observed aligned at the same drift time, then this is an indication that multiply charged ions (e.g., a doubly charged dimer which can occur for high-abundance lipids) have also been transmitted within the specified isolation window.

Acknowledgments This work was supported in part using the resources of the Center for Innovative Technology (CIT) at Vanderbilt University. BSR acknowledges a fellowship from the Vanderbilt Institute for Chemical Biology (VICB). Financial support was provided by the National Institutes of Health (R01GM107978) and the U.S. Environmental Protection Agency (EPA) under Assistance Agreement No. 83573601. This work has not been formally reviewed by the EPA and EPA does not endorse any products or commercial services mentioned in this document. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the EPA or the U.S. Government. References 1. Harris RA, Leaptrot KL, May JC et al (2019) New frontiers in lipidomics analyses using structurally selective ion mobility-mass spectrometry. TrAC Trends Anal Chem 116:316–323 2. Hancock SE, Poad BLJ, Batarseh A et al (2017) Advances and unresolved challenges in the structural characterization of isomeric lipids. Anal Biochem 524:45–55 3. Zheng X, Smith RD, Baker ES (2018) Recent advances in lipid separations and structural elucidation using mass spectrometry combined with ion mobility spectrometry, ion-molecule reactions and fragmentation approaches. Curr Opin Chem Biol 42:111–118 4. May JC, McLean JA (2015) Ion mobility-mass spectrometry: time-dispersive instrumentation. Anal Chem 87(3):1422–1436 5. Wang C, Wang M, Han X (2015) Applications of mass spectrometry for cellular lipid analysis. Mol BioSyst 11:698–713 6. Han X, Yang K, Gross RW (2012) Multidimensional mass spectrometry-based shotgun

lipidomics and novel strategies for lipidomic analyses. Mass Spectrom Rev 31:134–178 7. Koelmel JP, Ulmer CZ, Jones CM et al (2017) Common cases of improper lipid annotation using high-resolution tandem mass spectrometry data and corresponding limitations in biological interpretation. Biochim Biophys Acta Mol Cell Biol Lipids 1862:766–770 8. Wishart DS, Feunang YD, Marcu A et al (2018) HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res 46: D608–D617 9. Kliman M, May JC, McLean JA (2011) Lipid analysis and lipidomics by structurally selective ion mobility-mass spectrometry. Biochim Biophys Acta Mol Cell Biol Lipids 1811:935–945 10. Paglia G, Kliman M, Claude E et al (2015) Applications of ion-mobility mass spectrometry for lipid analysis. Anal Bioanal Chem 407:4995–5007 11. Mason EA, McDaniel EW (1988) Transport properties of ions in gases. Wiley, New York, NY

Ion Mobility for High Confidence Lipidomics 12. Dodds JN, May JC, McLean JA (2017) Investigation of the complete suite of the leucine and isoleucine isomers: toward prediction of ion mobility separation capabilities. Anal Chem 89:952–959 13. Groessl M, Graf S, Knochenmuss R (2015) High resolution ion mobility-mass spectrometry for separation and identification of isomeric lipids. Analyst 140:6904–6911 14. Kyle JE, Zhang X, Weitz KK et al (2016) Uncovering biologically significant lipid isomers with liquid chromatography, ion mobility spectrometry and mass spectrometry. Analyst 141:1649–1659 15. Fenn LS, Kliman M, Mahsut A et al (2009) Characterizing ion mobility-mass spectrometry conformation space for the analysis of complex biological samples. Anal Bioanal Chem 394:235–244 16. May JC, Goodwin CR, Lareau NM et al (2014) Conformational ordering of biomolecules in the gas phase: nitrogen collision cross sections measured on a prototype high resolution drift tube ion mobility-mass spectrometer. Anal Chem 86:2107–2116 17. Picache JA, Rose BS, Balinski A et al (2019) Collision cross section compendium to annotate and predict multi-omic compound identities. Chem Sci 10:983–993 18. Leaptrot KL, May JC, Dodds JN et al (2019) Ion mobility conformational lipid atlas for high confidence lipidomics. Nat Commun 10:985 19. Stow SM, Causon TJ, Zheng X et al (2017) An interlaboratory evaluation of drift tube ion mobility-mass spectrometry collision cross section measurements. Anal Chem 89:9048–9055 20. Matyash V, Liebisch G, Kurzchalia TV et al (2008) Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics. J Lipid Res 49:1137–1146 21. Ulmer CZ, Jones CM, Yost RA et al (2018) Optimization of Folch, Bligh-Dyer, and Matyash sample-to-extraction solvent ratios

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for human plasma-based lipidomics studies. Anal Chim Acta 1037:351–357 22. Morris CB, May JC, Leaptrot KL et al (2019) Evaluating separation selectivity and collision cross section measurement reproducibility in helium, nitrogen, argon, and carbon dioxide drift gases for drift tube ion mobility–mass spectrometry. J Am Soc Mass Spectrom 30:1059–1068 23. Kurulugama RT, Darland E, Kuhlmann F et al (2015) Evaluation of drift gas selection in complex sample analyses using a high performance drift tube ion mobility-QTOF mass spectrometer. Analyst 140:6834–6844 24. May JC, Dodds JN, Kurulugama RT et al (2015) Broadscale resolving power performance of a high precision uniform field ion mobility-mass spectrometer. Analyst 140:6824–6833 25. Mahieu NG, Spalding JL, Gelman SJ et al (2016) Defining and detecting complex peak relationships in mass spectral data: the Mz. unity algorithm. Anal Chem 88:9037–9046 26. Mahieu NG, Patti GJ (2017) Systems-level annotation of a metabolomics data set reduces 25,000 features to fewer than 1000 unique metabolites. Anal Chem 89:10397–10406 27. Zheng X, Aly NA, Zhou Y et al (2017) A structural examination and collision cross section database for over 500 metabolites and xenobiotics using drift tube ion mobility spectrometry. Chem Sci 8:7724–7736 28. Hoaglund-Hyzer CS, Li J, Clemmer DE (2000) Mobility labeling for parallel CID of ion mixtures. Anal Chem 72:2737–2740 29. Navas-Iglesias N, Carrasco-Pancorbo A, Cuadros-Rodrı´guez L (2009) From lipids analysis towards lipidomics, a new challenge for the analytical chemistry of the 21st century. Part II: analytical lipidomics. TrAC Trends Anal Chem 28:393–403 30. Pati S, Nie B, Arnold RD et al (2016) Extraction, chromatographic and mass spectrometric methods for lipid analysis. Biomed Chromatogr 30:695–709

Chapter 3 Global Lipidomics Profiling by a High Resolution LC-MS Platform Thomas Zu¨llig, Martin Tro¨tzmu¨ller, and Harald C. Ko¨feler Abstract Lipidomics is the determination of big lipid assemblies by mass spectrometry. When using chromatography coupled high resolution mass spectrometry, lipids can be identified by exact mass, fragment spectra, and retention time. This protocol covers lipid extraction, LC-MS data acquisition by Orbitrap mass spectrometry and data processing by Lipid Data Analyzer, a custom developed open source software. Key words Lipidomics, Mass spectrometry, Chromatography, LC-MS, Lipids, Lipid data analyzer

1

Introduction When electrospray ionization came to its stage of maturity in the early nineties, this development resulted in an enormous boost for mass spectrometric analysis of lipids. In fact, it enabled the determination of huge assemblies of lipids in one analytical run, a technology termed lipidomics [1, 2]. Over the last three decades, lipidomics developed into two basic branches: shotgun lipidomics [3–5] which refrains from using chromatographic separation and LC-MS based lipidomics [6, 7] which uses chromatographic coupling. In a nutshell, shotgun lipidomics generally excels by its robustness and quantitative properties, while the merits of LC-MS lipidomics in general are deeper coverage of the lipidome and increased identification certainty. When sticking to the LC-MS approach, most groups use either reversed phase chromatography or hydrophilic interaction liquid chromatography (HILIC), with the former separating lipids by their fatty acyl hydrophobicity while the latter is separating them according to lipid class by their polar headgroups [8–11]. From the perspective of application, the chosen approach can either be targeted or non-targeted lipidomics. In a targeted approach a triple quadrupole mass spectrometer running on selected reaction monitoring (SRM) with known mass

Fong-Fu Hsu (ed.), Mass Spectrometry-Based Lipidomics: Methods and Protocols, Methods in Molecular Biology, vol. 2306, https://doi.org/10.1007/978-1-0716-1410-5_3, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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transitions is often sufficient, although also quadrupole-time of flight (Q-TOF) or Orbitrap instrumentation in data dependent acquisition mode with or without target mass lists can be used [12, 13]. For non-targeted approaches on the other hand, high resolution instruments with MS/MS capability are literally mandatory. At the end of each lipidomics pipeline is the data processing step, which is becoming more and more important recently, unless a targeted selected reaction monitoring (SRM) approach is chosen. The core business of data processing these days is the automated interpretation of lipid mass spectra by taking into account molecular masses, selective fragment ion masses, and retention times. The method described in the following protocol consists of reversed phase HPLC, LTQ-Orbitrap mass spectrometry, and a custom developed data processing software called Lipid Data Analyzer. All of the described components and their variations have been disseminated previously in several publications [10, 12, 14–17].

2

Materials

2.1 Chemicals and Equipments

2.2

Lipid Extraction

Acetonitrile, isopropanol, methanol, tert-methyl-butyl ether (MTBE) (all Chromasolv grade), and ammonium formate (LC/MS grade) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Chloroform and formic acid were purchased from Merck (Darmstadt, Germany). Deionized water was obtained from an in-house MilliQ Gradient A10 system (Millipore, Billerica, MA, USA). 1. Tert-methyl-butyl ether (MTBE). 2. Methanol. 3. Deionized water. 4. ULTRA-TURRAX homogenizer (Miccra, Germany). 5. Ultrasonic bath (Bandelin, Germany).

2.3

Lipid Standards

LIPID MAPS quantitative lipid standards (Avanti Polar Lipids, Alabaster, AL, USA) 1. 1-Dodecanoyl-2-tridecanoyl-sn-glycero-3-phosphocholine (PC 12:0/13:0). 2. 1-Heptadecanoyl-2-(9Z-tetradecenoyl)-sn-glycero-3-phosphocholine (PC 17:0/14:1(9Z)). 3. 1-Heptadecanoyl-2-(5Z,8Z,11Z,14Z-eicosatetraenoyl)-snglycero-3-phosphocholine (PC17:0/20:4(5Z,8Z,11Z,14Z)). 4. 1-Henicosanoyl-2-(4Z,7Z,10Z,13Z,16Z,19Z-docosahexaenoyl)-sn-glycero-3-phosphocholine (PC 21:0/22:6 (4Z,7Z,10Z,13Z,16Z,19Z)).

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5. 1-Dodecanoyl-2-tridecanoyl-sn-glycero-3-phosphoethanolamine (PE 12:0/13:0). 6. 1-Heptadecanoyl-2-(9Z-tetradecenoyl)-sn-glycero-3-phosphoethanolamine (PE 17:0/14:1(9Z)). 7. 1-Heptadecanoyl, 2-(5Z,8Z,11Z,14Z-eicosatetraenoyl)-snglycero-3-phosphoethanolamine (PE 17:0/20:4 (5Z,8Z,11Z,14Z)). 8. 1-Heneicosanoyl-2-(4Z,7Z,10Z,13Z,16Z,19Z-docosahexaenoyl)-sn-glycero-3-phosphoethanolamine (PE 21:0/22:6 (4Z,7Z,10Z,13Z,16Z,19Z)). 9. 1-Dodecanoyl-2-tridecanoyl-sn-glycero-3-phosphoserine (ammonium salt) (PS 12:0/13:0). 10. 1-Heptadecanoyl-2-(9Z-tetradecenoyl)-sn-glycero-3-phosphoserine (ammonium salt) (PS 17:0/14:1(9Z)). 11. 1-Heptadecanoyl, 2-(5Z,8Z,11Z,14Z-eicosatetraenoyl)-snglycero-3-phosphoserine (ammonium salt) ((PS 17:0/20:4 (5Z,8Z,11Z,14Z)). 12. 1-Heneicosanoyl-2-(4Z,7Z,10Z,13Z,16Z,19Z-docosahexaenoyl)-sn-glycero-3-phosphoserine (ammonium salt) (PS 21:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)). 13. 1-Dodecanoyl-2-tridecanoyl-sn-glycero-3-phospho-(10 -myo-inositol)(ammonium salt) (PI 12:0/13:0). 14. 1-Heptadecanoyl-2-(9Z-tetradecenoyl)-sn-glycero-3-phospho-(10 -myo-inositol) (ammonium salt), (PI 17:0/14:1(9Z)). 15. 1-Heptadecanoyl-2-(5Z,8Z,11Z,14Z-eicosatetraenoyl)-snglycero-3-phospho-(10 -myo-inositol) (ammonium salt) (PI 17:0/20:4(5Z,8Z,11Z,14Z)). 16. 1-Heneicosanoyl-2-(4Z,7Z,10Z,13Z,16Z,19Z-docosahexaenoyl)-sn-glycero-3-phospho-(10 -myo-inositol) (ammonium salt) (PI 21:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)). 17. 1-(10Z-heptadecenoyl)-2-hydroxy-sn-glycero-3-phosphocholine (LPC 17:1(10Z)). 18. d5-TG internal standard mixture I (LM6000). 19. d5-DG internal standard mixture I (LM6001). 20. Ceramide/sphingoid internal standard mixture I (LM6002). 21. Cholesterol (D7). 22. Cholesteryl-nonadecanoate (CE 19:0). Qualitative lipid standards (Avanti Polar Lipids, Alabaster, AL, USA) 23. 1-Heptadecenoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine (LPE 17:1).

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24. 1,2-dilauroyl-sn-glycero-3-phosphoethanolamine (PE 12:0/ 12:0). 25. 1,2-dilauroyl-sn-glycero-3-phospho-L-serine (sodium salt) (PS 12:0/12:0). Lipid standards (Larodan Fine Chemicals AB, Malmo¨, Sweden) 26. Triheptadecanoin (TG 17:0/17:0/17:0/). 27. Natural phosphatidylinositol (bovine, liver). 2.4

HPLC/MS

1. A Dionex Ultimate 3000 RS UHPLC system with Thermo Orbitrap Velos Pro with a HESI II ion source controlled by Xcalibur software. 2. Mobile phases: (a) Mobile phase A: Aqua bidest. Containing 1% of a 1 M aqueous ammonium formate solution and 0.1% formic acid. (b) Mobile phase B: Acetonitrile/2-propanol 5:2 (v/v) containing 1% of a 1 M aqueous ammonium formate solution and 0.1% formic acid. 3. Autosampler wash solution: 2-propanol. 4. Column: Acquity UPLC BEH C8 1.7μm 1.0  100 mm (Waters).

3

Methods

3.1 Preparation of Standard Mixes

Internal standard solutions are diluted according to Table 1 into an internal standard mix containing the required compounds at the indicated concentrations (see Note 1).

3.2

Perform the whole lipid extraction process in glass tubes (e.g., Pyrex) on ice (see Note 2).

Lipid Extraction

1. Add 1.5 mL methanol to about 50 mg liver or 50μL plasma. 2. Homogenize tissue samples with an ULTRA-TURRAX homogenizer. 3. Add 5 mL MTBE. 4. Vortex each sample for 10 s. 5. Sonicate the samples for 10 min in an ultrasonic bath. 6. Mix the samples in an overhead shaker for 10 min. 7. Add 1.25 mL aqua dest. 8. Mix the samples in an overhead shaker for 10 min. 9. Centrifuge for 10 min at 1500  g. 10. Transfer the upper layer into another glass tube (Pyrex).

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Table 1 List of internal standards

Supplier

Product number

Compound

Stock concentration [μM]

Amount per sample added [pmol]

Avanti

LM-1000

PC 12:0/13:0

15.62

112.5

Avanti

LM-1002

PC 17:0/20:4

10.9

112.5

Avanti

LM-1003

PC 21:0/22:6

11.42

112.5

Avanti

LM-1004

PC 17:0/14:1

15.11

112.5

Avanti

LM-1100

PE 12:0/13:0

17.14

180

Avanti

LM-1102

PE 17:0/20:4

14.52

180

Avanti

LM-1103

PE 21:0/22:6

11.93

180

Avanti

LM-1104

PE 17:0/14:1

16.13

180

Avanti

LM-1300

PS 12:0/13:0

15.85

180

Avanti

LM-1302

PS 17:0/20:4

12

180

Avanti

LM-1303

PS 21:0/22:6

11.19

180

Avanti

LM-1304

PS 17:0/14:1

14.28

180

Avanti

LM-1500

PI 12:0/13:0

14.38

180

Avanti

LM-1502

PI 17:0/20:4

11.04

180

Avanti

LM-1503

PI 21:0/22:6

10.49

180

Avanti

LM-1504

PI 17:0/14:1

12.72

180

Avanti

LM-1601

LPC 17:1

19.35

135

Avanti

856707

LPE 17:1

10.74

180

Avanti

LM-6000

TG-Mix

4

112.5

Avanti

LM-6001

DG-Mix

4

180

Avanti

LM-4100

Chol-D7

1336.06

7200

Avanti

LM-4000

CE 19:0

201.1

1350

Avanti

LM-6002

Sphingolipid Mix I

25

135

11. Add 2 mL of a MTBE/methanol/aqua dest. (10:3:2.5 v/v/v) mixture to the remaining lower layer. 12. Mix the samples in an overhead shaker for 10 min. 13. Centrifuge for 10 min at 1500  g. 14. Transfer the upper layer and pool it with the upper layer from the first extraction step. 15. Evaporate the lipid extract in a SpeedVac overnight (see Note 3).

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16. Dissolve the dried lipid extract in 0.5 mL of chloroform/ methanol (1:1 v/v). 17. Quickly vortex the solution and sonicate in an ultrasonic bath for a few seconds. 18. Transfer the lipid extract into a 1.5 mL autosampler vial (see Note 4). 1. Transfer 20μL of the lipid extract into a 0.2 mL autosampler vial.

3.3 Sample Preparation for Positive Ion Electrospray Ionization (ESI)

2. Add the amounts of PC, PE, PS, LPC, LPE, TG, DG, CE, sphingolipids, and cholesterol internal standards as indicated in Table 1. 3. Evaporate in a SpeedVac. 4. Add 100μL 2-propanol/chloroform/methanol (90:5:5 v/v/ v). 5. Quickly vortex each vial and sonicate in an ultrasonic bath for a few seconds. 1. Transfer 50μL of the lipid extract into a 0.2 mL autosampler vial.

3.4 Sample Preparation for Negative Ion ESI Analysis

2. Add the amounts of PI internal standard as indicated in Table 1. 3. Evaporate in a SpeedVac. 4. Add 100μL 2-propanol/chloroform/methanol (90:5:5 v/v/ v). 5. Quickly vortex each vial and sonicate in an ultrasonic bath for a few seconds. 1. Gradient: see Table 2.

3.5 Instrument Settings for LC/MS Analysis

2. Column temperature: 50  C. 3. Injection volume: 2μL.

3.5.1 UHPLC 3.5.2 Mass Spectrometry

1. Source parameters: see Table 3

Table 2 UHPLC gradient Time [min]

Mobile phase A [%]

Mobile phase B [%]

Flow rate [μL/min]

0

50

50

150

40

0

100

150

50

0

100

150

58

50

50

150

High Resolution Lipidomics Profiling

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Table 3 Source parameters of the HESI II ion source Parameter

Positive ESI

Negative ESI

Source voltage [kV]

4.5

3.8

Source temperature [ C]

275

325

Sheath gas [arbitrary units]

25

30

Aux gas [arbitrary units]

9

10

Sweep gas [arbitrary units]

0

0

Capillary temperature [ C]

300

300

2. Automatic Gain Control (AGC) target value: 106. 3. Maximum ion accumulation time: 500 ms. 4. m/z range for positive ion full scan MS1 spectra: 400–1200, 5. m/z range for negative ion full scan MS1 spectra: 200–1600, 6. Resolution setting: 100,000 at m/z 400. 7. MS2 spectra acquisition: The 10 most abundant ions from each MS1 spectrum (survey scan) are collision induced dissociation (CID) fragmented in LTQ in data dependent acquisition (DDA) mode resulting in centroid product ion spectra. 8. Collison gas: Helium. 9. Normalized collision energy: 50%. 10. Isolation width: 1.5 Da. 11. Activation Q: 0.2. 12. Activation time: 10 ms. 13. Scan rate: normal (33 kDa/s). 14. DDA exclusion time: 10 s. 15. Lock Mass: Polysiloxane 536.165370 m/z (only positive ion mode). 3.6

Data Acquisition

1. Perform calibration check and instrument calibration using Pierce™ LTQ Velos ESI Positive Ion Calibration Solution (88323) for positive ionization, and Pierce™ Negative Ion Calibration Solution (88324) for negative ionization before MS analysis. 2. Optimize lens settings and source parameters using a 1μM solution of PE 16:0/18:1 in chloroform/methanol 1:1. 3. Run a Quality Control (QC) sample containing a mixture of TG 15:0/15:0/15:0, PE 12:0/12:0, PS 12:0/12:0 (all for positive ion ESI mode), and a natural liver PI mix (for negative ion ESI mode) for checking if retention times and signal

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intensities are within the expected range (see Note 5) before running the sample sequence. Each acquisition sequence consists of one QC and one extraction blank in the beginning, after every ten samples, and at the end. While the QC samples monitor signal stability throughout the sequence, extraction blanks are used for control of background signals. 3.7

Data Processing

Process data using Lipid Data Analyzer 2.6 (LDA 2.6) software, which can be downloaded at http://genome.tugraz.at/lda2/lda_ download.shtml and is an open source software as outlined in the publication of Hartler et al. [17]. All current functions of LDA 2.6 are summarized in the software manual, which is available under the following link (https://genome.tugraz.at/lda2/2.6/LDA_2.6. pdf). 1. Transfer the raw data (.raw files) to a local drive at the processing computer. 2. Create an excel sheet based target mass list. The list must follow a few conventions: one lipid class per sheet, one column with molecular names, molecular formulas, and the monoisotopic mass of the adducts and optional retention time. The standards we use are marked on the target mass list with the prefix “IS” followed by the name (e.g., IS25:0) shown in Fig. 1. 3. Choose on the menu bar “Settings” the MS device OrbiTrap_velos_pro and in the dropdown menu “fragmentation Selection 1:” +50 in positive mode and 50 in negative mode measured “.raw” files, save the settings with “apply” or “as default” (see Note 6). 4. To process the data go to menu bar “Batch Quantitation.” There you have to complete the path to the “Raw files” and

Fig. 1 Excel based target mass list for LDA 2 with different lipid classes (TG to CE), hydrogen and sodium adduct (column I, J), molecular formula (column D to H), named by numbers of carbon atoms (column A), number of double bounds (dbs) and an optional column for retention time (column K)

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Fig. 2 An example of LDA 2 input mask for threshold cutoff (e.g., 5 per mile) and the added glycerophosphocholine standards (PC 37:4, PC 43:6). The “sample volume” is the volume of the extraction and the “final volume” is the volume of the resuspension with the internal standards and both values are required

to the “Quant. files” where the target mass list is located and “Start Quantitation” (see Note 7). 3.8

Data Analysis

1. Go to the menu bar “Statistical Analysis,” choose the path (add Dir) where the prior calculated raw files are stored. 2. Add cutoff settings to filter out low abundant features respectively false positive noise based features and add the standard concentrations from Table 1 and dilution steps of the used standards to normalize your values with a one point calibration shown in Fig. 2. 3. Before exporting the results, the data should be manually inspected as explained in the next three steps: (a) Missing lipids on the heat map are shown as gray boxes. To search for this lipid species manually, right-click on a lipid found (colored box) and select “Quant. Anal. At not found.” (b) To aid the identification of lipid species and the integration borders, check the homologous series. Export a figure (“chroms” menu button) with chromatograms to visualize the shift in retention time depending on the double bonds (e.g., Fig. 3). If the background of the chromatogram is green, this means that the MS/MS spectrum detected complies with the MS/MS fragmentation rules defined for this lipid class (see Note 8).

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Fig. 3 LDA 2 exported chromatogram of a homologous series to aid identification of lipid species. The behavior of lipids on a reversed phase depends on number of carbon atoms and degree of unsaturation. This shift is illustrated with PC 34:0 to 35:5 where each left shift on the time axis shows the effect of increasing double bonds. Green background shows the matching of the MS/MS information with the predefined lipid class specific rule. Red peak shows peak integration for further calculation

(c) If there are problems with deviations in the homologous series, or if several peaks are detected or in the case of problems with the integration limits in the exported chromatograms, then all these issues can be adjusted under “Display Results.” Using the graphical user interface you can adjust the integration limits in MS 1 view mode and you can control the MS/MS fragmentation pattern and the Fragmentation rules in the “show MS/MS” or “Edit MSn rules” mode. Right-clicking on the lipid species of interest changes the view.

4

Notes 1. The amounts of internal standards might need to be adjusted individually for each sample type. For example, a fatty liver from mice on high fat diet could need higher amounts of internal standard than a liver from mice on normal chow diet. The adaption of internal standard amounts can either happen by knowing what to expect in this sample type by experience, or by simply injecting the lipid amounts to be expected by doing a test measurement of one or two representative samples. The signal intensity of the internal standards should be within the

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intensity range (factor 2–0.5) of the corresponding lipid classes’ base peak. 2. All lipid extractions should be done in glass vials of appropriate size and high quality with a tight screw cap containing a Teflon insert. A typical recommendation would be 10 mL Pyrex glass tubes. 3. If available, we recommend using a SpeedVac for evaporation, because it can take several hours to remove the residual water in the extract. With a SpeedVac, this can be conveniently done overnight. Alternatively, the samples can be evaporated by nitrogen(g), but for big sample batches, it is not recommended for economic reasons (costs of nitrogen), particularly when evaporation to complete dryness takes long hours. 4. The lipid extracts can be stored at 80  C if not processed immediately. 5. When retention times deviate more than half a minute from the expected retention times (retention times of the same QC sample for the last acquisition batch), then it is likely that there are either problems with a plugged injection needle or plugged tubings, or that the HPLC pump is not working properly or even the mobile phase is not mixed well. When signal intensities are lowered by a factor of 3 or even more when compared to the previous QC sample determination, this indicates problems with a plugged injection system or problems with the electrospray. 6. There are several “Settings” and the “fragmentation Selection 1:” option. The correct choice depends on the used MS device (we use an Orbitrap Velos Pro). There are several options available. The correct “Fragmentation Selection” enables the use of a specific set of fragmentation rules depending on your device and the used fragmentation energy. The fragmentation rules as well as the specific device parameters can be changed or extended manually (LDA software Manual). 7. Quantitation is the most time-consuming step which can be optimized depending on your computer system. The changes are all made in the LDA installation folder on your system: (1) The available memory settings can be changed in the .bat file. The value -Xmx corresponds to the maximum reservable memory which can be increased if your system has free space. (2) Increasing translation speed from mzXML to chrom files for big “.raw files” can be achieved with increasing the “maxFileSizeForChromTranslationAtOnce” value in the properties folder. It can be set individually for each device opted. (3) Enable Cuda to speed up the time-consuming Savitzky– Golay smoothing for quantification. Open “.settings” file with

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an editor and change useCuda ¼ true. This file can be hidden depending on your windows folder option. 8. There are several colors to give you a quick overview about the quality of your data. This color code is shown when you export chromatograms and when you check samples in the “Display Results” window. As mentioned above green means MS1 information is verified by MS/MS spectra, white means, that there is no MS/MS information available, yellow means that the algorithm found more than one lipid subclass and a split at the MS1 level is possible, and orange means again that more than one lipid subclass is found but the split on MS1 level is not reliable. For the last two cases, the spectrum should be controlled by the graphical interface on MS/MS level.

Acknowledgments This work was supported by the Austrian Federal Ministry of Education, Science and Research grant number BMWFW10.420/0005-WF/V/3c/2017. References 1. Han XL, Gross RW (1994) Electrosprayionization mass spectroscopic analysis of human erythrocyte plasma-membrane phospholipids. Proc Natl Acad Sci U S A 91 (22):10635–10639. https://doi.org/10. 1073/pnas.91.22.10635 2. Brugger B, Erben G, Sandhoff R, Wieland FT, Lehmann WD (1997) Quantitative analysis of biological membrane lipids at the low picomole level by nano-electrospray ionization tandem mass spectrometry. Proc Natl Acad Sci U S A 94(6):2339–2344. https://doi.org/10.1073/ pnas.94.6.2339 3. Han XL, Gross RW (2003) Global analyses of cellular lipidomes directly from crude extracts of biological samples by ESI mass spectrometry: a bridge to lipidomics. J Lipid Res 44 (6):1071–1079. https://doi.org/10.1194/jlr. R300004-JLR200 4. Ekroos K, Chernushevich IV, Simons K, Shevchenko A (2002) Quantitative profiling of phospholipids by multiple precursor ion scanning on a hybrid quadrupole time-of-flight mass spectrometer. Anal Chem 74(5):941–949 5. Liebisch G, Drobnik W, Reil M, Trumbach B, Arnecke R, Olgemoller B, Roscher A, Schmitz G (1999) Quantitative measurement of different ceramide species from crude cellular extracts by electrospray ionization tandem

mass spectrometry (ESI-MS/MS). J Lipid Res 40(8):1539–1546 6. Holcapek M, Jandera P, Zderadicka P, Hruba L (2003) Characterization of triacylglycerol and diacylglycerol composition of plant oils using high-performance liquid chromatographyatmospheric pressure chemical ionization mass spectrometry. J Chromatogr A 1010 (2):195–215 7. Harrison KA, Davies SS, Marathe GK, McIntyre T, Prescott S, Reddy KM, Falck JR, Murphy RC (2000) Analysis of oxidized glycerophosphocholine lipids using electrospray ionization mass spectrometry and microderivatization techniques. J Mass Spectrom 35 (2):224–236 8. Cifkova E, Holcapek M, Lisa M, Ovcacikova M, Lycka A, Lynen F, Sandra P (2012) Nontargeted quantitation of lipid classes using hydrophilic interaction liquid chromatography-electrospray ionization mass spectrometry with single internal standard and response factor approach. Anal Chem 84 (22):10064–10070. https://doi.org/10. 1021/ac3024476 9. Scherer M, Schmitz G, Liebisch G (2009) High-throughput analysis of sphingosine 1-phosphate, sphinganine 1-phosphate, and lysophosphatidic acid in plasma samples by

High Resolution Lipidomics Profiling liquid chromatography-tandem mass spectrometry. Clin Chem 55(6):1218–1222. https://doi.org/10.1373/clinchem.2008. 113779 10. Fauland A, Kofeler H, Trotzmuller M, Knopf A, Hartler J, Eberl A, Chitraju C, Lankmayr E, Spener F (2011) A comprehensive method for lipid profiling by liquid chromatography-ion cyclotron resonance mass spectrometry. J Lipid Res 52 (12):2314–2322 11. Hein EM, Blank LM, Heyland J, Baumbach JI, Schmid A, Hayen H (2009) Glycerophospholipid profiling by high-performance liquid chromatography/mass spectrometry using exact mass measurements and multi-stage mass spectrometric fragmentation experiments in parallel. Rapid Commun Mass Spectrom 23 (11):1636–1646 12. Triebl A, Trotzmuller M, Hartler J, Stojakovic T, Kofeler HC (2017) Lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry and its application to complex biological samples. J Chromatogr B Analyt Technol Biomed Life Sci 1053:72–80. https://doi.org/10.1016/j.jchromb.2017. 03.027 13. Knittelfelder OL, Weberhofer BP, Eichmann TO, Kohlwein SD, Rechberger GN (2014) A versatile ultra-high performance LC-MS method for lipid profiling. J Chromatogr B

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Anal Technol Biomed Life Sci 951-952:119–128. https://doi.org/10. 1016/j.jchromb.2014.01.011 14. Hartler J, Trotzmuller M, Chitraju C, Spener F, Kofeler HC, Thallinger GG (2011) Lipid data analyzer: unattended identification and quantitation of lipids in LC-MS data. Bioinformatics 27(4):572–577 15. Triebl A, Trotzmuller M, Eberl A, Hanel P, Hartler J, Kofeler HC (2014) Quantitation of phosphatidic acid and lysophosphatidic acid molecular species using hydrophilic interaction liquid chromatography coupled to electrospray ionization high resolution mass spectrometry. J Chromatogr A 1347:104–110 16. Sala P, Potz S, Brunner M, Trotzmuller M, Fauland A, Triebl A, Hartler J, Lankmayr E, Kofeler HC (2015) Determination of oxidized phosphatidylcholines by hydrophilic interaction liquid chromatography coupled to fourier transform mass spectrometry. Int J Mol Sci 16 (4):8351–8363 17. Hartler J, Triebl A, Ziegl A, Trotzmuller M, Rechberger GN, Zeleznik OA, Zierler KA, Torta F, Cazenave-Gassiot A, Wenk MR, Fauland A, Wheelock CE, Armando AM, Quehenberger O, Zhang Q, Wakelam MJO, Haemmerle G, Spener F, Kofeler HC, Thallinger GG (2017) Deciphering lipid structures based on platform-independent decision rules. Nat Methods 14(12):1171–1174. https://doi. org/10.1038/nmeth.4470

Chapter 4 Comprehensive Structural Characterization of Lipids by Coupling Paterno`–Bu¨chi Reaction and Tandem Mass Spectrometry Qingyuan Hu, Yu Xia, and Xiaoxiao Ma Abstract Since the invention of soft ionization methods, in particular electrospray ionization (ESI), mass spectrometry (MS) has become the method of choice for both qualitative and quantitative analysis of lipids from complex samples. A large number of lipids can be readily detected from a single mass spectrum free from molecular fragmentation that may complicate spectral interpretation. This has been the driving force for MS to play a predominant role in lipidomics. However, elucidation of the detailed lipid structures, especially the location of carbon-carbon double bond (C¼C), remains challenging for MS-based lipid analysis workflows. Here we describe the coupling of photochemical derivatization of C¼C via Paterno`–Bu¨chi (PB) reaction with tandem mass spectrometry (MS/MS) to identify C¼C locations in unsaturated lipids and quantify lipid C¼C location isomers. The PB reaction can be conducted online in ~30 s, which transforms a C¼C into the oxetane ring structure. Subjecting PB products of lipids to MS/MS leads to the formation of abundant C¼C-specific fragment ions upon low energy collision-induced dissociation. Key word Lipid, Tandem mass spectrometry, Photochemical derivation, Carbon-carbon double bond, Structural elucidation

1

Introduction Mass spectrometry (MS) has now become the driving force for lipidomics. However, detailed characterization of lipid structures, in particular the C¼C location that is critical to lipid functions, remains a challenge to MS [1]. Although high-resolution mass spectrometry and tandem mass spectrometry (MS/MS) can help to resolve lipid structures, they are not amenable to lipid C¼C location isomers, which have exactly the same molecular formula and therefore the same molecular mass. No differences in the MS/MS fragmentation patterns of lipid isomers that differ only in the C¼C location are discernible, as the C¼C cannot be cleaved by low energy collision-induced dissociation (CID) due to its non-polar nature and high dissociation energy [2].

Fong-Fu Hsu (ed.), Mass Spectrometry-Based Lipidomics: Methods and Protocols, Methods in Molecular Biology, vol. 2306, https://doi.org/10.1007/978-1-0716-1410-5_4, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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A variety of chemical reactions have been explored to derivatize the C¼C either for direct cleavage or making it more susceptible to fragmentation by CID [3–6]. The most successful method for C¼C localization in lipids is C¼C cleavage via ozonolysis. Later in order to enable lipid mixture analysis, ozone-induced dissociation (OzID) was developed to implement ozonolysis of mass-selected ions inside the mass spectrometer after instrument modification [7]. Electron impact excitation of ions from organics (EIEIO) [8] and ultraviolet photodissociation (UVPD) [9] have also been demonstrated to be capable to locate the C¼C in lipids. Over the past 6 years, we have demonstrated that the Paterno`– Bu¨chi (PB) reaction coupled with tandem MS (PB-MS/MS) allows large-scale structural characterization and quantitative analysis of lipids, to enable “deep lipidomics” and drive a wide range of biomedical applications [10–12]. The PB-MS/MS method is fast, applicable to multiple classes of lipids, and compatible with both shotgun lipidomics and liquid chromatography-MS (LC-MS) [13, 14]. Moreover, the resulting MS/MS spectrum is fairly easy to interpret as the C¼C-derived oxetane is easily fragmentable. Besides C¼C localization, by a systematic screening of a variety of PB reagents we have achieved near-complete structure elucidation of phospholipids by using 2-acetylpyridine as the derivatizing reagent [12]. Both the C¼C locations and sn-positions of fatty acyls can be simultaneously acquired from a single MS/MS spectrum. It is worth mentioning that all PB-MS/MS analyses can be performed on common commercial mass spectrometers with tandem MS capability, with no technical barrier for any research group to use it. We believe the PB-MS/MS methodology has the potential to completely characterize all levels of lipid structures, including C¼C geometry (cis/trans) that is by now still an analytical challenge.

2

Materials Prepare all solutions using ultrapure water and analytical grade reagents. Prepare and store all reagents at room temperature (unless indicated otherwise). Carefully follow all waste disposal regulations when disposing waste materials. 1. Cell line for culture: RWPE1, PC3, MCF-7, SK-BR-3, MDA-MB-468, and BT-474. 2. Lipid standards: 100 μM each methanolic FA 18:1 (9Z), PC 16:0/18:1(9Z), PC 18:1(9Z)/16:0 solution. 3. Lipid samples: extracted lipids, cells cultured on petri dish, and frozen tissues.

Analysis of C¼C in Unsaturated Lipids

55

4. Trypsin solution: 0.25% trypsin in phosphate buffer saline (PBS), stored at 4  C. 5. Dulbecco’s modification of Eagle’s medium (DMEM). 6. 50% Acetone (by volume) in water containing 0.1% acetic acid (positive-ion mode), 50% acetone in water containing 1% NH4OH (negative-ion mode). 7. Acetonitrile/water 50/50 (v/v) containing 2-acetylpyridine, and 100 μM sodium acetate.

1

mM

8. 500 μM Acetophenone in ACN: H2O (1:1 by volume) containing 1% 50 mM LiCl (positive-ion mode), 500 μM acetophenone in ACN: H2O (1:1 by volume) containing 1% NH4OH (negative-ion mode). 9. 500 μM Benzophenone in ACN: H2O (1:1 by volume) containing 1% 50 mM LiCl (positive-ion mode), 500 μM benzophenone in ACN: H2O (1:1 by volume) containing 1% NH4OH (negative-ion mode) [15]. 10. Low-pressure mercury lamp with a major emission band at 254 nm (BHK Inc., Ontario, CA, USA).

3

Methods The following procedures are carried out at room temperature unless otherwise specified.

3.1 Cell Sample Preparation

1. Remove all culture medium in the petri dish and wash the dish by 2.5 mL PBS gently. 2. Remove PBS, add 1 mL trypsin solution and 2.5 mL DMEM to the petri dish. 3. Incubate the petri dish in 37  C incubator for 5 min. 4. Collect all solution from the petri dish and centrifuge it at 200  g for 1 min. 5. Remove the supernatant and resuspend the cells in 1 mL methanol and 1 mL water as the sample solution.

3.2 Tissue Sample Preparation

1. Cut 50 mg tissue and put it in a test tube, then add 300 μL PBS. 2. Homogenize the solution using a hand-held homogenizer, then add 1 mL methanol and 1 mL water.

3.3

Lipid Extraction

1. Add 2 mL chloroform to the sample solution prepared as above, then mix them well until there is no visible layers. 2. Centrifuge the solution at 7200  g for 8 min, then extract the subnatant as solution B. The solution left is solution A.

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3. Add 2 mL chloroform to solution A and mix them well. Centrifuge the mixture at 7200  g for 8 min, then combine the subnatant with solution B. 4. Repeat step 3. 5. Dry Solution B under N2 flow, then add 1 mL methanol, mix them well and transfer the solution to another test tube. 6. Centrifuge the solution in the test tube at 5000  g for 5 min, then collect the supernatant as the lipid solution. 3.4 PB Reaction and MS Analysis

1. Mix 100 μL lipid solution with 900 μL PB reagent solution (one item from items 4–7 in Subheading 2). 2. Purge the solution with N2. The flow rate of N2 should be slow to adequately remove trace O2 from the solvent system and prevent solvent evaporation. 3. Prepare nanoESI tips of ~10 μm of outer diameter by pulling borosilicate glass capillary tips (1.5 mm o.d. and 0.86 mm i.d.) using a micropipette puller. 4. Load 5 μL sample solution into a nanoESI tip, and align it with the sampling orifice of the MS. The distance between the nanoESI tip and the MS orifice is 5–10 mm. 5. Set a low-pressure mercury lamp in a vertical direction, orthogonal to the nanoESI tip. The distance between the lamp and the nanoESI tip is 0.5–1.0 cm (Fig. 1a). 6. Turn on the lamp and start MS analysis sequence. Apply a voltage of 1500–1800 V for MS analysis in the positive mode and 1800 to 1500 V for MS analysis in the negative-ion mode to the solution in the nanoESI tip via a platinum or stainless-steel wire. 7. FA 18:1(9Z) is used as an example to explain the C¼C location identification process (Fig. 1b, c). First, the PB reaction products at m/z 339.4 are isolated. These ions have a mass increase of 58 Da compared to intact FA 18:1(9Z) at m/z 281.3. The isolated ions are then analyzed by MS/MS via collisioninduced dissociation (CID) using a collision energy (CE) between 25 and 40 V. 8. Find the diagnosis ions. For a certain class of unsaturated lipids, a pair of diagnostic ions should have a mass difference of 26 Da when acetone is used as the PB reagent. The diagnostic ions of FA 18:1(9Z) are at m/z 171.1 and 197.2. The mass-to-charge ratios of diagnostic ions are used to assign the C¼C location in FA 18:1(9Z). When PB reagents other than acetone are used, the mass difference may be different.

Analysis of C¼C in Unsaturated Lipids

a)

~254 nm UV lamp

10 µM oleic acid in acetone/water (50/50, v/v) added with 1% (v) NH4OH

57

inlet of mass spectrometer D2: 1.5-2.5 cm

nanoESI tip

HV b)

D1: 0.5-1.0 cm Voltage: ±1500-1800 V c)

oleic acid 281.3

100

339.3

100 +58 Da 339.4

281.3

I 171.1 0

200

240 280 m/z

320

360

0

100

197.2

180

260 m/z

58 Da 340

420

Fig. 1 (a) Experimental setup for PB derivation of lipids using acetone. (b) PB reaction mass spectrum of FA 18:1(9Z) in negative mode. (c) MS/MS spectrum of PB reaction product at m/z 339.4. Figure adapted from Ref. [10] with permission from Wiley

3.5 PB Derivatization Using 2-Acetylpyridine for Determining Both C¼C Locations and sn-Position of Fatty Acyls in Phospholipids

1. Prepare a lipid solution in 1 mM 2-acetylpyridine solution at a molar ratio of 1:100 (lipid:2-acetylpyridine) (see Notes 1 and 2). 2. Purge the solution with N2 (see Note 3). 3. Prepare nanoESI tips of ~10 μm in outer diameter. 4. Load 5 μL sample solution into a nanoESI tip, and align it with the sampling orifice of the MS. The distance between the nanoESI tip and the MS orifice is 5 and 10 mm. 5. Set a low-pressure mercury lamp (major emission band at 254 nm) in a vertical direction, orthogonal to the nanoESI tip. The distance between the lamp and the nanoESI tip is 0.5–1.0 cm (see Notes 4–6). 6. Turn on the lamp and start MS analysis sequence. The PB products after derivatization by 2-acetylpyridine are 183 Da higher than the intact lipid (Fig. 2a, c) (see Note 7). 7. Apply CID to PB products at m/z 903. From a single MS/MS spectrum, both C¼C- and sn-specific diagnostic ions are detected, labeled in red and blue, respectively. The underlying reason that this method allows the determination of sn-positions of fatty acyls is as follows: CID of sodiated PB products leads to 183 Da loss, resulting in formation of a five-membered

Qingyuan Hu et al.

PC 16:0/18:1(9Z)

PC 16:0/18:1(9Z)

a

x 10.0 No PB [C18:1 +Na]+ 305 599.5 782.6 [C16:0 +Na]+ sn 599.5 279 319 sn-1 319 sn-2 345 200 300 400 543.5 100 200 300 400 500 600 700 800 900 1000 m/z

c

PC 16:0/18:1(9Z) With PB

903.6

sn [PBC18:1 +Na]+ 720.6 396426 408 489 720.6 466 sn-1 396 599.5 sn-2 578 380 380 489 578 200 300 400 500 600 700 800 900 1000 m/z

[I396 + I380 ] / [I396 + I380+ I466] (%)

e

b Rel. abundance(%)

58

sn-position ions

C=C specific ions

100

50

0

PC 18:1(9Z)/16:0

d

With PB [PBC18:1

466 sn 426

+Na]+

720.6

489

408

903.6 720.6

sn-1466

599.5 578

489 578 sn-2 200 300 400 500 600 700 800 900 1000 m/z 396

f isomer 1 isomer 3

100 PC(40:4) PC(40:5) PC(38:3) PC(38:4) PC(36:1) PC(36:2) PC(36:3) PC(36:4) PC(34:1) PC(34:2) PC(32:0)

y = 1.0398x - 2.3111 80 R² = 0.9974 60 40 20 0 0

20

40

60

isomer 2 isomer 4

80

PC 16:0/18:1(9Z) from PLA2 digest

100

0

20

40

60

80

100

(%)

Fig. 2 Near-complete lipid structure analysis, including C¼C location and sn-positions of fatty acyls, via 2-acetylpyridine derivatization and tandem MS analysis. (a) MS3 spectrum of PC 16:0/18:1(9Z) without PB derivatization. The dioxolane at m/z 599.5 formed after headgroup loss (183 Da) is shown as the inset. The dioxolane was further fragmented via MS3 to release sn-1-specific diagnostic ions at m/z 319 (C16:0) and 345 (C18:1), both of which were at very low abundances. (b) PB reagent screening. (c, d) Analysis of a pair of phospholipid sn-position isomers, i.e., (c) PC 16:0/18:1(9Z) and (d) PC 18:1(9Z)/16:0, using the developed method. (e) Correlation between the PLA2 digestion and PB-MS3 methods for the relative quantitation of PC 16:0_18:1 sn-isomers. Error bar represents the standard deviation, n ¼ 3. (f) The relative composition of snisomers of GPs in bovine liver polar extract. Figure adapted from Ref. [12] with permission from SpringerNature

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dioxolane ring that can be used to resolve sn-1 and sn-2 fatty acyls upon MS3 analysis (Fig. 2c, d) (see Note 8). 8. As shown in Fig. 2b, PB products from 2-acetylpyridine offer C¼C- and sn-specific diagnostic ions of high abundance after CID. The mass-to-charge ratios and relative abundances of diagnostic ions enable structural characterization and relative quantitation of lipid structure isomers (Fig. 2e, f), similar to the principle for the structural elucidation and relative quantitation of lipid C¼C location isomers (see Note 9).

4

Notes 1. 100 μM Sodium acetate is added to lipid solutions to maximize the formation of sodium adducts. 2. The composition of PB reagent solution should be adjusted to achieve good solubility of the lipid class of interests. For example, for the analysis of cholesteryl ester (CE), 40% acetone, 30% methanol, 20% dichloromethane, and 10% water (by volume) added with 100 μM LiOH is demonstrated to be a good solvent condition [16]. 3. Incomplete purge of the solvent system may result in rapid reactions between lipid radicals and O2 upon UV irradiation, leading to the generation of lipid peroxidation products. This will significantly reduce the signals of both intact lipids and their PB products, and a complete O2 removal from the lipid solution is therefore necessary. 4. The flow microreactor should be considered when precise control of the reaction time is needed or when PB reaction is to be coupled with the LC-MS workflow. The flow microreactor uses UV-transparent fused silica capillary (363 μm o.d., 100 μm i.d.) as the flow path for PB reaction. The capillary can be either coiled (~3 cm in diameter) concentrically with the UV lamp or placed in parallel with it at 0.5 cm distance, according to the reaction time and flow rate. 5. UV light is a safety hazard, and all UV-involved experiments should be carefully performed. Moreover, the reaction zone should be well enclosed to prevent leakage of UV irradiation. 6. Besides acetone, other carbonyl compounds such as acetylpyridine, acetophenone, and benzophenone can also be used as PB reactants. The solvent conditions should thereby be optimized to make both the PB reagent and lipid(s) of interests dissolve well to promote PB reactions. 7. Note that both intact lipids and their PB products are sodiated in order for the PB-MS3 method (using 2-acetylpyrdine as the PB reagent) to work.

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8. The unexpected ions in Fig. 2c, d e.g., m/z 466 in Fig. 2c and m/z 396 in Fig. 2d, are due to impurity in the lipid standards. 9. The relative quantitation of lipid C¼C location isomers is achieved by comparing the relative intensities of MS/MS diagnostic ions between corresponding lipid C¼C location or snposition isomers. References 1. Porta Siegel T, Ekroos K, Ellis SR (2019) Reshaping lipid biochemistry by pushing barriers in structural lipidomics. Angew Chem Int Ed Engl 58(20):6492–6501. https://doi.org/ 10.1002/anie.201812698 2. Rustam YH, Reid GE (2018) Analytical challenges and recent advances in mass spectrometry based lipidomics. Anal Chem 90 (1):374–397. https://doi.org/10.1021/acs. analchem.7b04836 3. Blomquist GJ, Howard RW, McDaniel CA, Remaley S, Dwyer LA, Nelson DR (1980) Application of methoxymercurationdemercuration followed by mass spectrometry as a convenient microanalytical technique for double-bond location in insect-derived alkenes. J Chem Ecol 6(1):257–269. https:// doi.org/10.1007/bf00987544 4. Buser HR, Arn H, Guerin P, Rauscher S (1983) Determination of double bond position in mono-unsaturated acetates by mass spectrometry of dimethyl disulfide adducts. Anal Chem 55(6):818–822. https://doi.org/ 10.1021/ac00257a003 5. Cervilla M, Puzo G (1983) Determination of double bond position in monounsaturated fatty acids by mass-analyzed ion-kinetic-energy spectrometry/collision-induced dissociation after chemical ionization of their amino alcohol derivatives. Anal Chem 55(13):2100–2103. https://doi.org/10.1021/ac00263a022 6. Ellis SR, Hughes JR, Mitchell TW, Mih P, Blanksby SJ (2012) Using ambient ozone for assignment of double bond position in unsaturated lipids. Analyst 137(5):1100–1110. https://doi.org/10.1039/C1AN15864C 7. Thomas MC, Mitchell TW, Harman DG, Deeley JM, Nealon JR, Blanksby SJ (2008) Ozoneinduced dissociation: elucidation of double bond position within mass-selected lipid ions. Anal Chem 80(1):303–311. https://doi.org/ 10.1021/ac7017684 8. Campbell JL, Baba T (2015) Near-complete structural characterization of phosphatidylcholines using electron impact excitation of ions from organics. Anal Chem 87(11):5837–5845. https://doi.org/10.1021/acs.analchem. 5b01460

9. Klein DR, Brodbelt JS (2017) Structural characterization of phosphatidylcholines using 193 nm ultraviolet photodissociation mass spectrometry. Anal Chem 89(3):1516–1522. https://doi.org/10.1021/acs.analchem. 6b03353 10. Ma X, Xia Y (2014) Pinpointing double bonds in lipids by Paterno-Buchi reactions and mass spectrometry. Angew Chem Int Ed Engl 53 (10):2592–2596. https://doi.org/10.1002/ anie.201310699 11. Tang F, Guo C, Ma X, Zhang J, Su Y, Tian R, Shi R, Xia Y, Wang X, Ouyang Z (2018) Rapid in situ profiling of lipid C horizontal lineC location isomers in tissue using ambient mass spectrometry with photochemical reactions. Anal Chem 90(9):5612–5619. https://doi. org/10.1021/acs.analchem.7b04675 12. Cao W, Cheng S, Yang J, Feng J, Zhang W, Li Z, Chen Q, Xia Y, Ouyang Z, Ma X (2020) Large-scale lipid analysis with C¼C location and sn-position isomer resolving power. Nat Commun 11(1):375. https://doi.org/10. 1038/s41467-019-14180-4 13. Ma X, Chong L, Tian R, Shi R, Hu TY, Ouyang Z, Xia Y (2016) Identification and quantitation of lipid C¼C location isomers: a shotgun lipidomics approach enabled by photochemical reaction. Proc Natl Acad Sci U S A 113(10):2573–2578. https://doi.org/10. 1073/pnas.1523356113 14. Zhang W, Zhang D, Chen Q, Wu J, Ouyang Z, Xia Y (2019) Online photochemical derivatization enables comprehensive mass spectrometric analysis of unsaturated phospholipid isomers. Nat Commun 10(1):79. https://doi.org/10. 1038/s41467-018-07963-8 15. Xu T, Pi Z, Song F, Liu S, Liu Z (2018) Benzophenone used as the photochemical reagent for pinpointing C¼C locations in unsaturated lipids through shotgun and liquid chromatography-mass spectrometry approaches. Anal Chim Acta 1028:32–44. https://doi.org/10.1016/j.aca.2018.04.046 16. Ren J, Franklin ET, Xia Y (2017) Uncovering structural diversity of unsaturated fatty acyls in cholesteryl esters via photochemical reaction and tandem mass spectrometry. J Am Soc Mass Spectrom 28(7):1432–1441. https:// doi.org/10.1021/jasms.8b05584

Chapter 5 Chemical Derivatization-Aided High Resolution Mass Spectrometry for Shotgun Lipidome Analysis Vinzenz Hofferek, Huaqi Su, and Gavin E. Reid Abstract Chemical derivatization coupled with nano-electrospray ionization (nESI) and ultra-high resolution accurate mass spectrometry (UHRAMS) is an established approach to overcome isobaric and isomeric mass interference limitations, and improve the analytical performance, of direct-infusion (i.e., “shotgun”) lipidome analysis strategies for “sum composition” level identification and quantification of individual lipid species from within complex mixtures. Here, we describe a protocol for sequential functional group selective derivatization of aminophospholipids and O-alk-10 -enyl (i.e., plasmalogen) lipids, that when integrated into a shotgun lipidomics workflow involving deuterium-labeled internal lipid standard addition, monophasic lipid extraction, and nESI-UHRAMS analysis, enables the routine identification and quantification of >500 individual lipid species at the “sum composition” level, across four lipid categories and from >30 lipid classes and subclasses. Key words Lipidomics, Chemical derivatization, Direct infusion, High resolution mass spectrometry, Deuterated lipid internal standard, Semi-quantitative analysis

1

Introduction The field of lipidomics aims to provide functional insights into the roles that lipids play in the regulation of cellular homeostasis, and to understand their dysregulation in mammalian diseases such as cancer, diabetes, cardiovascular, and neurodegeneration [1–7]. Lipidomics can also serve as a valuable technique in other research disciplines, e.g., for elucidating the role of lipid membrane alterations in the development of antimicrobial resistance [8], for microbiological diagnostics [9], as well as in plant biology [10, 11]. Each of these endeavors rely on the application of analytical strategies, particularly those employing mass spectrometry (MS), to comprehensively identify, characterize, and quantify the lipid compositions within a given sample [12, 13], at the category, class, subclass, or individual molecular species levels [14–16].

Fong-Fu Hsu (ed.), Mass Spectrometry-Based Lipidomics: Methods and Protocols, Methods in Molecular Biology, vol. 2306, https://doi.org/10.1007/978-1-0716-1410-5_5, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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With the exception of in vivo lipid analysis strategies, MS-based lipidome analysis methods begin with extraction of all, or a subset of, the lipids from a given sample of interest. A variety of organic solvent based lipid extraction methods have been developed for this purpose, including both biphasic and monophasic methods, each having their own advantages and disadvantages [17–21]. Regardless of the method however, when working at room temperature the extraction method of choice must be robust, and relatively fast to minimize lipids oxidation, degradation, and isomerization. For semi-quantitative or quantitative analysis, a series of internal lipid standards (typically deuterated) at known concentrations are typically added to the sample prior to lipid extraction [22]. Electrospray ionization (ESI) or nano-ESI coupled with ultrahigh resolution accurate mass spectrometry (UHRAMS) [12, 13, 23–26], and used in combination with online or offline chromatographic separation techniques [27, 28], are increasingly used for lipidome analysis. The use of UHRAMS overcomes limitations associated with isobaric mass overlap that is inherent to low resolution MS analysis methods [25]. Limitations do remain, however, for UHRAMS analysis of multiple types of isomeric lipid ion species [25, 29, 30], including (1) those from different classes but with the same ionic elemental compositions (e.g., PC(30:1) + H+ ¼ PE(33:1) + H+ ¼ PA(35:2) + NH4+, or PS(36:1) + H+ ¼ PG(36:3) + NH4+ in positive ionization mode and PC(34:2) + acetate ¼ PS(38:1)-H, or PC(34:2) + formate ¼ PS(37:1)-H in negative ionization mode), (2) those from the same class but different subclass (e.g., PC(O-31:1) + H+ ¼ PC(P-31:0) + H+), (3) molecular lipid species from within the same class, subclass, and sum composition, but differing in acyl chain compositions (e.g., PE(18:2/ + + 20:4) + H ¼ PE(16:0/22:6) + H ), (4) molecular lipid species within the same class, subclass, sum composition, and acyl chain compositions, but differing in (A) sn-1 and sn-2 regiochemistry (e.g., PI(18:0/20:4) + NH4+ ¼ PI(20:4/18:0) + NH4+) and/or (B) head group regiochemistry (e.g., PIP2[30 ,40 ](18:0/ + + 0 0 20:4) + NH4 ¼ PIP2[3 ,5 ](18:0/20:4) + NH4 ), and (5) structurally defined molecular lipid species differing in their sites of unsaturation and/or stereochemistry (e.g., PE(16:0/18:1 + + + + H ¼ PE + H ¼ PC (16:0/18:1(Δ11E)) (16:0/18:1(Δ9E)) + H ) (Δ9Z)) [25]. Off- or online chromatographic separation using either Reversed-Phase (RP) or Hydrophilic Interaction Liquid Chromatography (HILIC) may be used to spatially separate some of these isomeric lipid types prior to introduction to the mass spectrometer [27, 28]. However, ionization-matrix effects resulting from the elution of internal lipid standards at different retention times to their corresponding endogenous lipids may compromise quantitative accuracy, while relatively long separation times may reduce sample throughput. On the other hand, “shotgun” direct-infusion

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techniques, as described herein, usually require shorter acquisition times (e.g., only 1–2 min per sample) while ensuring that endogenous lipids and their corresponding internal standards are exposed to common matrix effects for improved lipid quantitation (assuming that “global” ion suppression effects are controlled by maintaining appropriate maximum total sample concentrations during analysis) [12, 13, 21]. However, direct-infusion workflows may not resolve the significant number of isomeric lipid overlaps that will be observed when directly analyzing crude complex lipid extracts. As a means to overcome these limitations of UHRAM analysis methods for the type (1) and (2) isomeric mass lipid overlaps defined above, chemical-derivatization methods may be employed [24, 25, 31]. For example, we have previously reported that derivatization of free amino groups within the aminophospholipids phosphatidylethanolamine (PE) and phosphatidylserine (PS) with a 13C1–S,S0 -dimethylthiobutanoyl-N-hydroxysuccinimide ester (13C1–DMBNHS) reagent results in the addition of 131.0487 Da, and therefore separation of these species during mass analysis from potentially isomeric PC, PA, and PG lipid ions (Scheme 1a) [24, 25]. Furthermore, derivatization of O-alk-10 -enyl (i.e., plasmalogen)-containing lipids using iodine and methanol results in the mass addition of 157.9229 Da, thereby resolving them from potentially isomeric alkyl-ether containing lipids (Scheme 1b) [25, 31]. These derivatization methods not only directly enable “sum composition” level lipid identification using UHRAMS, but further improve the ionization sensitivity of DMBNHS derivatized lipid species [24, 25] due to the introduction of a “fixed charge” sulfonium ion, as well as enhancing MS/MS fragmentation of DMBNHS and/or I2/MeOH derivatized lipids for improved structural characterization [24, 25, 31], and also enable multiplexed quantitation when using isotopomeric variants of the DMBNHS reagent [32]. Similar approaches have also been reported by others [33–35]. In this report, we outline the steps involved in a chemical derivatization-aided workflow for UHRAMS shotgun lipidome analysis, involving (1) tissue homogenization/cell lysis, (2) lipid internal standard addition, (3) monophasic “global” extraction of both polar and non-polar lipids, and (4) functional group selective derivatization reactions, with subsequent analysis using nano-ESI “shotgun” infusion and ultra-high resolution accurate mass spectrometry analysis. “Sum composition” level lipid identification and quantification is performed by automated database searching [26, 36, 37], while further lipid identification and quantification at the “molecular lipid” or “structurally defined molecular lipid” levels may be achieved by using “targeted” HCD and/or UVPDMS/MS and -MSn data acquisition and analysis.

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A

O O P O

O

NH2

O

N

O

O

OH

O

S

3

+

O

OH R2

13CH

O

13C-DMBNHS

Aminophospholipids (i.e., PE and PS) O H N

P O

O

+ S

O OH

R2

O

13CH

O O

OH

3

+131.0495 Da

B R1'

O

O O

Plasmalogen lipids

R3

I I2 and MeOH

R1' R2

OCH3 O

+157.9229 Da

O O

R3

R3

Scheme 1 Schematic overview of the reactions for aminophospholipid derivatization using a) 13C1–S, S0 -dimethylthiobutanoyl-N-hydroxysuccinimide ester (13C1–DMBNHS) (addition of 131.0487 Da) and b) for O-alk-10 -enyl (i.e., plasmalogen)-containing lipid derivatization using iodine and methanol (addition of 157.9229 Da). These reactions may be performed individually, or sequentially. For sequential derivation reactions, plasmalogen-containing aminophospholipids will undergo the combined addition of 288.9716 Da

2

Materials Conscientiously adhere to all waste disposal and PPE regulations, especially when handling toxic organic solvents and biological materials. All steps should be carried out using a fume hood.

2.1

Lipid Extraction

1. 0.1% [w/v] Butylated hydroxytoluene (BHT) in MS-grade methanol (MeOH) (10 BHT). Add 10 mg crystalline BHT to 10 mL MeOH and vortex until dissolved (see Note 1). 2. 0.1% [w/v] BHT in HPLC-grade chloroform (CHCl3) (10 BHT). Add 10 mg crystalline BHT to 10 mL CHCl3 and vortex until dissolved. 3. 60% [v/v] MeOH in Ultrapure water (MilliQ or HPLC grade) containing 0.01% [w/v] BHT. Mix 4 mL water with 5 mL MeOH and 1 mL 10 BHT in MeOH.

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4. MeOH containing 0.01% [w/v] BHT. Add 1 mL 10 BHT in MeOH to 9 mL MeOH. 5. CHCl3 containing 0.01% [w/v] BHT. Add 1 mL 10 BHT in CHCl3 to 9 mL CHCl3. 6. CHCl3:MeOH 1:2 [v/v], containing 0.01% [w/v] BHT. Mix 1 part CHCl3 with 0.7 parts MeOH and 0.3 parts 10 BHT in MeOH. 7. MS-grade isopropanol (IPA):MeOH:CHCl3 4:2:1, [v/v/v], containing 0.01% [w/v] BHT. Mix 4 parts IPA with 2 parts MeOH, 0.3 parts CHCl3, and 0.7 parts 10 BHT in CHCl3. 8. Sample-specific multiple-class deuterated lipid standard mixtures (see Note 2). The concentrations and compositions of the lipid standard mixtures depend on the biological sample of interest. To make up 1 mL of a sample-specific deuterated internal lipid standard mixture, refer to Table 1. Nomenclature details can be found in Rustam and Reid [13]. Add the listed volumes of standard lipids from the shipped stocks (available dissolved in CHCl3 from Avanti Polar Lipids). Dry the mixture under a stream of nitrogen, then reconstitute in 1 mL CHCl3 containing 0.01% [w/v] BHT. 2.2 Lipid Derivatization

1. 2:1 [v/v] CHCl3:MeOH. Mix 2 parts CHCl3 with 1 part MeOH. 2. 90 mM Ammonium bicarbonate (NH4HCO3) in MeOH. Dissolve 71.2 mg NH4HCO3 in 10 mL MeOH. 3. 2.5 mM S,S0 -dimethylthiobutanoylhydroxysuccinimide ester iodide (13C1-DMBNHS) in Dimethylformamide (DMF). Dissolve 4.87 mg 13C1-DMBNHS in 5.0 mL DMF. Synthesis of this amine-specific lipid derivatization reagent can be achieved following the protocol described in Zhou et al. [38] and Nie et al. [32]. Alternatively, other commercially available aminespecific reagents may be employed, including the deuterated 4 (dimethylamino)-benzoic acid N-hydroxysuccinimide ester (DMABA) reagents described by Zemski Berry et al. [34] and available from Avanti Polar Lipids, or the Tandem Mass Tag (TMT) reagents as reported by Tokuoka et al. [35]. 4. 2.5 mM TEA in CHCl3. Mix 9.97 mL CHCl3 with 3.4μL TEA. 5. 3.94 mM Iodine (I2) in CHCl3. Dissolve 10 mg I2 in 10 mL CHCl3. 6. 39:1:1.1 [v/v/v] CHCl3:2.5 mM Triethylamine (TEA) in CHCl3:2.5 mM 13C1-DMBNHS in DMF. Prepare fresh on the day of use. For 100 reactions, mix 3.9 mL CHCl3 with 110μL 2.5 mM TEA (in CHCl3) and 100μL 2.5 mM 13C1DMBNHS (in DMF).

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Table 1 Deuterated internal standard lipid working solutions for different biological matrices. Listed are the volumes to be used from the original stock solutions (from Avanti Polar Lipids) to prepare 1 mL of each mixture. The final concentrations of each standard are listed in μM

Shipped stock concentration

Mammalian brain Mammalian cell tissue/extracellular lines/tissue vesicles

Bacteria

Lipid standard

mg/mL

μM

μL

μM

μL

μM

μL

PC(15:0/18:1(d7))

1

1328.9

130

172.6

190

252.3

190

PE(15:0/18:1(d7))

1

1406.4

40

56.3

170

239.1

170 843.9

PS(15:0/18:1(d7))

1

1287.0

60

77.2

190

244.5

190

77.2

PG(15:0/18:1(d7))

1

1308.9

60

78.5

20

26.2

20

209.4

PI(15:0/18:1(d7))

1

1180.5

20

23.6

190

224.3

190

23.6

PA(15:0/18:1(d7))

1

1449.4

5

7.2

120

173.9

120

87.0

LPC(18:1(d7))

1

1891.4

3

5.7

20

37.8

20

18.9

LPE(18:1(d7))

1

2054.9

2

4.1

5

10.3

5

10.3

LPS(13:0)

1

2094.4

2

4.2

20

41.9

20

20.9

LPG(13:0)

5

10,765

2

4.3

9

19.4

9

21.5

PC(P-18:0/18:1(d9))

1

1280.1

10

12.8

40

51.2

10

12.8

PE(P-18:0/18:1(d9))

1

1353.0

50

67.6

150

202.9

150

67.6

MG(18:1(d7))

1

2750.4

2

5.5

4

11.0

4

27.5

DG(15:0/18:1(d7))

1

1700.8

6

10.2

10

17.0

10

17.0

TG(15:0/18:1(d7)/15:0)

1

1231.0

20

24.6

4

4.9

4

24.6

SM(d18:1/18:1(d9))

1

1354.8

40

54.2

60

81.3

60

27.1

Cer(d18:1(d7)/15:0)

1

1883.5

30

56.5

20

37.7

20

18.8

CE(18:1(d7))

1

1519.4

20

30.4

7

10.6

7

15.2

Cholesterol(d7)

1

2540.0

10

25.4

8

20.3

8

50.8

7838.9

10

7.8

10

7.8

10

3.9

CL(14:0/14:0/14:0/14:0) 10

μM 66.4

7. 266μM I2 in CHCl3 and 2 mM NH4HCO3 in 2:1 [v/v] CHCl3:MeOH. Prepare fresh on the day of use. For 100 reactions, mix 2.88 mL CHCl3 with 320μL 3.94 mM I2 in CHCl3, 1493μL MeOH, and 107μL 90 mM NH4HCO3 in MeOH, and keep on ice. 8. 10 mM ice-cold aqueous NH4HCO3 solution. Prepare fresh on the day of use. Dissolve 7.9 mg NH4HCO3 in 10 mL water, and keep on ice.

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9. 4:2:1 [v/v/v] IPA:MeOH:CHCl3, containing 20 mM Ammonium formate (AF). Prepare fresh on the day of use. Dissolve 22.7 mg AF in 4 mL MeOH. Add 8 mL IPA and 2 mL CHCl3. 2.3 Consumables and Equipment

1. 1.5 mL Safe-Lock Biopure Eppendorf tubes. 2. 2 mL Safe-Lock Biopure Eppendorf tubes. 3. 2 mL glass vials with Teflon sealed caps. 4. 96-well Teflon plates. 5. Ultra-thin adhesive 96-well Teflon sealing tape. 6. Aluminum foil. 7. Zirconium oxide beads (0.15 mm or 0.5 mm). 8. 100μL plastic scoop. 9. Bullet blender (Next Advance, Averill Park, NY). 10. Benchtop centrifuge (up to 14,000 rpm). 11. Vortex. 12. Centrifugal vacuum evaporator or Nitrogen Dryer. 13. Thermal mixer or shaker.

3

Methods Unless stated otherwise, reactions are performed at room temperature. Typical sample matrices and amounts used for this protocol are: (1) 2  106 mammalian cells, (2) 1 mg fresh frozen mammalian tissue, (3) 100μg extracellular vesicle protein (protein content in vesicles is determined prior to lipid extraction), (4) 5 mL of an OD600 nm ¼ 1 bacteria (e.g., E. coli). Samples should be in 1.5 mL Safe-Lock Biopure Eppendorf tubes and washed at least three times in an appropriate buffer (e.g., phosphate buffered saline), followed by centrifugation, snap freezing in liquid nitrogen, and freeze drying. Freeze drying helps to reduce degradation and can improve lipid extraction.

3.1 Monophasic Lipid Extraction

Method is adapted from Lydic et al. [21]. 1. Prepare an empty 1.5 mL Safe-Lock Biopure Eppendorf tube that serves as an extraction blank for every five to six samples (see Note 3). 2. Remove samples from 80  C freezer and keep on ice. 3. Add 1 scoop of zirconium oxide beads (0.15 mm for cells and 0.5 mm for tissue) using a 100μL plastic scoop per 200μL of homogenization solvent to each sample and blank tube. 4. Add 200μL of ice-cold 60% [v/v] MeOH in water containing 0.01% [w/v] BHT.

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5. Add xμL of deuterated internal standard working solution to each sample and extraction blank tubes. The volume of deuterated internal standard working solution needed depends on the type and amount of sample. It is advisable to use an internal standard to endogenous lipid ratio that yields an ion abundance approx. 10–20% of the most abundant lipid within each class. For example, 5μL or 50μL of the mammalian cell line/tissue specific deuterated internal standard working solution is sufficient for 2  106 mammalian cells or 1 mg mammalian tissue, respectively, 1μL of the mammalian brain tissue/extracellular vesicle specific deuterated internal standard working solution is sufficient per 10μg of extracellular vesicle protein, and 5μL of the bacteria specific deuterated internal standard working solution is sufficient per 5 mL of OD600 nm ¼ 1 bacteria. 6. Homogenize using a bullet blender at speed 8 for 30 s, then immediately cool samples on dry ice for approximately 30 s. 7. Repeat step 6 twice. 8. If incomplete homogenization is observed, speed may be increased, or samples may be sonicated for 10–30 min until complete homogenization is achieved. 9. Add 120μL water. 10. Add 420μL MeOH containing 0.01% [w/v] BHT, and vortex for 5 s. 11. Add 250μL CHCl3 containing 0.01% [w/v] BHT, and vortex for 5 s. The ratio of total H2O:CHCl3:MeOH up to this step should be 0.74:1:2 [v/v/v]. 12. Vortex sample vigorously for 1 min, then optionally incubate samples on a thermal shaker at 1400 rpm for 5–30 min. 13. Centrifuge samples for 15 min at 14,000 rpm. 14. Carefully transfer the supernatant, without disturbing the pellet, to a new, labeled 2 mL Safe-Lock Biopure Eppendorf tube. 15. Re-extract the remaining pellet by adding 100μL water and 400μL of 1:2 [v/v] CHCl3:MeOH containing 0.01% [w/v] BHT, and vortex. 16. Homogenize using the bullet blender at the lowest speed for 1 min. 17. Centrifuge samples for 15 min at 14,000 rpm. 18. Transfer the supernatant to the supernatant collected in step 12. 19. Dry the protein pellet by evaporating any remaining CHCl3 for 30 min. Some liquid may remain. Store the pellet for further analysis (e.g., protein assays or proteomics) at 80  C.

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20. Dry the pooled supernatants from step 16 in a centrifugal vacuum evaporator or under a nitrogen stream. Centrifugal vacuum evaporation takes approximately 1–3 h. Drying under nitrogen stream can be performed overnight. 21. Resuspend dried lipid extracts in IPA:MeOH:CHCl3 (4:2:1, [v/v/v]) containing 0.01% [w/v] BHT. Add 1 mL per 1  106 mammalian cells. Add 100μL per mg of mammalian tissue. Add 4μL per μg of extracellular vesicle protein. Add 100μL per 1 mL E. coli OD600 nm ¼ 1. 22. Vortex vigorously and shake for 3 min on a thermal mixer at 1400 rpm. 23. Centrifuge samples at 14,000 rpm (max 20,000  g) for 15 min to precipitate any residual particulate material. 24. Transfer the supernatant to a fresh 1.5 mL Safe-Lock Biopure Eppendorf tube and repeat step 21. 25. Transfer the resuspended lipid extract to a 2.0 mL glass vial and store at 80  C, or proceed to lipid derivatization and/or analysis. 3.2 Functional Group Selective Lipid Derivatization Reactions

Functional group derivatization of aminophospholipids and plasmalogen-containing lipids using 13C1-DMBNHS and I2/ MeOH, respectively, can be performed as standalone reactions, or may be performed sequentially. Reaction schemes for both processes are illustrated in Scheme 1. Positive ionization mode mass spectra acquired on a brain tissue lipid extract, before and after sequential derivatization reactions, and using an Orbitrap Fusion Lumos mass spectrometer operating at 500,000 mass resolving power, are shown in Figs. 1 and 2, respectively. A characteristic mass shift of 131.0486 Da (i.e., +13C112C5H10O1S1) is observed for aminophospholipid ions derivatized with 13C1-DMBNHS, a mass shift of 157.9229 Da (i.e., +CH3O1I1) is observed for plasmalogen-containing lipid ions, and a mass shift of 288.9715 Da (i.e., + 13C112C6H13O2S1I1) is observed for lipid ions containing both amine and plasmalogen functional groups. The utility of this approach for resolving isomeric lipids using nESIUHRAMS analysis is exemplified for the m/z 744.5907 ion (C42H83N1O7P1, calc. m/z 744.5907) shown in the top left inset to Fig. 1. In the absence of derivatization, this ion could potentially (i.e., ambiguously) be assigned based on its accurate mass value as containing PC(P-34:1), PC(O-34:2), PE(P-37:1), and/or PE (O-37:2) lipid species. Upon derivatization however, three of these potential lipid species could be unambiguously identified at distinct m/z values, i.e., PC(O-34:2) remaining at a trace level at m/z 744.5907, {PC(P-34:1) at m/z 902.5131 (C43H86N1O8P1I1, calc m/z 902.5136, 0.55 ppm), and {{PE(P-37:1) at m/z 1033.5617 (13C112C48H96N1O9P1S1I1, calc m/z 1033.5622,

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Vinzenz Hofferek et al. 876.8017 1.56E4 874.7860

1.47E5

877.8051 875.7895

PC(P-34:1) and/or PC(O-34:2) and/or 746.6064 PE(P-37:1) and/or PE(O-37:2)

876.5599

873.7737

877.5633

746.5701 873

742.5749 742.0

743.0

744.0

m/z

745.0

875

876

877

4.92E4

878

904.5913 903.8208

746.0

747.0

905.5946

901.8051

904.8241

902.5756

PC(34:1) and/or PE(37:1) 760.5850

3.69E6

874

902.8174

744.5907 745.5941 744.5543 745.5577 743.5782

901

902

903

904

905.8364

905

906

1032.8357

Abundance

2.19E3

PE(P-36:2) 728.5595 I.S. PE(33:1(d7)) 711.5665 680.4805 600

700

PC(32:2) 734.5696

PE(38:4) 768.5538 788.6162

1033.8391

PE(40:6) 792.5540

1034.8425

810.6009

800

1031.8234

m/z

1031

1032

900

1033

m/z

1000

1034

1035

1036

1100

Fig. 1 Positive ionization mode nESI-UHRAMS analysis (m/z 600–1100) of a crude brain tissue total lipid extract. The insets show expanded m/z regions, including for m/z 744.5907 that may be potentally assigned at the sum composition level as containing PC(P-34:1), PC(O-34:2), PE(P-37:1), and/or PE(O-37:2) lipid species

0.48 ppm), while the potential {PE(O-37:2) lipid, expected at m/z 875.6393, was not observed (each shown in the insets to Fig. 2). The improved ionization efficiency associated with 13C1DMBNHS derivatization is exemplified for two of the PE lipids annotated in Fig. 1, e.g., compare the ion abundances of the protonated PE(33:1(d7)) internal standard at m/z 711.5665 (C381H682H7NO8P1, calc. m/z 711.5670, 0.70 ppm) and the endogenous PE(P-36:2) lipid at m/z 728.5595 (C41H79NO7P1, calc. m/z 728.5594, +0.14 ppm) in Fig. 1, with their derivatized counterparts at m/z 842.6150 (derivatized with 13C1-DMBNHS) and m/z 1017.5303 (i.e., derivatized with 13C1-DMBNHS and I2/MeOH) in Fig. 2, respectively. In the latter case, the potentially isomeric PE(O-36:3), PC(P-33:2), and PC(O-33:3) lipid species were not observed at their expected m/z values upon derivatization, thereby unambiguously excluding them from being present. 3.2.1 Functional Group Selective Derivatization of Aminophospholipids

Method is adapted from Fhaner et al. [24]. 1. Aliquot 10μL of lipid extract (from step 23 of Subheading 3.1) including extraction blanks, into a 96-well Teflon plate (Teflon is preferred, although glass or other suitable solvent resistant materials may be used).

Chemical Derivatization-Aided Lipidomics 746.6060

1.86E5

746.5700

PC(O-34:2) 742.9777 742.0

71

743.0

*

744.5907 744.0

901.6092 745.6218

745.0

904.4925

†PC(P-34:1)

1.90E5

*

902.5131

746.0

747.0

902.6214

904.5911 905.4958 903.6337

905.6494

‡PE(O-37:2)

not observed 5.28E5

877.6181

875.6026

901 6.12E4

876.6059 873.5869 873

874.5902

874

875

m/z

876

903

904

905

906

‡†PE(P-37:1)

*

1032.5494 1033.5617

876.4611 877

1034.5651 1034.0164

878

PC(34:1) 760.5850

5.40E6

902 1031.5460

1031

1032

1033

m/z

1034

1035.5680

1035

1036

Abundance

‡†PE(P-36:2)

PC(32:2) 734.5694

‡PE(40:6)

1017.5303 ‡†PE(P-38:4)

923.6025

I.S. PC(36:1) ‡PE(38:4) ‡ 788.6164 PE(33:1(d7)) 899.6025 842.6150

‡†PE(P-34:1)

991.5148

1041.5302 ‡†PE(P-40:4) 1069.5611

967.5923 600

700

800

m/z

900

1000

1100

Fig. 2 Positive ionization mode nESI-UHRAMS analysis (m/z 600–1100) of a crude brain tissue total lipid extract after sequential derivatization of aminophospholipids with 13C1–S,S0 -dimethylthiobutanoyl-N-hydroxysuccinimide ester (13C1–DMBNHS) (addition of 131.0486 Da), and O-alk-10 -enyl (i.e., plasmalogen)-containing lipids using iodine and methanol (addition of 157.9229 Da). Plasmalogen-containing aminophospholipids undergo the combined addition of 288.9715 Da. A superscript { symbol indicates lipids derivatized with 13 C1–DMBNHS, while a superscript { symbol indicates lipids derivatized with iodine and methanol. The insets show expanded m/z regions of the mass spectrum, confirming the presence of three of the four lipid species that were potentially assigned at m/z 744.5907 in Fig. 1

2. Dry the lipid extracts using a centrifugal vacuum evaporator for 10 min. 3. Dissolve the dried lipids in 40μL of a freshly prepared 39:1:1.1 [v/v/v] CHCl3:2.5 mM TEA (in CHCl3):2.5 mM 13C1DMBNHS (in DMF) solution by pipetting the solution at least 5 times up and down. Use a multichannel pipette when derivatizing multiple samples to minimize sample handling time. 4. Cover the plate with an ultra-thin adhesive Teflon 96-well plate sealing tape and incubate on an orbital shaker with gentle shaking at approximately 150 rpm for 30 min. 5. Dry the plate completely under vacuum using a centrifugal vacuum evaporator for 20 min (see Note 4).

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6. Proceed to step 1 of Subheading 3.2.2 when sequential chemical derivatization of plasmalogen-containing lipids is to be performed, or proceed to step 5 of Subheading 3.2.2 when only aminophospholipid derivatization is required. 3.2.2 Functional Group Selective Derivatization of Plasmalogen-Containing Lipids

Method is adapted from Fhaner et al. [31]. If only plasmalogencontaining lipid derivatization is to be performed, start with steps 1 and 2 of Subheading 3.2.1, then proceed with the following steps outlined below. 1. Pre-cool the 96-well Teflon plate containing lipid extracts, and extraction blanks, on ice for at least 10 min under a gentle nitrogen stream, using a compatible drying manifold (see Fig. 3 for a custom manifold design that can be easily produced using a conventional 96-well plate and cover, that contains pierced wells for N2 delivery, and that may be placed on top of the 96-well Teflon plate containing lipid extracts). 2. Add 40μL of ice-cold 266μM I2 in CHCl3 and 2 mM NH4HCO3 in 2:1 [v/v] CHCl3:MeOH solution to each well. Use a multichannel pipette when derivatizing multiple samples to minimize sample handling time. 3. Cover the 96-well Teflon plate with aluminum foil and incubate on ice for 5 min (see Note 5). 4. Immediately dry the samples completely under vacuum using a centrifugal vacuum evaporator for 15 min.

Fig. 3 Schematic design of a custom 96-well plate N2 drying manifold, that may be produced by piercing the wells of a conventional 96-well plate, and gluing a cover on top that contains a hole in one end that is connected to tubing for N2 delivery. This drying manifold may then be placed on top of the 96-well Teflon plate containing lipid extracts

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5. Carefully wash the samples three times with 40μL of the aqueous 10 mM NH4HCO3 solution using multichannel pipette. Discard supernatant. 6. Dry the samples under vacuum using centrifugal vacuum evaporator until all liquids are evaporated (approximately 1 h). 7. Dissolve the dried derivatized lipids in 40μL of a 4:2:1 [v/v/v] IPA:MeOH:CHCl3 containing 20 mM AF solution. 8. Cover the 96-well Teflon plate with Ultra-thin adhesive Teflon 96-well plate sealing tape. 9. Samples can be measured directly, or stored for up to 2 weeks at 80  C.

4

Notes 1. BHT dissolves slowly in polar solvents like MeOH or water. It is advisable to prepare solvents the day before the extraction and use 10 stocks to make up working solutions. 2. The concentrations and compositions of the deuterated lipid standard mixtures will vary depending on the biological sample matrix and amount. Smaller volumes from the deuterated internal standard lipid working solutions may be aliquoted into glass vials, then immediately snap frozen and stored at 80  C for up to 6 months without noticeable degradation. However, frozen aliquots should be thawed only once and used immediately. 3. It is important to use Safe-Lock tubes due to their capability to maintain the tube cap properly sealed and closed during homogenization using a bullet blender system. Otherwise, loss of sample during homogenization could result in inaccurate lipid quantification. An extraction blank allows users to identify and correct for background contaminants that may be introduced during the extraction process. 4. If samples do not completely dry, add an additional 5μL CHCl3 and continue the drying process until all samples are dried. 5. The plasmalogen-containing lipid derivatization reaction is light and pH sensitive. Reaction times should not exceed 5 min, otherwise other carbon-carbon double bonds may be derivatized.

Acknowledgments This work was supported by research grants APP1156778 and APP1142750 from the National Health and Medical Research

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Council (NHMRC), and research grant DP190102464 from the Australian Research Council (ARC). References 1. O’Donnell VB, Ekroos K, Liebisch G, Wakelam M (2020) Lipidomics: current state of the art in a fast moving field. Wiley Interdiscip Rev Syst Biol Med 12(1):e1466. https://doi.org/ 10.1002/wsbm.1466 2. Beloribi-Djefaflia S, Vasseur S, Guillaumond F (2016) Lipid metabolic reprogramming in cancer cells. Oncogene 5:e189 3. Pakiet A, Kobiela J, Stepnowski P, Sledzinski T, Mika A (2019) Changes in lipids composition and metabolism in colorectal cancer: a review. Lipids Health Dis 18:29. https://doi.org/10. 1186/s12944-019-0977-8 4. Park JK, Coffey NJ, Limoges A, Le A (2018) The heterogeneity of lipid metabolism in cancer. Adv Exp Med Biol 1063:33–55 5. Ekroos K, Lavrynenko O, Titz B, Pater C, Hoeng J, Ivanov NV (2020) Lipid-based biomarkers for CVD, COPD, and aging—a translational perspective. Prog Lipid Res 78:101030. https://doi.org/10.1016/j. plipres.2020.101030 6. Huynh K, Martins RN, Meikle PJ (2017) Lipidomic profiles in diabetes and dementia. J Alzheimers Dis 59:433–444 7. Wong MW, Braidy N, Poljak A, Pickford R, Thambisetty M, Sachdev PS (2017) Dysregulation of lipids in Alzheimer’s disease and their role as potential biomarkers. Alzheimers Dement 13:810–827 8. Lee TH, Hofferek V, Separovic F, Reid GE, Aguilar MI (2019) The role of bacterial lipid diversity and membrane properties in modulating antimicrobial peptide activity and drug resistance. Curr Opin Chem Biol 52:85–92 9. Leung LM, Fondrie WE, Doi Y, Johnson JK, Strickland DK, Ernst RK, Goodlett DR (2017) Identification of the ESKAPE pathogens by mass spectrometric analysis of microbial membrane glycolipids. Sci Rep 7:6403 10. Shulaev V, Chapman KD (2017) Plant lipidomics at the crossroads: from technology to biology driven science. Biochim Biophys Acta Mol Cell Biol Lipids 1862:786–791 11. Sarabia LD, Boughton BA, Rupasinghe T, van de Meene AML, Callahan DL, Hill CB, Roessner U (2018) High-mass-resolution MALDI mass spectrometry imaging reveals detailed spatial distribution of metabolites and lipids in roots of barley seedlings in response to salinity

stress. Metabolomics 14:63. https://doi.org/ 10.1007/s11306-018-1359-3 12. Han X (2016) Lipidomics: comprehensive mass spectrometry of lipids. Wiley, Hoboken, NJ 13. Rustam YH, Reid GE (2018) Analytical challenges and recent advances in mass spectrometry based lipidomics. Anal Chem 90 (1):374–397 14. Fahy E, Subramaniam S, Brown HA, Glass CK, Merrill AH, Murphy RC, Raetz CRH, Russell DW, Seyama Y, Shaw W, Shimizu T, Spener F, van Meer G, VanNieuwenhze MS, White SH, Witztum JL, Dennis EA (2005) A comprehensive classification system for lipids. J Lipid Res 46:839–861 15. Fahy E, Subramaniam S, Murphy RC, Nishijima M, Raetz CRH, Shimizu T, Spener F, van Meer G, Wakelam MJO, Dennis EA (2009) Update of the LIPID MAPS comprehensive classification system for lipids. J Lipid Res 50:S9–S14 16. Liebisch G, Vizcaino JA, Kofeler H, Trotzmuller M, Griffiths WJ, Schmitz G, Spener F, Wakelam MJ (2013) Shorthand notation for lipid structures derived from mass spectrometry. J Lipid Res 54:1523–1530 17. Bligh EG, Dyer WJ (1959) A rapid method of total lipid extraction and purification. Can J Biochem Physiol 37:911–917 18. Folch J, Lees M, Sloane Stanley GH (1957) A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem 226:497–509 19. Matyash V, Liebisch G, Kurzchalia TV, Shevchenko A, Schwudke D (2008) Lipid extraction by methyl-tert-butyl ether for highthroughput lipidomics. J Lipid Res 49:1137–1146 20. Alshehry ZH, Barlow CK, Weir JM, Zhou Y, McConville MJ, Meikle PJ (2015) An efficient single phase method for the extraction of plasma lipids. Meta 5:389–403 21. Lydic TA, Busik JV, Reid GE (2014) A monophasic extraction strategy for the simultaneous lipidome analysis of polar and nonpolar retina lipids. J Lipid Res 55:1797–1809 22. Wang M, Wang C, Han X (2017) Selection of internal standards for accurate quantification of complex lipid species in biological extracts by electrospray ionization mass spectrometry—

Chemical Derivatization-Aided Lipidomics what, how and why? Mass Spectrom Rev 36:693–714 23. Schuhmann K, Almeida R, Baumert M, Herzog R, Bornstein SR et al (2012) Shotgun lipidomics on a LTQ orbitrap mass spectrometer by successive switching between acquisition polarity modes. J Mass Spectrom 47:96–104 24. Fhaner CJ, Liu S, Ji H, Simpson RJ, Reid GE (2012) Comprehensive lipidome profiling of isogenic primary and metastatic colon adenocarcinoma cell lines. Anal Chem 84:8917–8926 25. Ryan E, Reid GE (2016) Chemical derivatization and ultrahigh resolution and accurate mass spectrometry strategies for “Shotgun” lipidome analysis. Acc Chem Res 49 (9):1596–1604 26. Wang M, Huang Y, Han X (2014) Accurate mass searching of individual lipid species candidates from high-resolution mass spectra for shotgun lipidomics. Rapid Commun Mass Spectrom 28:2201–2210 27. Cajka T, Fiehn O (2014) Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry. Trends Analyt Chem 61:192–206 28. Pham TH, Zaeem M, Fillier TA, Nadeem M, Vidal NP, Manful C, Cheema S, Cheema M, Thomas RH (2019) Targeting modified lipids during routine lipidomics analysis using HILIC and C30 reverse phase liquid chromatography coupled to mass spectrometry. Sci Rep 9:5048 29. Hu C, Duan Q, Han X (2019) Strategies to improve/eliminate the limitations in shotgun lipidomics. Proteomics 10:e1900070. https:// doi.org/10.1002/pmic.201900070 30. Bielow C, Mastrobuoni G, Orioli M, Kempa S (2017) On mass ambiguities in high-resolution shotgun lipidomics. Anal Chem 89 (5):2986–2994 31. Fhaner CJ, Liu S, Zhou X, Reid GE (2013) Functional group selective derivatization and

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gas-phase fragmentation reactions of plasmalogen glycerophospholipids. Mass Spectrom 2: S0015 32. Nie S, Fhaner CJ, Liu S, Peake D, Kiyonami R, Huang Y, Reid GE (2015) Characterization and multiplexed quantification of derivatized aminophospholipids. Int J Mass Spectrom 391:71–81 33. Han X, Yang K, Cheng H, Fikes KN, Gross RW (2005) Shotgun lipidomics of phosphoethanolamine-containing lipids in biological samples after one-step in situ derivatization. J Lipid Res 46(7):1548–1560 34. Zemski Berry KA, Turner WW, VanNieuwenhze MS, Murphy RC (2009) Stable isotope labeled 4-(dimethylamino)benzoic acid derivatives of glycerophosphoethanolamine lipids. Anal Chem 81:6633–6640 35. Tokuoka SM, Kita Y, Shimizu T, Oda Y (2019) Isobaric mass tagging and triple quadrupole mass spectrometry to determine lipid biomarker candidates for Alzheimer’s disease. PLoS One 14:e0226073 36. Haimi P, Uphoff A, Hermansson M, Somerharju P (2006) Software tools for analysis of mass spectrometric lipidome data. Anal Chem 78:8324–8331 37. Husen P, Tarasov K, Katafiasz M, Sokol E, Vogt J, Baumgart J, Nitsch R, Ekroos K, Ejsing CS (2013) Analysis of lipid experiments (ALEX): a software framework for analysis of high-resolution shotgun lipidomics data. PLoS One 8:e79736. https://doi.org/10.1371/ journal.pone.0079736 38. Zhou XA, Lu YL, Wang WJ, Borhan B, Reid GE (2010) ‘Fixed Charge’ chemical derivatization and data dependant multistage tandem mass spectrometry for mapping protein surface residue accessibility. J Am Soc Mass Spectrom 21:1339–1351

Chapter 6 Quantitative Analysis of Polyphosphoinositide, Bis(monoacylglycero)phosphate, and Phosphatidylglycerol Species by Shotgun Lipidomics After Methylation Meixia Pan, Chao Qin, and Xianlin Han Abstract Phospholipids play important roles in biological process even at a very low level. For example, bis (monoacylglycerol)phosphate (BMP) is involved in the pathogenesis of lysosomal storage diseases, and polyphosphoinositides (PPI) play critical roles in cellular signaling and functions. Phosphatidylglycerol (PG), a structural isomer of BMP, mediates lipid–protein and lipid–lipid interactions, and inhibits platelet activating factor and phosphatidylcholine transferring. However, due to their low abundance, the analysis of these phospholipids from biological samples is technically challenging. Therefore, the cellular function and metabolism of these phospholipids are still elusive. This chapter overviews a novel method of shotgun lipidomics after methylation with trimethylsilyl-diazomethane (TMS-D) for accurate and comprehensive analysis of these phospholipid species in biological samples. Firstly, a modified Bligh and Dyer procedure is performed to extract tissue lipids for PPI analysis, whereas modified methyl-tert-butylether (MTBE) extraction and modified Folch extraction methods are described to extract tissue lipids for PPI analysis. Secondly, TMS-D methylation is performed to derivatize PG/BMP and PPI, respectively. Then, we described the shotgun lipidomics strategies that can be used as cost-effective and relatively high-throughput methods to determine BMP, PG, and PPI species and isomers with different phosphate position(s) and fatty acyl chains. The described method of shotgun lipidomics after methylation achieves feasible and reliable quantitative analysis of low-abundance lipid classes. The application of this novel method should enable us to reveal the metabolism and functions of these phospholipids in healthy and disease states. Key words Methylation, Polyphosphoinositide, Bis(monoacylglycerol)phosphate, Phosphatidylglycerol, Mass spectrometry, Shotgun lipidomics

1

Introduction Glycerophospholipids are defined by the presence of at least one phosphate (or phosphonate) group being esterified to one of the glycerol hydroxyl groups. The glycerophospholipid family includes phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, phosphatidylserine, phosphatidylglycerol, phosphatidic acid, cardiolipin, lysophosphatidylcholine, lysophosphatidylethanolamine, bis(monoacylglycerol)phosphate (BMP), etc. Besides

Fong-Fu Hsu (ed.), Mass Spectrometry-Based Lipidomics: Methods and Protocols, Methods in Molecular Biology, vol. 2306, https://doi.org/10.1007/978-1-0716-1410-5_6, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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functioning as key components of cellular membranes, glycerophospholipids also involve metabolism and signaling [1, 2]. Identification of lipid composition and quantification of cellular lipids are essential for characterization of molecular signatures of lipid-related pathways [3]. However, the diversity of lipid structures and characteristics brings difficulty for lipidome analysis [2], which in turn limits the study of lipids to a certain extent, especially for those involved in metabolic pathways and signaling transduction. For example, cellular lipid molecular species and composition are different from species, cell types and origins, organelles, membranes, and membrane microdomains [2]. Furthermore, the cellular lipidome is dynamic [2, 4]. Technically, due to the low recovery and abundance [3, 5, 6], different polarity and wide dynamic range [7], and the presence of many isomeric structures [5] of glycerophospholipids, it is challenging to recover and analyze these lipids from biological samples for their accurate cellular levels and structures with existing methods including low throughput and an inability to resolve different fatty acyl species [8]. Bis(monoacylglycerol)phosphate (BMP) and phosphatidylglycerol (PG) are structural isomers [8]. The former is a class of low-abundance, negatively charged phospholipids almost exclusively located in late endosomes/lysosomes [9]. BMP is involved in the pathology of lysosomal storage diseases such as mucopolysaccharidosis, Niemann–Pick disease type A/B/C, Gaucher disease, and Fabry disease [10–13]. It also plays an important role in certain drug-induced phospholipidosis [14] and sphingolipid degradation [15]. On the other hand, PG is abundant in the lung surfactant and microorganisms, is mostly considered as a precursor for the biosynthesis of cardiolipin, and plays important roles in both lipid–protein and lipid–lipid interactions, such as activating RNA synthesis [16] and nuclear protein kinase C [17], and inhibiting platelet activating factor [18] and phosphatidylcholine transferring [19]. Up to date, even high-resolution tandem mass spectrometry (MS) cannot distinguish BMP and PG species without prior extensive separation as they undergo an identical fragmentation pattern. Therefore, extensive investigation of the characteristics, metabolism, and cellular function of PG and BMP has been hindered to a certain extent. Although a limited number of studies on the analysis of PG and BMP, based on HPLC–electrospray ionization MS (ESI-MS), have been reported [20–22], HPLC-based studies on lipid analysis are generally labor intensive [20] and usually not comprehensive. As a category of cellular membrane lipids, polyphosphoinositides (PPI) are another low-abundance lipid class [6]. They are dynamically phosphorylated/dephosphorylated from/to phosphatidylinositol (PI). PPI phospho-derivatization at the positions 3, 4, and/or 5 of the inositol ring by different kinases and phosphatases

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can generate seven distinct PPI classes, including PI(3)P, PI(4)P, PI (5)P, PI(3,4)P2, PI(3,5)P2, PI(4,5)P2, and PI(3,4,5)P3 [23– 26]. They are predominantly located on the cytoplasmic side of cell membrane. PPI classes and their hydrolysis products play critical roles in numerous cellular processes, including membrane trafficking, cell growth, survival, and motility [23, 24, 27– 29]. Aberrant PPI signaling is associated with numerous human diseases, such as cancer, neurological disorders, diabetes, and cardiovascular dysfunction [24, 25, 29–31]. Classic methods for PPI analysis, e.g., thin layer chromatography, HPLC, receptor displacement assay, and radioactive labeling [32, 33], are usually laborious, time-consuming and do not provide comprehensive information about fatty acyl composition. In the past few years, a variety of ESI-MS-based methods for the analysis of PPI species have been developed [8, 34–38]. Wenk et al. analyzed PPI by MS/MS in the precursor-ion scan mode targeted to inositol phosphate fragment ions after an enrichment of these lipids with an affinity SPE column [38]. The method was labor intensive and resulted in relatively low sensitivity in PIP2 analysis. Haag et al. developed a method based on the NLS of ammoniated inositol phosphate from their ammoniated molecular species [35]; however, the isomeric PIP and PIP2 cannot be distinguished. Trimethylsilyl-diazomethane (TMS-D) enables relatively fast and clean esterification of protonated phosphate groups at room temperature [39]. Therefore, it has been used to achieve rapid and complete methylation of the phosphate groups in PPI with some degree of methylation of free hydroxyl groups in the inositol ring, but no modification of unsaturated fatty acyl chains [8]. Clark et al. derivatized PPI species from lipid extracts with TMS-D and analyzed methylated PIP3 species using LC-MS/MS [8]. The group also extended the method to analyze other PPI classes [36]. The approach significantly enhanced the analysis sensitivity and showed promising results for the analysis of PPI species. However, this approach is relatively time-consuming, because it requires running NLS of methylated inositol phosphate head groups first in order to identify the presence of particular PPI species and accurately quantify them by multiple-reaction-monitoring-based MS. Moreover, the method was unable to quantify all PPI species carrying different fatty acyl chains and phosphate positional isomers. Although the methylation method has been used by shotgun lipidomics for relative quantification of comparable samples with and without stable isotope methyl labeling, the PPI isomers were not identified [34, 40]. Recently, a methylation method is reported, in which TMS-D is used to methylate phosphate groups of phospholipids. The methylation method has been optimized and exploited in shotgun lipidomics for identification and quantitation of BMP and PPI species and isomers in the biological sample. The method offers a novel

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strategy of high sensitivity measurement and chemical stability to solve the major problems [8, 34]. The methylation method has been exploited by shotgun lipidomics for identification and quantification of low-abundance phospholipids [5, 6]. In this chapter, BMP and PPI are used as examples to introduce this novel strategy for quantitation of phospholipids in biological samples by shotgun lipidomics after TMS-D methylation.

2

Materials All animal studies should be approved by the Institutional Animal Care and Use Committee at University or Institute. Mice are maintained on a standard light-dark cycle (12 h) at room temperature (23
C). All tissues are harvested on ice, then snap-frozen in liquid nitrogen, stored at 80
C until further used. All solutions and solvents are prepared using ultrapure water and analytical grade reagents. All solutions and materials should be ice-cold and/or stored at 4
C before use. All extraction and derivatization procedures should be performed in a chemical fume hood.

2.1 Tissue Harvest and Homogenization

1. Fluriso (Isoflurane, USP), anesthetic use (applied by Laboratory Animal Research Center). 2. 75% Alcohol for sterilization. 3. SomoSuite Small Scientific, USA).

Animal

Anesthesia

System

(Kent

4. Perfusion pump (Thermo scientific, USA). 5. Surgery platform for small animals. 6. Butterfly needle (20 G) for perfusion. 7. Scissors. 8. Forceps. 9. Cryopreservation tube. 10. 0.1 PBS. 11. Liquid nitrogen, ice. 12. Cryolys Evolution homogenizer (Precellys® Evolution, USA). 13. 1.4 and 2.8 mm ceramic beads. 14. 2 mL hard tissue homogenizing tube for Cryolys Evolution homogenizer (Bertin, USA), or 2 mL Precellys lysing kit (Bertin, USA). 15. Scale (0.01 mg). 16. Cold 0.1 PBS.

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The total amount of tissue sample used to do lipidomic analysis is equivalent to about 0.1 mg protein. The protein content is used to normalize the lipid species for quantitation. The reagents applied are HPLC or MS grade or higher. 1. Automated nano-electrospray ionization (ESI) source device and Chipsoft 8.3.1 software (TriVersa NanoMate, Advion Bioscience Ltd., Ithaca, NY, USA). 2. Triple Quadrupole Mass Spectrometer (Thermo TSQ Quantiva™, San Jose, CA). 3. Q-Exactive Plus Mass Spectrometer (Thermo Fisher Scientific, San Jose, CA). 4. 6 and 10 mL disposable culture tubes with PTFE lined cap. 5. 5.7500 disposable borosilicate glass Pasteur pipets. 6. Reagents: methanol (MeOH), chloroform (CHCl3), Millipore deionized water (dd-H2O), isopropanol (IPA), lithium hydroxide (LiOH), and lithium chloride (LiCl), methyl-tertbutylether (MTBE), hydrochloric acid (HCl), TMS-D (see Note 1), hexane, glacial acetic acid, ammonium acetate. 7. Extraction solvents: CHCl3–MeOH (1:1, v:v) (Solvent A), 50 mM LiCl in dd-H2O (Solvent B), 10 mM LiCl in dd-H2O (Solvent C), MTBE–MeOH–2 M HCl (200:60:13, v:v:v) (Solvent D), CHCl3–MeOH-37% HCl (40:80:1, v:v:v) (Solvent E). 8. Derivatization solvents: pre-derivatization wash solution (bottom phase of MTBE–MeOH–0.01 M HCl (20:6:5, v:v:v)) (Solvent F), 2 M TMS-D in hexane, glacial acetic acid, postderivatization wash solution (bottom phase of MTBE– MeOH–H2O (20:6:5, v:v:v)) (Solvent G). 9. MS analysis solvents: CHCl3–MeOH–IPA (1:2:4, v:v:v), 5 mM ammonium acetate, 2000-fold diluted saturated LiCl methanol solution. 10. Vortex mixer. 11. Ultracentrifuge (Beckman).

with

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12. Lipid internal standards (see Note 2): (a) 1,2-Dipentadecanoyl-sn-glycero-3-phosphoglycerol (sodium salt) (di15:0 PG). (b) 17:0–20:4 PI(3)P (PIP). (c) 17:0–20:4 PI(4)P (PIP). (d) 17:0–20:4 PI(5)P (PIP). (e) 17:0–20:4 PI(3,4)P2 (PIP2).

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(f) 17:0–20:4 PI(3,5)P2 (PIP2). (g) 17:0–20:4 PI(4,5)P2 (PIP2). (h) 17:0–20:4 PI(3,4,5)P3 (PIP3).

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Methods All procedures should be carried out at 4
C.

3.1 Tissue Harvest and Homogenization

1. Fix the anesthetized mouse to a surgery platform (see Note 3). 2. Open the abdominal cavity and thoracic cavity to visualize heart and liver (see Note 4). 3. Perform perfusion via apex of left ventricle (LV) with cold 0.1 PBS at a speed of 3.5 mL/min for 5 min (see Note 5). 4. Separate and dissect the identical spot of liver, rinse with cold 0.1 PBS. 5. Snap-freeze the harvested liver with liquid nitrogen and then store at 80
C until used. 6. Lyophilize the liver sample for 24 h. 7. Pre-fill a 2 mL Precellys homogenate tube (Bertin) with three 2.8 mm beads and ten 1.4 mm beads. Weigh 5–10 mg of dried liver tissue. 8. Add 0.1 PBS (80μL/mg dried weight), then homogenize the tissue in hard mode using Cryolys Evolution homogenizer (see Note 6). 9. Carry out a protein assay following the instruction of bicinchoninic acid protein assay kit (Pierce, Rockford, IL). The protein content is used to normalize the quantitation of lipid species.

3.2 Shotgun Lipidomics for Accurate, Comprehensive, and High-Throughput Analysis of Isomeric PG and BMP

Figure 1 illustrated the novel strategy based on shotgun lipidomics for accurate, comprehensive, and high-throughput analysis of isomeric PG and BMP species [5]. In the strategy, individual PG and BMP species as well as their potential mixtures present in lipid extracts are first identified by separate products ion analyses directly from lipid extracts after mass matching. Identified species or their mixtures are quantified by survey scan mass spectra in comparison to a selected internal standard by high mass accuracy MS. Then, TMS-D methylation is performed with lipid extracts of biological samples. Neutral-loss scans (NLS) 203 mass spectra of the methylated lipid extracts are acquired for identification and quantification of methylated PG (Me-PG) species. Finally, identification and quantification of BMP species are derived from the aforementioned two steps.

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Fig. 1 Schematic diagram of quantitative analysis of isomeric BMP and PG species by shotgun lipidomics after one-step methylation 3.2.1 Lipid Extraction [2] for PG and BMP Analysis

1. Make a stock solution of internal standard (see Note 7) and prepare individual internal standard with concentration suitable for quantification (e.g., 2 nmol/mg protein of di15:1 PG in liver sample). 2. In a 12-mL glass tube, add the tissue homogenate (~0.1 mg protein or higher), internal standard(s), 4 mL of Solvent A, and 2 mL of Solvent B (see Note 8). 3. Cap the tube and vortex for 5–30 s. 4. Centrifuge at 1500  g for 20 min. 5. Collect the bottom layer (organic phase) with a glass pipette and transfer it to a new glass tube. 6. To the same glass tube, add 2 mL of chloroform and repeat steps 3 and 4. 7. Collect the bottom layer with a glass pipette and pool (see Note 9). 8. Evaporate the solvent under a nitrogen stream to dryness. Add 3 mL of Solvent A and 1.5 mL of Solvent C. 9. Cap the tube and vortex for 15–30 s. 10. Centrifuge at 1500  g for 20 min. 11. Collect the bottom layer (organic phase) with a glass pipette and transfer it to a new glass tube.

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12. To the same glass tube, add 1.5 mL of CHCl3 and repeat steps 9 and 10. 13. Collect the bottom layer with glass pipette and pool. 14. Evaporate the solvent under a nitrogen stream to dryness and resuspend in ~50μL of CHCl3–MeOH (1:1, v:v). The solution is ready for mass spectrometric analysis and performing derivatization (see Note 10). 3.2.2 Permethylation of PG with TMS-D Reagent

1. Add an equivalent to 0.1 mg of tissue protein of liver lipid extract to a disposable glass tube, and dry with nitrogen stream. 2. Add 25μL of 2 M TMS-D in hexane. 3. Cap the tube, vortex for 30 s and uncap every 5 s to vent gas, and then place the tube at room temperature for 30 min. 4. Add 5μL of glacial acetic acid to quench the reaction. 5. Add 3 mL of Solvent A and 1.5 mL of dd H2O. 6. Cap the tube and vortex for 20 s. 7. Centrifuge at 1500  g for 10 min. 8. Collect the bottom layer (organic phase) with a glass pipette and transfer it to a new glass tube. 9. To the same tube, add 1.5 mL of chloroform and repeat steps 6 and 7. 10. Collect the bottom layer with a glass pipette and pool. 11. Evaporate the solvent under a nitrogen stream to dryness and resuspend in 80μL of CHCl3–MeOH (1:1, v:v) before mass spectrometric analysis.

3.2.3 Mass Spectrometric Analysis of Methylated PG and BMP Species

1. Dilute the as-prepared lipid extraction solution to 99.97% purity). 4. Heating block.

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2.2 Reagents for Preparation of FA-AMPP Derivatives

1. AMP+ Mass Spectrometry Kit (50 test) containing AMPP derivatizing reagent, n-butanol (HOBt), 1-ethyl-3(3-dimethylaminopropyl)carbodiimide (EDC), acetonitrile/ DMF (Cayman Chemical Co, Ann Harbor, MI, USA) (see Note 1).

2.3 Sample Preparation for MALDI TOF/TOF Analysis

1. Matrices: α-cyano-4-hydroxycinnamic acid (CHCA) (>99.0%, purity) (Sigma, St. Louis, USA). 2. Fresh matrix solution (10 mg/mL in 60% acetonitrile with 1% TFA) (see Note 2). 3. Bruker Daltonics UltrafleXtreme TOF/TOF spectrometer equipped with a smartbeam-II™ laser (Bremen, Germany). MALDI source 1 has a gridless MALDI ion source with delayed extraction (DE) electronics and a collision cell; and ion source 2 has a timed ion selector (TIS), and a “LIFT” accelerating cell.

2.4 High-Resolution Tandem Mass Spectrometric Analysis

3

1. Thermo Scientific LTQ Orbitrap Velos mass spectrometer with Xcalibur operating system (Thermo Scientific, Waltham, MA, USA). 2. Methanol.

Methods All experiments are carried out at room temperature unless otherwise specified. Experiments that use open organic solvents are performed in a hood.

3.1 Preparation of Hydroxyphthioceranoic and Phthioceranoic Acids

M. tuberculosis strain H37Rv was grown and sulfolipids were extracted and isolated as previously described [12]. 1. Add 500μL methanol and 500μL tetrabutylammonium hydroxide (40 wt% solution in water) to the dried sulfolipid extract (200μg) in an 8 mL glass centrifugation tube. 2. Close the tube with Teflon-lined cap, vortex at full speed for 1 min, and heat at 75  C for 2 h in a heating block. 3. Cool to room temperature, add 2 mL water and 2 mL hexane, vortex with full speed for 1 min. 4. Centrifuge at 1200  g for 2 min. Transfer the top layer containing hydroxyphthioceranoic (HPA) and phthioceranoic acids (PA) to another centrifuge tube, and dry under a stream of nitrogen.

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Total microbial lipids extracted from Listeria monocytogenes (L. monocytogenes), Rhodococcus equi (R. equi), and Mycobacteria tuberculosis (M. tuberculosis) are according to the methods described in the literature [12, 19, 22] (see Note 3).

3.2 Preparation of Other Microbial Long-Chain Fatty Acids

1. Add 500μL 0.5 M HCl in acetonitrile/water (9:1 by volume) to the dried total lipid extract (200μg) in an 8 mL glass centrifugation tube. 2. Seal the tube with Teflon-lined cap, vortex at full speed for 1 min, and heat at 100  C for 1 h in a heating block. 3. Cool to room temperature, and dry the sample under a stream of nitrogen. 4. Add 1 mL methanol:water:chloroform (2:0.8:1 by volume), vortex at full speed for 1 min. 5. Add 0.52 mL chloroform:water (1:1 by volume), vortex at full speed for 1 min. 6. Centrifuge at 1200  g for 2 min, and transfer the bottom organic layer containing free fatty acids to another centrifuge tube, dry under a stream of nitrogen (see Note 4). The chemical reaction of FA with N-(4-aminomethylphenyl) pyridinium (AMPP) to form FA-AMPP derivatives is shown in Fig. 1 [23]. All FA-AMPPs can be prepared in the same manner as described below (see Note 5).

3.3 Preparation of FA-AMPP Derivative

1. Add 20μL ice-cold acetonitrile/DMF (4:1, v/v) to the glass tubes containing dried HPA and PA (Subheading 3.1, step 4) or LCFA (Subheading 3.2, step 6) extract, vortex at full speed for 30 s.

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Fig. 1 Chemical reaction of FA and AMPP reagents to form FA-AMPP derivatives [23]

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2. Add 20μL of ice-cold 1 M N-3-(dimethylaminopropyl) N’ethyl carbodiimide hydrochloride (EDCI) in water, vortex at full speed for another 30 s. 3. Immerse the glass tube with sample solution in an ice bath. 4. Add 10μL of 5 mM N-hydroxybenzotriazole (HOBt) solution and 30μL solution of 15 mM AMPP (in distilled acetonitrile), mix and heat at 65  C for 30 min (see Note 6). 5. Cool to room temperature, add 1 mL water and 1 mL nbutanol. 6. Vortex at full speed for 1 min, and centrifuge at 1200  g for 3 min. 7. Transfer the top organic layer to a new glass vial. 3.4 MALDI-TOF/TOF Structural Analysis of FA-AMPP Derivatives

Perform on Bruker Daltonic ultrafleXtreme TOF/TOF instrument with FlexControl 3.4 software. All FA-AMPP derivatives are analyzed as M+ ions in the positive-ion mode. 1. Sample spots on MALDI plate. (a) Mix 0.5μL CHCA with 0.5μL FA-AMPP derivative on MALDI plate (see Note 7). (b) Air dry sample spots. 2. MALDI-TOF/TOF instrument settings. (a) 8 kV collision energy with argon collision gas at a pressure of 1.7  104 Pa (see Note 8). (b) LIFT potential: 19 kV. (c) Precursor ion selection window setting (timed ion selector): 1% of molecular mass (see Note 9). (d) Post LIFT metastable suppressor (PLMS): on. (e) Mass spectra acquisition: collect 5–10 TOF/TOF spectra from 500 shots/each and average. (f) Sampling spot: randomly selected to locate sweet spots by visualization, and sum the spectra. (g) Set default Detector Gain Boost at 100% and Laser Power Boost at 50%. Set all other parameters in default setting. 3. Data processing. (a) Process data with the Bruker FlexAnalysis 3.0. (b) Export spectra to PowerPoint. (c) Structural assignments follow the previously established rules (see Notes 10–12).

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3.5 ESI Tandem Mass Spectrometric Analysis of the FA-AMPP Derivatives as M+ Ions

Perform on a Thermo Scientific LTQ Orbitrap Velos mass spectrometer with Xcalibur operating system. All FA-AMPP derivatives are analyzed as M+ ions in the positive-ion mode. 1. Ion source settings: (a) ESI needle voltage: 4.0 kV. (b) Heated capillary temperature: 300  C. (c) Automatic gain control of the ion trap: 5  104. (d) Maximum injection time: 50 ms. (e) Buffer and collision gas: Helium at a pressure of 1  10
3 mbar (0.75 mTorr). 2. Sample inlet. (a) Loop-inject FA-AMPP derivative in butanol into ESI source with a built-in syringe pump that delivers a 15μL/min flow of methanol. (b) Wash the sample loop >2 times between injections of different samples to ensure no sample carryover from the previous samples. 3. Mass spectra acquisition: (a) Acquire high-resolution (R ¼ 100,000 at m/z 400) mass spectra. (b) Acquire MSn (n ¼ 2, and 3 and 4 if necessary) spectra for structural identification, with optimized relative collision energy (50–75%), activation q value (0.25), activation time (10 ms) to leave a residual precursor ion abundance around 20%. (c) Precursor ion selection window: 1 Da (monoisotopic mass) for CID and HCD unit resolution or highresolution tandem mass spectra acquisition. (d) Acquisition time: 1–5 min in the profile mode for MSn spectra (n ¼ 2–4). 4. Data processing: (a) Process data with the Thermo Xcalibur software and perform offline recalibration with an internal ion of known m/z to establish the elemental compositions of the fragment ions if needed (see Note 10). (b) Structural assignments follow the previously established rules (see Notes 11–13). (c) For relative quantitation, peak intensity ratio of the measure ion to the internal standard is calculated.

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Notes 1. The AMP+ Mass Spectrometry Kit is a complete derivatization kit consisting of ready-to-use reagents and solvents. It can be stored in
80  C after use. We found it is stable over 6 months. 2. Fresh matrix solution should be used each time. 3. This protocol is applicable for release of LCFA from any total lipid extracts by hydrolysis. 4. This extraction process recovers >80% LCFA, and a second extraction can be performed if desired. 5. Prior to use, AMP+ Mass Spectrometry Kit removed from storage should be thawed in an ice bath until the solution is clear. 6. The derivative is made using the AMP+ kit according to the manufacturer’s instruction with modification. The derivatization processes are rather forgiving. For example, the proportion of reagents ratio (v/v) added can be in a range of 1–3, and the temperature can be in the range of 65–100  C with reaction time 30–60 min. 7. The MALDI-TOF spectrum can also be obtained from the sample spotted without matrix to avoid matrix interference, but the sensitivity is significantly lower. 8. There is no significant difference in the LIFT-TOF/TOF spectra obtained with and without argon collision gas. Therefore, acquiring the LIFT-TOF/TOF spectra without Ar collision gas is recommended. The fragmentations are mainly achieved by the elevated laser fluence, and the processes are similar to postsource decay. 9. This default precursor ion selection window is to be set to maintain sufficient sensitivity, the trade-off is that FA molecules with difference in one double bond (2 Da) in a mixture can’t be specifically selected, and the MS2 spectra are indistinguishable if the two FA species differed by one double in a mixture are selected for LIFT-TOF/TOF acquisition. 10. For location of the functional groups for fatty acids, highresolution mass spectrometry is not required, but the elemental compositions can add another layer of confident assignment of, in particular, unknowns with complex structures. 11. See refs. 7–12, 18–21 for the mechanisms of fragmentations for various LCFAs. 12. Despite a slight difference among the product ion spectra obtained by HCD, CID, or MALDI-TOF/TOF, all the spectra contain similar structural information applicable for complete structural identification.

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Fig. 2 The product ion spectra of the M+ ions of Δ8,11,1420:3-AMPP at m/z 473 (a), Δ11,14,1720:3-AMPP at m/z 473 (b), Δ14,17,20,23,26,2932:6-AMPP at m/z 635 (c) obtained by MALDI-TOF/TOF. The corresponding LIT HCD MS2 spectra obtained at optimized collision energies (CE) are shown in Panels (d) (50% normalized CE), (e) (45% normalized CE), and (f) (65% normalized CE), respectively. The spectra on left panels (panels a, b, and c) containing fragment ions from charge remote fragmentation clearly show the double bond position of the polyunsaturated fatty acids (the fragmentation processes of the molecules are shown in each inset). The MS2 spectra obtained by HCD (panels d, e, and f) also yield similar structurally informative ions leading to locate the double bonds. The results demonstrate the utility of FA-AMPP derivatives combined with tandem mass spectrometry regardless of the various mass spectrometric approaches applying high- or low-energy CID towards location of the double bond(s) along the FA chain of the molecules. All the FAs are purchased standards

13. Using the tandem mass spectrometric approach to locate the double bond(s), and other functional groups along the fatty acid chain are exemplified in Figs. 2 and 3.

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Fig. 3 Examples of the HCD tandem mass spectra of FA-AMPP derivatives that define the structures of microbial LCFA (mainly from M. tuberculosis) with various chain length and functional groups including multiple methyl side chains, hydroxy groups, and double bond(s). As shown: (a) dihydrosterculic acid (9,10methyleneoctadecanoic acid) at m/z 463, (b) mycolipanoic acid (2,4,6-trimethylhexacosanoic acid) at m/z 605, (c) mycolipenic acid (2,4,6-trimethyltetracos-2-enoic acid) at m/z 575, (d) α-hydroxytetracosanoic acid at m/z 551, (e) β-hydroxyheptadecanoic acid at m/z 453, and (f) C40 hydrophthioceranoic acid at m/z 775. Please see each inset for the proposed fragmentation processes leading to define the lipid structures

Acknowledgments This work was supported by NIH grants P30DK020579, P30DK056341, and R24GM136766 to Mass Spectrometry Resource of Washington University.

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References 1. Goren MB, Brokl O, Das BC, Lederer E (1971) SulfolipidI of Mycobaterium tuberculosis, strain H37Rv. Nature of the acyl substituents. Biochemistry 10:72–81 2. Goren MB, Brokl O, Roller P, Fales HM, Das BC (1976) Sulfatides of Mycobacterium tuberculosis: the structure of the principal sulfatide (SL-I). Biochemistry 15(13):2728–2735. https://doi.org/10.1021/bi00658a003 3. Minnikin DE, Dobson G, Sesardic D, Ridell M (1985) Mycolipenates and mycolipanolates of trehalose from Mycobacterium tuberculosis. J Gen Microbiol 131(6):1369–1374. https:// doi.org/10.1099/00221287-131-6-1369 4. Ariza MA, Martı´n-Luengo F, Valero-Guille´n PL (1994) A family of diacyltrehaloses isolated from Mycobacterium fortuitum. Microbiology 140(8):1989–1994. https://doi.org/10. 1099/13500872-140-8-1989 5. Frankfater C, Abramovitch RB, Purdy GE, Turk J, Legentil L, Lemie`gre L, Hsu F-F (2019) Multiple-stage precursor ion separation and high resolution mass spectrometry toward structural characterization of 2,3-diacyltrehalose family from Mycobacterium tuberculosis. Separations 6(1):4 6. Curr MI, Harwood JL, Frayn KN (2002) Lipid biochemistry: an introduction, 5th edn. Blackwell Science, Oxford, UK 7. Hsu F-F, Turk J (1999) Distinction among isomeric unsaturated fatty acids as lithiated adducts by electrospray ionization mass spectrometry using low energy collisionally activated dissociation on a triple stage quadrupole instrument. J Am Soc Mass Spectrom 10 (7):600–612. https://doi.org/10.1016/ s1044-0305(99)00041-0 8. Hsu F-F, Turk J (2008) Elucidation of the double-bond position of long-chain unsaturated fatty acids by multiple-stage linear ion-trap mass spectrometry with electrospray ionization. J Am Soc Mass Spectrom 19 (11):1673–1680. https://doi.org/10.1016/j. jasms.2008.07.007 9. Jensen N, Gross M (1986) Fast atom bombardment and tandem mass spectrometry for determing iso- and anteiso-fatty acids. Lipids 21(5):362–365. https://doi.org/10.1007/ bf02535702 10. Jensen NJ, Tomer KB, Gross ML (1985) Collisional activation decomposition mass spectra for locating double bonds in polyunsaturated fatty acids. Anal Chem 57(11):2018–2021. https://doi.org/10.1021/ac00288a004

11. Tomer KB, Crow FW, Gross ML (1983) Location of double-bond position in unsaturated fatty acids by negative ion MS/MS. J Am Chem Soc 105:5487–5488. https://doi.org/ 10.1021/ja00354a055 12. Rhoades ER, Streeter C, Turk J, Hsu F-F (2011) Characterization of sulfolipids of Mycobacterium tuberculosis H37Rv by multiplestage linear ion-trap high-resolution mass spectrometry with electrospray ionization reveals that the family of sulfolipid II predominates. Biochemistry 50(42):9135–9147. https://doi. org/10.1021/bi2012178 13. Ma X, Chong L, Tian R, Shi R, Hu TY, Ouyang Z, Xia Y (2016) Identification and quantitation of lipid C¼C location isomers: a shotgun lipidomics approach enabled by photochemical reaction. Proc Natl Acad Sci U S A 113(10):2573–2578 14. Poad BLJ, Zheng X, Mitchell TW, Smith RD, Baker ES, Blanksby SJ (2018) Online ozonolysis combined with ion mobility-mass spectrometry provides a new platform for lipid isomer analyses. Anal Chem 90 (2):1292–1300. https://doi.org/10.1021/ acs.analchem.7b04091 15. Bollinger JG, Rohan G, Sadilek M, Gelb MH (2013) LC/ESI-MS/MS detection of FAs by charge reversal derivatization with more than four orders of magnitude improvement in sensitivity. J Lipid Res 54(12):3523–3530. https://doi.org/10.1194/jlr.D040782 16. Bollinger JG, Thompson W, Lai Y, Oslund RC, Hallstrand TS, Sadilek M, Turecek F, Gelb MH (2010) Improved sensitivity mass spectrometric detection of eicosanoids by charge reversal derivatization. Anal Chem 82(16):6790–6796. https://doi.org/10.1021/ac100720p 17. Wang M, Han RH, Han X (2013) Fatty acidomics: global analysis of lipid species containing a carboxyl group with a charge-remote fragmentation-assisted approach. Anal Chem 85 (19):9312–9320. https://doi.org/10.1021/ ac402078p 18. Yang K, Dilthey BG, Gross RW (2013) Identification and quantitation of fatty acid double bond positional isomers: a shotgun lipidomics approach using charge-switch derivatization. Anal Chem 85(20):9742–9750. https://doi. org/10.1021/ac402104u 19. Tatituri RV, Wolf B, Brenner M, Turk J, Hsu F-F (2015) Characterization of polar lipids of Listeria monocytogenes by HCD and low-energy CAD linear ion-trap mass spectrometry with

Charge-Switch Derivatization for Fatty Acid Analysis electrospray ionization. Anal Bioanal Chem 407 (9):2519–2528. https://doi.org/10.1007/ s00216-015-8480-1 20. Hsu F-F (2016) Characterization of hydroxyphthioceranoic and phthioceranoic acids by charge-switch derivatization and CID tandem mass spectrometry. J Am Soc Mass Spectrom 27(4):622–632 21. Frankfater C, Jiang X, Hsu FF (2018) Characterization of long-chain fatty acid as N-(4-aminomethylphenyl) pyridinium derivative by

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Chapter 8 One-Pot Extractive Transesterification of Fatty Acids Followed by DMOX Derivatization for Location of Double Bonds Using GC-EI-MS Charles H. Hocart, Abdeljalil El Habti, and Gabriel O. James Abstract Fatty acids are an essential structural and energy storage component of cells and hence there is much interest in their metabolism, requiring identification and quantification with readily available instrumentation, such as GC-MS. Fatty acid methyl esters (FAMEs) can be generated and extracted directly from biological tissue, in a one-pot process, and following high resolution GC, their respective chain length, degrees of unsaturation, and other functionalities can be readily identified using EI-MS. Defining the positions of the double bonds in the alkyl chain requires conversion of the FAMEs into their respective dimethyloxazoline (DMOX) derivatives. Following EI, this derivative allows charge retention on the heterocycle, and concomitant charge remote fragmentation of the alkyl chain to yield key double bond position identifying ions. The protocols described herein have been applied to the identification and quantification of fatty acids harvested from microalgae grown to produce biofuels and to the screening of salt tolerant Arabidopsis mutants. Key words Fatty acids, Fatty acid methyl esters (FAMEs), Dimethyloxazoline (DMOX) derivatives, GC/MS, Identification, Quantification

1

Introduction Lipids and their constituent fatty acids are essential structural components of cellular membranes and are important forms of cellular energy storage. The biophysical properties of membranes, such as membrane fluidity, can be modified in response to changes in growth temperature by varying the acyl chain length, and the number and positions of the double bonds [1, 2]. These membrane changes are also reflected in the structure of the constituent fatty acids in lipid stores such as triacylglycerols (TAGs) which may be harvested from microalgae or plant seeds for biofuels or food [2, 3]. The fatty acid composition of membranes have also proved of interest in the study of selected plant mutants [4]. Some indication of the metabolic complexity of the biological fatty acids [5–7]

Fong-Fu Hsu (ed.), Mass Spectrometry-Based Lipidomics: Methods and Protocols, Methods in Molecular Biology, vol. 2306, https://doi.org/10.1007/978-1-0716-1410-5_8, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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and their structural diversity can be seen in online databases such as the Plant Fatty Acid database (PlantFAdb) [8], LIPID MAPS®Lipidomics Gateway [9], and the LipidWeb [10]. The methods of Bligh and Dyer [11] and Folch and coworkers [12] for the extraction of lipids from biological material using a mixture of methanol and chloroform, and modifications thereof, have proven popular. However, analysis of the lipid fatty acids then required further steps to hydrolyze and esterify to yield the fatty acid methyl esters (FAMEs). Later, Lewis and coworkers [13] demonstrated that the efficiency of the whole process could be dramatically improved by a direct one-pot transesterification/ extraction, saving time, money and solvents. FAMEs have proved very amenable to analysis by GC, as described in 1956 in the original pioneering work of James and Martin [14], as they have good volatility and chromatographic properties. Furthermore, when the GC is interfaced to a mass spectrometer (GC-EI-MS) the EI mass spectra of the FAMEs have found great utility in structural elucidation [15, 16]. However, structural features in the mass spectra of the FAMEs defining the position of double bonds are lacking because the double bonds tend to migrate during EI [15, 16]. A convenient solution to this problem is the use of heterocyclic derivatives in place of the methyl ester which allows charge retention on the heterocyclic moiety and charge remote fragmentation, yielding position identifying ions under EI [17, 18]. The dimethyloxazoline (DMOX) derivatives of fatty acids were first described by Zhang and coworkers [19] and, later, Fay and Richli [20] developed a procedure for directly converting FAMEs into DMOX derivatives. The DMOX derivatives have been found to have good GC characteristics and to yield diagnostic ions for the positions of cyclic and oxygenated moieties, double and triple bonds, and methyl branching in their respective EI mass spectra [17–21]. The procedures described in the protocol below bring together the one-pot transesterification/extraction procedure of Lewis and coworkers [13] to generate the FAMEs and the method of Fay and Richli [20] to generate the DMOX derivatives and complete the characterization of the extracted fatty acids. The advantage of the described methodology is that it represents the combination of simple chemistry with readily available GC-MS instrumentation to achieve identification and quantification of fatty acids. A further, and mostly unsung advantage of GC-EI-MS analysis, is the lack of matrix effects on the in-vacuum EI of analytes compared with, for example, electrospray ionization used in LC-MS [22–24].

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Materials and Equipment All solvents and reagents are analytical grade and water was deionized to a minimum conductivity of 18.2 MΩ cm. All reagents are freshly prepared at room temperature, in accordance with relevant safety precautions and regulations using appropriate personal protection equipment (lab coat, gloves, and safety glasses). Transesterification and extraction should be performed in a fumehood. Quantification is performed against an internal standard (IS) such as an odd numbered fatty acid, for example, heptadecanoic acid (17:0) that has previously been shown to be essentially absent in the sample (i.e., below the level of detection; see Note 1).

2.1 Preparation of Biological Samples

1. Benchtop centrifuge and centrifuge tubes (see Note 2). 2. Aluminum foil. 3. Liquid nitrogen. 4. Freeze dryer.

2.2 Transesterification and Extraction of FAMEs

1. Electronic balance. 2. Teflon tubes (10 mL) or glass reacti-vials (3 or 5 mL). 3. Chloroform. 4. Heptadecanoic acid (17:0) standard, 10 mg dissolved with CHCl3 in 50 mL volumetric flask. 5. 3 M Methanolic HCl (see Note 3). 6. Heating block or oven. 7. Water. 8. Extraction solvent, hexane/CHCl3; 4:1 (v/v). 9. Glass test tubes (10 cm
1.2 cm or similar). 10. Dichloromethane. 11. Sodium sulfate (anhydrous). 12. Nitrogen line. 13. GC autosampler vials.

2.3 Formation of DMOX Derivative

1. Glass reacti-vials (3 or 5 mL). 2. Nitrogen line. 3. 2-Amino-2-methyl-1-propanol. 4. Heating block or oven. 5. Water. 6. Extraction solvent, hexane/CHCl3; 4:1 (v/v). 7. Glass test tubes (10 cm
1.2 cm or similar). 8. Sodium sulfate (anhydrous).

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9. GC autosampler vials. 10. Dichloromethane. 2.4

GC/MS Analysis

1. Thermo Polaris Q GC-MS (see Note 4). 2. SGE BPX70 fused-silica capillary column (60 m
0.25 mm i. d.) coated with a 70% cyanopropyl polysilphenylenesiloxane bonded phase (film thickness 0.25μm) (see Note 5). 3. Analytical Software (Xcalibur, AMDIS), access to MS Libraries: Wiley Registry of Mass Spectral Data, NIST/EPA/NIH EI-MS Library, LIPID MAPS®Lipidomics Gateway [9], and the LipidWeb [10] (see Note 6). 4. GC injector wash solvents; acetone and dichloromethane.

3

Methods

3.1 Preparation of Biological Samples [2–4]

1. Separate cultured cells from the media by centrifugation (3200
g, 20 min, 25  C) (see Notes 2 and 7).

3.1.1 Cell Culture

3. Place the tubes containing the cell pellet into a bath of liquid nitrogen to freeze.

2. Decant the supernatant.

4. Cover top of centrifuge tube with a piece of aluminum foil and pierce the foil with a series of small holes. 5. Place samples in freeze dryer (2 days at 50 100–200 mTorr, or lower).



C and

6. Store samples at 80  C until ready for analysis. 3.1.2 Plant Tissue

1. Harvest required plant tissues (e.g., leaves, stems, roots) and immediately place into liquid nitrogen (see Note 8). 2. Transfer samples to the freeze dryer in tubes (use Pyrex or Teflon centrifuge tubes with pierced aluminum foil covering as for cell cultures in Subheading 3.1.1) (2 days at 50  C and 100–200 mTorr, or lower). 3. Store samples at 80  C until ready for analysis.

3.2 Extractive Transesterification to Generate Fatty Acid Methyl Esters (FAMEs) (Fig. 1) [3, 13]

1. Allow frozen and sealed samples to warm to room temperature, prior to homogenization in either a mortar and pestle or a ball mill (see Note 9). 2. Accurately weigh 2–3 mg of sample into a 10 mL Teflon tube (or 3 or 5 mL reacti-vial) (check integrity of seals). 3. Add 100μL CHCl3, followed by 60μL of the heptadecanoic acid IS (see Notes 1 and 10). 4. Add 1 mL of 3 M methanolic HCl (see Note 3) and carefully seal vial.

Extractive Derivatisation and Identification of Fatty Acids R3

O

O

109

O O

R1

OH

O

CH3

O R1

O

H+

O

R2

+

CH3-OH

HO OH

+

R2

O

CH3

O O triacylglycerol

glycerol

R3 O FAMEs

CH3

Fig. 1 One-pot hydrolysis and transesterification of dried biological material heated at 90  C for 60 min with 3 M methanolic HCl

5. Heat at 90  C for 60 min on a heating block or in an oven. 6. Allow to cool then add water (1 mL). 7. Extract FAMEs with hexane/CHCl3 (4:1, v/v; 3
2 mL) and combine organic phases (top layer) in a clean test tube (10 cm
1.2 cm or similar) (see Note 11). 8. Wash combined organic phase with water (1
2 mL). 9. Dry extract with a small spatula full of anhydrous sodium sulfate and transfer into a fresh test tube. 10. Carefully evaporate solvent under a dry nitrogen stream. 11. Re-dissolve dried residue in dichloromethane (400μL) and transfer to an autosampler vial for GC/MS analysis. 3.3 Dimethyloxazoline (DMOX) Derivatives from FAMEs (Fig. 2) [3, 18, 20, 21 ]

1. Transfer FAME solution from GC/MS autosampler vial to a reacti-vial (5 mL) and dry under a stream of nitrogen. 2. Add 2-amino-2-methyl-1-propanol (500μL). 3. Bubble nitrogen through solution to remove oxygen and carefully seal vial. 4. Heat at 180  C for 18 h on heating block. 5. Allow vials to cool, then add water (2 mL). 6. Extract with hexane/chloroform (4:1, v/v; 2
1 mL) and transfer solvent (top layer) to a clean glass test tube (10 cm
1.2 cm or similar) (see Note 11). 7. Wash combined organic phase with water (2
2 mL). 8. Dry solvent with small spatula full of anhydrous sodium sulfate and transfer into a fresh test tube. 9. Carefully evaporate solvent under dry nitrogen stream. 10. Re-dissolve dried residue in dichloromethane (200μL) and transfer to an autosampler vial for GC/MS analysis.

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O R

O FAME

CH3

N

OH

+

NH2

180 oC, 18 h

2-amino2-methyl-1-propanol

R

O

DMOX derivative

Fig. 2 DMOX derivatives synthesized from FAMEs by heating with 2-amino-2methyl-1-propanol at 180  C for 18 h, under nitrogen 3.4

GC/MS Analysis

1. Install the SGE BPX70 fused-silica capillary column (60 m
0.25 mm i.d.) coated with a 70% cyanopropyl polysilphenylenesiloxane bonded phase (film thickness 0.25μm) (see Note 5) following instrument manufacturer’s directions. 2. Install new GC inlet liner and septum (see Note 12). 3. Add fresh GC syringe wash solvents, dichloromethane and acetone (see Note 13). 4. Turn on the helium gas flow (15 psi inlet pressure) and check for leaks at injector and interface column connections with a leak detector. 5. Turn on the heating elements and set temperatures for the injection port (200  C), the GC-MS interface (250  C), and the GC oven (60  C). 6. Follow instrument manufacturer’s directions for starting up the mass spectrometer and set source temperature to 250  C (see Note 14). 7. Run the Auto Tune program to tune and calibrate the mass analyzer, and check for leaks. 8. Set up and save the GC-MS acquisition method: EI at 70 eV, source temperature 250  C, mass scan from m/z 50 to 500. The GC oven is temperature programmed from 60  C (hold 1 min) to 120  C at 30  C/min, then to 140  C at 5  C/min, then to 170  C at 2  C/min, then to 200  C at 1  C/min and then to 250  C at 10  C/min (see Note 15). Set injection volume (0.2–1.0μL) and program a needle wash before and after each injection with dichloromethane (6
5μL) followed by acetone (6
5μL). 9. Write and save sample list to run a trial sample and start an acquisition run. 10. Evaluate the trial chromatogram for resolution and time required for completion as described in Subheading 3.5.1 (see Note 16). 11. Adjust chromatograph oven temperature gradients and amount of sample injected on-column, in response to the chromatographic evaluation (step 10) and rerun.

Extractive Derivatisation and Identification of Fatty Acids

111

12. Repeat steps 10 and 11 as required to establish optimized GC-MS method. 13. Analyze samples with optimized method. 3.5

Data Analysis

3.5.1 Evaluation of Chromatography

1. Using the analytical software supplied with the GC-MS, generate a Total Ion Current (TIC) chromatogram, as per the examples in Figs. 3 and 4. 2. Evaluate the chromatogram for resolution, intensity, and amount of added IS (see Note 16).

3.5.2 Generating Suitable Mass Spectra and Assigning Identification

1. Load each sample file into AMDIS to deconvolute spectra and match spectra against electronic EI libraries. Manual confirmation can be obtained by comparison with EI mass spectra described online at Lipid Maps [9] and LipidWeb [10] and published in [15, 16, 18–21] (see Note 17).

3.5.3 Quantification

1. Using the analytical software supplied with the GC-MS, integrate the peaks in the TIC to obtain their respective areas. 2. Calculate the quantity of individual sample components as a proportion of the IS (see Note 18).

Fig. 3 Total ion current (TIC) from GC-EI-MS analysis of FAMEs derived from Arabidopsis seed (see also Table 1)

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Fig. 4 TIC from GC-EI-MS analysis of DMOX derivatives of FAMEs (see also Table 2)

4

Notes 1. An excellent discussion of quantification using mass spectrometry is to be found in [25] and for fatty acids, in particular, the analyst is directed to [26]. 2. Centrifuge tubes need to be of an appropriate volume and number to accommodate the size of sample to be collected. Tubes will need to be capable of being snap frozen in liquid nitrogen (195.79  C) prior to freeze-drying (Pyrex and Teflon tubes are recommended). 3. Methanolic HCl can be made in situ by the addition of acetyl chloride to cold, dry methanol but trace amounts of methyl acetate are also formed [17]. The use of commercially available 3 M methanolic HCl (see for example, Supelco or SigmaAldrich) is recommended for reasons of safety and consistency. 4. This protocol is not limited to the use of an ion trap mass analyzer, as described here, but is applicable to any GC-EIMS, preferably interfaced to an autosampler, with one or more combinations of quadrupole, time-of-flight or orbitrap mass analyzers. 5. The analyst is spoilt for choice in selecting a column for FAMEs separations as most GC column manufacturers produce at least one such column in a range of different dimensions (length and

Extractive Derivatisation and Identification of Fatty Acids

113

internal diameter) and film thicknesses. Possibilities include, for example, SGE BPX 70, Agilent HP-88, Supelco SP-2560, and Phenomenex Zebron ZB-FAME. If your separation is particularly challenging, then consider contacting the respective chromatography company and ask to send a test sample to their applications department or to borrow a demonstration column. For difficult chromatographic separations, consider using a longer column with a smaller internal diameter and thinner bonded phase film thickness. 6. Commercial GC-MS instruments are normally accompanied with vendor specific software, e.g., Thermo and Xcalibur; Agilent and MSD ChemStation Data Analysis; Leco and ChromaTOF; Waters and MassLynx; PerkinElmer and TurboMass; Shimadzu and GCMSsolution, for instrument operation, data acquisition, and data analysis. The Wiley Registry of Mass Spectral Data and the NIST/EPA/NIH EI-MS Library are commercially available through instrument vendors and distributors. The AMDIS deconvolution software is available as a free download (https://chemdata.nist.gov/dokuwiki/doku. php?id¼chemdata:start. Accessed 23 Aug 2020). 7. Cell cultures can also be collected by filtration but losses will occur in removing the cells from the filter surface. Plus there is the added problem of filter fragments or residues being included with the collected cells. 8. Mature seeds do not require freeze-drying and may be stored dry at room temperature. 9. The use of a ball mill is recommended for speed and consistency of homogenization. Differences in the latter may have implications for variability in the yield of the transesterification/extraction process. 10. The amount of IS added to the sample (60μL of 10 mg IS dissolved in 50 mL) may be calculated as follows: 10 mg 1000

60 μL ¼ 12 μg IS added to each sample 50 mL 1000 The amount of internal standard (IS) to be added will vary depending on the sample to be analyzed and may require some experimentation to establish a suitable level for quantification (aim to have peak areas of the analytes within a factor of 10 of the area of the IS) (see Subheading 3.5 and Note 16). 11. Solvent layers will separate on standing, otherwise tubes may be centrifuged. If no layers are apparent, add additional organic solvent or water or salt (NaCl or KCl). 12. The GC inlet liner and septum are consumables and should be regularly changed as required.

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13. The GC syringe wash solvents are refreshed for each analytical run and the tops of the containers should be covered with aluminum foil in preference to using septa, eliminating any chance of contamination from a degraded septa. Other combinations of wash solvents may be used but should include the solvent in which the FAMEs are dissolved. 14. For best analytical results, the GC/MS source is regularly cleaned, maintained and tuned in accordance with the manufacturer’s instructions. In addition, the GC septum, inlet liner, and injection syringe are changed at regular intervals as required. 15. Most importantly, take care not to exceed the maximum operating temperature of the column in any of the temperature settings or temperature program; 250  C for the BPX70 column. 16. The trial chromatogram should be evaluated for peak resolution, peak intensity, and time taken to complete analysis. Ideally each peak should be symmetrical and baseline resolved. For complex samples, shallower, longer GC oven temperature gradients should be used to optimize the chromatographic separations (i.e., maximize peak resolution). For less complex samples, faster temperature gradients may be used, thus resulting in shorter GC-MS analysis time. Intense, asymmetrical (fronting) peaks may be indicative of an over-loaded column, in which case either the sample injection volume should be reduced or the sample diluted. Using the injector sample split can result in discrimination against higher molecular weight components in the sample. Additionally, check that a suitable amount of internal standard has been added to the sample such that the areas of the most and least intense peaks are within a factor of 10 of the area of the IS peak. 17. The AMDIS deconvolution software, along with instructions for its use, is available as a free download (https://chemdata. nist.gov/dokuwiki/doku.php?id¼chemdata:start. Accessed 23 Aug 2020). Manually confirm identification against online and published compendia of fatty acid mass spectra [9, 10, 15, 16, 18–21]. Automated library matching identification software should be used with caution as the key identifying ions for the fatty acid derivatives may be of relatively low intensity (Figs. 5 and 6, Tables 1 and 2). For example, apart from the molecular ion [M]+., [M29]+., [M31]+., and [M43]+. ions, the mass spectra of the saturated FAMEs are very similar, especially when the identifying ions are of low intensity and possibly obscured in the background, making the automated

Extractive Derivatisation and Identification of Fatty Acids

A

115

74

FAME 18:0, MW 298 101

-Me

[M-43]+. [M-29]+.

Relative Abundance

87

[M]+.

DMOX 18:0, MW 337

B

113.07

100 90 80

113

70

[M-15]+.

98.08

60

126.14

322.21

50 294.29

40 72.09 30 20 83.03

[M]+.

182.14

140.12

224.26

10

292.33

337.30

0 50

100

150

200

250

300

350

400

m/z

Fig. 5 Mass spectra of the respective FAME (a) and DMOX (b) derivatives of the saturated stearic acid (18:0)

identification problematic. Thus a robust identification requires a consideration of the retention times as well as the mass spectra. Access to authentic standards will be of great assistance for Rt and MS comparisons but commercial availability may be limited to the most common fatty acids.

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Fig. 6 Mass spectra of the respective FAME (a) and DMOX (b) derivatives of the unsaturated linoleic acid (18:2Δ9,12)

Extractive Derivatisation and Identification of Fatty Acids

117

Table 1 The FAMEs derived from Arabidopsis seed were identified based on a combination of the [M]+., [M29]+., [M31]+., and [M43]+. ions for the saturated FAMEs (Fig. 5a), and the [M]+., [M31]+., and [M32]+. ions (Fig. 6a) for the unsaturated FAMEs FAMEa, b

Unsaturated FAMEa, b

Saturated FAME

Rt (min) [M]+.

[M29]+.

[M31]+.

[M43]+.

[M31]+.

[M32]+.

14:0

8.34

242

213

211

199

16:0

10.38

270

241

239

227

16:1Δ7

10.69

268

–

–

–

237

236

9

10.79

268

–

–

–

237

236

17:0 (IS)

11.39

284

255

253

241

18:0

12.39

298

269

267

255

16:1Δ

18:1Δ9

12.76

296

–

–

–

265

264

18:1Δ

11

12.81

296

–

–

–

265

264

18:2Δ

9,12

13.38

294

–

–

–

263

262

18:2Δ

9,12

13.83

294

–

–

–

263

262

18:3Δ

9,12,15

14.13

292

–

–

–

261

260

20:0

14.31

326

297

295

283

11

14.67

324

–

–

–

293

292

20:1Δ13

20:1Δ

14.72

324

–

–

–

293

292

20:2Δ

11,14

15.27

322

–

–

–

291

290

20:3Δ

11,14,17

15.96

320

–

–

–

289

288

16.06

354

325

nd

311

22:1Δ13

16.43

352

–

–

–

321

320

22:1Δ

10

16.87

352

–

–

–

321

320

22:2Δ

14,17

17.00

350

–

–

–

319

318

24:0

17.73

382

353

nd

339

24:1Δ15

18.06

380

–

–

–

349

348

26:0

19.25

410

nd

nd

367

22:0

nd not detected. a Double bond positions were determined from the respective mass spectra of the DMOX derivatives (Figs. 4–6 and Table 2) and by comparison with library standards and published mass spectra [9, 10, 15, 16, 18–21]. b Geometric configuration of the double bonds (cis- and trans-isomers) cannot be determined from the respective mass spectra (see for example two peaks identified as 18:2Δ9,12 representing different combinations of cis/trans double bond isomers eluting at different retention times).

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Table 2 FAMEs were converted to their respective DMOX derivatives and the position of the double bond assigned from the DMOX mass spectra either from first principles or in combination with library spectra (Figs. 5b and 6b). The one exception to this was an additional 18:3Δ9,12,15 identified in the DMOX chromatogram, representing a different combination of cis/trans isomers (Fig. 4) possibly arising from isomerization during the derivatization process. The DMOX derivatives elute at higher temperatures than the FAMEs but the order of elution matches that of the FAMEs (cf. Figs. 3 and 4)

DMOX derivative

+.

+.

[M]

14:0

9.59

281

266

16:0

11.58

309

294

16:1Δ7

11.82

307

292

(180, 168)

9

11.94

307

292

(208, 196)

17:0 (IS)

12.54

323

308

18:0

13.48

337

322

18:1Δ9

[M15]

db position diagnostic ion pairsa,b (Δm ¼ 12)

Rt (min)

16:1Δ

13.77

335

320

(208, 196)

18:1Δ

11

13.89

335

320

(236, 224)

18:2Δ

9,12

14.35

333

318

(248, 236), (208, 196)

18:2Δ

9,12

14.40

333

318

(248, 236), (208, 196)

18:3Δ9,12,15

14.80

331

316

(288, 276), (248, 236), (208, 196)

9,12,15

15.06

331

316

(288, 276), (248, 236), (208, 196)

18:3Δ 20:0

15.28

365

350

11

15.57

363

348

(236, 224)

20:1Δ13

20:1Δ

15.69

363

348

(264, 252)

20:2Δ

11,14

16.14

361

346

(276, 264), (236, 224)

20:3Δ

11,14,17

16.81

359

344

(316, 304), (276, 264), (236, 224)

16.95

393

378

22:1Δ13

17.26

391

376

(264, 252)

22:1Δ

10

17.69

391

376

(222, 210)

22:2Δ

14,17

17.99

389

374

(318, 306), (278, 266)

24:0

18.24

421

406

24:1Δ15

18.80

419

404

26:0

20.42

449

434

22:0

a

a, b

(292, 280)

Double bond (db) positions determined by MS interpretation and by comparison with standards and published mass spectra [9, 10, 15, 16, 18–21]. b Geometric configuration of the double bonds (cis- and trans-isomers) cannot be determined from the respective mass spectra but may be differentiated on the basis of GC retention time.

Extractive Derivatisation and Identification of Fatty Acids

119

The FAMEs are eluted in order of, firstly, the alkyl chain length, shorter chains first, as volatility decreases with increasing chain length (and molecular weight). The addition of double bonds to the carbon chain increases the polarity of the FAME and its retention in the GC bonded phase so they elute later than their saturated analogue. Thus for the same chain length, unsaturated FAMES will be eluted in order of increasing numbers of double bonds (Fig. 3 and Table 1). The same elution order is observed for the DMOX derivatives as for the FAMEs but as the methyl ester moiety has been replaced by the slightly more polar and heavier oxazoline moiety, the elution of the DMOX derivatives occurs at a later Rt (i.e., higher temperature) than the original FAME for the same temperature program (Fig. 4 and Table 2). Confirmation of the assigned FAME identity can be obtained from the corresponding DMOX derivatives along with the number and position of double bonds (Fig. 6b and Table 2) in the unsaturated fatty acids. Again, the published and online compendia of fatty acid mass spectra should be consulted [9, 10, 15, 16, 18–21]. A useful aid in locating minor sample components is to plot ion current profiles to target a specific analyte, for example, in looking for methyl stearate (18:0), one might target m/z 298 ([M]+.) and 269 ([M29]+.) and for methyl linoleate (18:2Δ9,12), m/z 294 ([M]+. and 262 ([M32]+. (Table 1, Figs. 5a and 6a). In the case of the respective DMOX derivatives, one can usefully plot m/z 98, 113, and 126 (Figs. 5b and 6b), ions which are common to all DMOX derivatives. An obvious additional aid in searching for minor sample components can be to inject larger or more concentrated samples onto the GC column. It should be noted that the geometric configurations (cis/ trans) of double bonds cannot be determined from the mass spectra of either the FAME or DMOX derivatives. However, they may be differentiated by their respective Rt in a high resolution GC separation but appropriate standards will be required for comparative purposes. 18. The challenge with quantifying the FAMEs is that there may be no readily available standard for many of the identified sample constituents, limiting the ability to determine a complete set of response factors and the construction of individual calibration curves. However, a quick and approximate quantification of the individual fatty acids against the internal standard (IS), based on the assumptions of (1) similar response factors for the analytes and the IS and (2) a linear calibration curve, may be obtained by, firstly, calculating the amount of IS added to the

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sample (12μg, see Note 10), then determining the quantity of analyte (X) as a proportion of the IS GC peak area, AreaIS. 12 μg AreaX 1

1 AreaIS tissue weight ðgÞ ¼ μg of X =g dried tissue weight

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Extractive Derivatisation and Identification of Fatty Acids chain olefinic acids. Biomed Mass Spectrom 15 (1):33–44 20. Fay L, Richli U (1991) Location of double bonds in polyunsaturated fatty acids by gas chromatography-mass spectrometry after 4,4-dimethyloxazoline derivatization. J Chromatogr 541:89–98 21. Spitzer V (1997) Structure analysis of fatty acids by gas chromatography – low resolution electron impact mass spectrometry of their 4,4-dimethyloxazoline derivatives – a review. Prog Lipid Res 35(4):387–408 22. Tokuoka SM, Yasumoto A, Kita Y, Shimizu T, Yatomi Y, Oda Y (2020) Limitations of deuterium-labeled internal standards for quantitative electrospray ionization mass spectrometry analysis of fatty acid metabolites. Rapid Commun Mass Spectrom 34:e8814. https:// doi.org/10.1002/rcm.8814

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23. Taylor PJ (2005) Matrix effects: the Achilles heel of quantitative high-performance liquid chromatography–electrospray–tandem mass spectrometry. Clin Biochem 38:328–334 24. Tsizin S, Fialkov AB, Amirav A (2020) Analysis of impurities in pharmaceuticals by LC-MS with cold electron ionization. J Mass Spectrom 55. https://doi.org/10.1002/jms.4587 25. Boyd RK, Basic C, Betham RA (2008) Trace quantitative analysis by mass spectrometry. John Wiley & Sons, Chichester 26. Quehenberger O, Armando AM, Dennis EA (2011) High sensitivity quantitative lipidomics analysis of fatty acids in biological samples by gas chromatography–mass spectrometry. Biochim Biophys Acta 1811:648–656

Chapter 9 Ceramide Analysis by Multiple Linked-Scan Mass Spectrometry Using a Tandem Quadrupole Instrument Fong-Fu Hsu Abstract Ceramides are a special class of sphingolipids and play a central role in sphingolipid metabolism, and have diverse structures. In this book chapter, tandem quadrupole mass spectrometric approaches applying multiple linked scannings including various constant neutral loss scan (NLS) and precursor ion scan (PIS), the unique applicable feature of a triple-stage quadrupole (TSQ) instrument for analysis of ceramides desorbed as [M H] and [M+Li]+ ions are described. These multiple dimensional tandem mass spectrometric approaches are fully adapted to the conventional shotgun lipidomics workflow with minimal or without prior chromatographic separation to profile ceramide molecules, and thus detection of a whole class of ceramide or various specific ceramide subclasses in crude lipid extract can be achieved. With addition of internal standard(s), semi-quantitation of ceramide in the lipid extract of biological origin is possible. Examples have shown promise in ceramide profiling of several whole lipid extracts from porcine brain, the model Dictyostelium Discoideum cells for cancer study, and skin. Key words Epidermal ceramides, Mass spectrometry, Precursor ion scan, Neutral loss scan, Linked scan, Multiple dimensional tandem mass spectrometry, Brain ceramides

1

Introduction Ceramides in biological systems not only play important roles in the sphingolipid metabolism [1], but also play roles as lipid mediators. Ceramide acts as a second messenger in activating apoptosis or growth suppression, immune responses and regulating specific responses to agonists [2–7]. Epidermal ceramides in the mammalian skin are the major class of lipids that play diverse roles in the outermost layers of the skin function including water retention and provision of a physical barrier critical to the permeability barrier of the skin [8–11].

Fong-Fu Hsu (ed.), Mass Spectrometry-Based Lipidomics: Methods and Protocols, Methods in Molecular Biology, vol. 2306, https://doi.org/10.1007/978-1-0716-1410-5_9, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Ceramides constitute a family of closely related molecules with a variety of long chain bases (LCB), to which various fatty acids are attached via an amide linkage. Ceramides differ in chain length, type and degree of hydroxylation and saturation, and separation of ceramides by chromatographic means including the conventional thin layer chromatography (TLC), solid-phase extraction (SPE) (see Chapter 10 of this book) and HPLC is difficult, and analysis of individual species can be challenging. Therefore, there is a need to establish a strategy to implement the conventional MS-based shotgun lipidomic workflow with direct infusion or loop injection for the analysis. Triple-stage quadrupole (TSQ) mass spectrometry is unique in its qualitative and quantitative capability by performing linked scannings, including product ion scan, multiple reaction monitoring (MRM), precursor ion scan (PIS), and neutral loss scan (NLS) [12]. The PIS and NLS approaches apply signature ion scans for identification and semi-quantitation (with addition of internal standard) of specific molecular species or family in mixtures with no or little prior chromatographic separation. In this chapter, direct tandem quadruple mass spectrometric approaches applying multiple dimensional PIS and NLS for global ceramide analysis and identification of specific ceramide family and subclasses are described. TSQ instrument is relatively simple to operate and more cost effective and thus, the method can be easily implemented in laboratories with the instrument without other expensive and sophisticated setups, such as HPLC.

2

Materials Solvents are of HPLC or GC grade. All mobile phase solvent mixtures are indicated in v/v ratios. Glass vials and centrifugation tubes with screw caps all contain a PTFE/TFE liner. All lipid standards and samples are stored at 20  C and only glassware is used.

2.1 Ceramide Preparation and Extraction

Please refer to Chapter 10 for epidermis ceramide extraction, classification, and nomenclatures. 1. Gibco Dulbecco’s phosphate buffer saline DPBS (1) (Sigma, St. Louis, MO, USA). 2. Lyophilizer. 3. Nitrogen evaporator (Organomation Associates, Inc., Berlin, MA, USA). 4. 2:1 (v/v) Chloroform:methanol.

Multiple Linked Scans with TSQ for Ceramide Analysis

2.2 Other Crude Ceramide Mixtures

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1. Natural bovine ceramide mixture (Matreya LLC, State college, PA, USA) (see Note 1). 2. Total porcine brain lipid extract (Avanti polar lipids, Alabaster, AL, USA). 3. Lipid extract from Dictyostelium Discoideum cells.

2.3 Mass Spectrometric Analysis

1. Thermo Scientific (Waltham, MA, USA) TSQ Vantage ULTRA-EMR (mass range: 10–3000 amu) triple-stage quadrupole mass spectrometer with S-Lens design and heated electrospray ionization probe (H-ESI II) coupled with Thermo Accela-1250 UHPLC/Autosampler (see Note 2). 2. Syringe pump (Harvard PHD 2000 Infusion, Plymouth Meeting, PA, USA). 3. 1% NH4OH in methanol (see Note 3). 4. 5 nM 7LiOH in 95% methanol (see Notes 4 and 5).

3

Methods All experiments are carried out at room temperature unless otherwise specified. Experiments that require organic solvents are performed in a hood.

3.1 Extraction of Ceramide from Dandruff Flakes

1. Weigh samples (~20 mg) and place in an 8 mL centrifuge tube. 2. Wash with 3  5 mL PBS (Dulbecco’s PBS) solution. 3. Perform Bligh & Dyer extraction [13] by adding 1 mL of chloroform and 2 mL of methanol to make a 1:2:0.8 chloroform:methanol:water single-phase solvent mixture. 4. Vortex at the maximal speed for 3 min, followed by 2 h shaking. 5. Add 1 mL of chloroform and 1 mL of water to make a final 2:2:1.8 chloroform:methanol:water two-phase solution. 6. Vortex for 1 min, followed by 5 min centrifugation at 800  g. 7. Transfer the lower organic phase (~1.5 mL) to a 1 drum glass vial. 8. Blow to dry in a nitrogen evaporator, resuspend in 1 mL of 1:1 chloroform:methanol before use.

3.2 Preparation of Crude Ceramide for Linked-Scan Data Acquisition

1. Total porcine brain lipid extract: 1μg/μL in methanol. 2. Natural bovine ceramide mixture: 50 ng/μL. 3. Crude lipid extract from dandruff flake: 1 mL. 4. Lipid extract from Dictyostelium discoideum cells: 1 mL. 5. Lipid extracts prepared for detection of ceramides as [M H] ions by precursor ion scan (PIS) and constant neutral loss scan (NLS) in the negative-ion mode.

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(a) Dissolve each crude ceramide/lipid extract in methanol with equal volume of 1% NH4OH in methanol. (b) Vortex at full speed for 1 min. 6. Lipid mixtures prepared for linked scannings to detect ceramide (total ceramide or specific ceramide subclasses) as the [M+Li]+ ions in the positive-ion mode. (a) Dissolve each crude ceramide/lipid mix in methanol with equal volume of 5 nM 7LiOH in 95% methanol before injection. (b) Vortex at full speed for 1 min. 3.3 Mass Spectrometric Analysis Using Linked Scannings with a TSQ Instrument

Perform on a Thermo Scientific TSQ Vantage EMR with Xcalibur 2.0.1. operating system. Instrument (Q1 and Q3) is tuned and calibrated with “Autotune” by continuous infusion of tyrosine 1,3,5 calibration kit (supplied by Thermo fisher) at 5μL/min to pass the instrument specification and save the tune file. Input the linked-scan transitions (Tables 1 and 2) for fishing out all ceramide species or specific ceramide subclasses previously established by optimization of collision energy, target gas pressure, and scan speed for various ceramides desorbed as [M H] ions in the negative-ion mode or as [M+Li]+ ions in the positive-ion mode (see Notes 6 and 7) [14–18]. All spectra are acquired in the profile mode. Samples are loop injected or continuously infused (see Note 8). 1. Neutral Loss Scan (NLS) in the positive-ion mode (Table 1). (a) ESI needle voltage: 3.5 kV. (b) Heated capillary inlet temperature: 300  C. (c) Q1 and Q3 mass window setting (full width at half maximum; FWHM): 0.75 Da. (d) Scan speed: 2 s/spectrum. (e) Argon target gas pressure: 1.2–1.5 mtorr. (f) Skimmer voltage: off. (g) Collision energy: 40–50 eV (lab frame) (optimized to the maximum sensitivity). 2. Constant NLS in the negative-ion mode (Table 2). (a) ESI needle voltage: 2.5 kV. (b) Other settings: same as those applied in the positive-ion mode, excepting collision energy is about 5 eV lower. 3. Precursor Ion Scan (PIS) in the positive-ion mode (Table 1). (a) ESI needle voltage: 3.5 kV. (b) Heated capillary temperature: 300  C.

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Table 1 Linked scannings applicable for identification of ceramides and specific ceramide subclasses as [M+Li]+ ions Ceramide (LCB/FA)

a

LCB/FA

All ceramides CNL

48

45–50

d18:1/nFA.

Cer[NS]

CNL CNL PIS

282 256 264

45–50 45–50 45–50

d20:1/nFA

Cer[NS]

CNL CNL PIS

310 284 292

45–50 45–50 45–50

d18:1/αhFA

Cer[AS]

CNL PIS PIS

327 306 298

45–50 45–50 45–50

Subclass

Collision Type of scan Scan mass energy (eV) Comments For all ceramide detection

Specific; good sensitivity Specific

d18:0/αhFA

Cer[AdS]

PIS

308

45–50

d18:1/βhFA

Cer[BS]

PIS PIS

348 330

45–50 45–50

Specific Specific

t18:1/ωhFA

Cer[OH]

CNL CNL CNL PIS

66 298 316 286

45–50 45–50 45–50 45–50

Specific Specific Specific

t18:1/αhFA

Cer[AH]

PIS PIS CNL

322 305 343

45–50 45–50 45–50

t18:0/nFA

Cer[NP]

CNL CNL PIS PIS

36 246 263 289

45–50 45–50 45–50 45–50

PIS CNL PIS

291 345 324

45–50 45–50 45–50

t18:0/αhFA

Cer[AP]

Specific

a

Please refer to Chapter 10 for ceramide subclass abbreviation

(c) Q1 mass window setting: 0.7 Da (FWHM); Q3 mass window setting 1.5 Da (FWHM) (see Note 9). (d) Argon target gas pressure: 0.7 mtorr (see Note 10). (e) Skimmer voltage: off. (f) Collision energy: optimized to the maximum sensitivity, and the typical voltage is between 40–50 eV (lab frame). 4. Precursor Ion Scan (PIS) in the negative-ion mode (Table 2). (a) ESI needle voltage: 2.5 kV. (b) Other settings: same as those applied in the positive-ion mode, excepting collision energy is 5 eV lower.

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Table 2 Linked scannings applicable for identification of ceramides and specific ceramide subclasses as [M H] ions Ceramide (LCB/FA) structure identified

a

LCB/FA

All ceramides NLS

30

35

Not observed for Cer[NdS] and Cer[AdS]; less sensitive for Cer[BS], Cer[AS] and Cer[AP]

LCB/FA

All ceramides NLS

32

35

Good sensitivity with Cer[NdS] and Cer[AdS]

d18:1/FA

Cer[NS]

NLS

256

35

For both d18:1/nFA and d18:1/hFA-ceramides

d18:1/nFA

Cer[NS]

NLS

256

45

General method for screening d18:1/nFA-Cer

d18:1/ωhFA

Cer[OS]

NLS

256

45

d18:0/nFA

Cer[NdS]

NLS

258

45

NLS

254

45

Subclass

d18:2/nFA

Type of Scan Collision scanning mass energy (eV)

General method for screening d18:0/nFA-Cer General method for screening d18:2/nFA-Cer

d20:1/nFA

Cer[NS]

NLS

284

45

General method for screening d20:1/nFA-Cer

t18:0/nFA

Cer[NP]

NLS

256

45

t18:1/ωhFA

Cer[OH]

NLS NLS PIS

246 272 279

45 45 45

Specific; low sensitivity Specific; low sensitivity Specific; good sensitivity

d18:1/αhFA d20:1/αhFA

Cer[AS]

NLS NLS

327 355

45 45

Specific; good sensitivity Specific; good sensitivity

d18:0/αhFA d20:0/αhFA

Cer[AdS]

NLS NLS

329 357

45 45

Specific; good sensitivity Specific; good sensitivity

t16:0/αhFA

Cer[AP]

NLS

317

45

t17:0/αhFA

Cer[AP]

NLS

331

45

t18:0/αhFA

Cer[AP]

NLS NLS

345 299

45 45

t19:0/αhFA

Cer[AP]

NLS

359

45

t20:0/αhFA

Cer[AP]

NLS

373

45

t21:0/αhFA

Cer[AP]

NLS

387

45

t22:0/αhFA

Cer[AP]

NLS

401

45

Specific

t18:1/αhFA

Cer[AH]

NLS NLS

30 343

40 40

Specific

Specific

Specific

(continued)

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Table 2 (continued) Ceramide (LCB/FA) structure identified

a

t19:1/αhFA

Cer[AH]

NLS

357

40

t20:1/αhFA

Cer[AH]

NLS

371

40

t22:1/αhFA

Cer[AH]

NLS

399

40

Subclass

Type of Scan Collision scanning mass energy (eV)

Specific

a

Please refer to Chapter 10 for ceramide subclass abbreviation

5. Data processing. (a) Process data with the Thermo Xcalibur software. (b) Render 5-point Gaussian smooth for the profile mass spectra (see Note 11). (c) Identify ceramide ion species (see Note 12), and calculate individual peak height ratio to internal standard (IS) for semi-quantitation (if IS is added for quantitation).

4

Notes 1. According to the vendor’s data sheet, the mix contains both N-α-hydroxy-fatty acyl (hFA) sphingosine (d18:1/hFA-Cer) and N-non-hydroxy fatty acyl (nFA) sphingosine (d18:1/ nFA-Cer) subclasses extracted from bovine spinal cord. 2. The online coupling with UPLC and autosampler are suitable for automatic analysis of a large batch of samples. The UPLC is set to deliver constant flow without LC column and samples are loop injected by the autosampler. For a small batch of samples, either automation or manual operation with syringe pump and loop injection can be used. For direct infusion, samples are filled in a gas-tight syringe and delivered to ion source by a syringe pump (also see Note 8). 3. 1% NH4OH in methanol is used to enhance ceramide [M H] ion formation and suppress formation of other adduct ions such as [M+Cl] ions, which can otherwise dominate if CHCl3, for example, is used as the solvent (Cl ion can be artificially formed from CHCl3 by ESI). 4. Naturally occurring lithium is a mixture of two stable isotopes 6 Li and 7Li with natural abundances of 7.6% and 92.4% respectively. To avoid formation of both 13C6Li and 12C7Li isotopomers that complicate the analysis, 7LiOH should be used. 5. 7LiOH is preferred to 7LiOAc or 7LiHCO2, because the formation of the [M+Li]+ ions using the latter two as Li+ additive is less exclusive and may also result in the undesired [M+H]+

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and [M+H H2O]+ ions. Another advantage to use LiOH is that after switching to negative-ion mode, the [M H] ions are readily formed. With the use of LiOAc or LiHCO2 when switch to negative ion ESI, ceramides are formed as [M+OAc] or [M+HCO2] adduct ions, and the intensity is dependent on the ceramide subclasses [16]. 6. The relative intensities of the ion species in the linked-scan MS2 spectra across different ceramide subclasses do not necessarily reflect the real ceramide amounts due to discrimination in the detection among the various ceramide subclasses, and the variation of the optimal collision energy required for the ceramides in the same subclass with different chain length. 7. Linked scans for specific ceramide subclasses are the scans that fish out specific ceramide subclasses in a mixture. The specific scans for detecting specific ceramide subclasses as [M H] ions in the negative-ion mode are listed in Table 1, and the specific scans for detecting specific ceramide subclasses as [M+Li]+ ions in the positive-ion mode are listed in Table 2. These two tables are established by the product ion spectrum of ceramide molecules in various ceramide subclasses as the [M H] and [M+Li]+ ions, respectively, and are based on the sensitivity and specificity of the scanned ions that can extract the molecular species [14–16]. 8. Use gas-tight syringe to maintain constant and stable flow to obtain stable MS signal. For loop injection, a constant flow of 10–20μL/min 0.5% NH4OH in methanol (negative-ion mode) or 5 nM 7LiOH methanolic solution (positive-ion mode) are continuously infused onto the mass spectrometer by a syringe pump. For direct infusion, a flow of 2–10μL/min can be used and the higher the flow rate the more stable the MS scan. 9. These Q1 and Q3 mass window settings give the best sensitivity, while the precursor-ion scan spectra with unit mass resolution can be obtained [19]. 10. The Ar target gas pressure of Q2 collision cell is set at 0.6–0.8 mtorr to minimize multiple collision dissociations, and the full width at half maximum (FWHM) of Q1 and Q3 is set at 0.7 and 1.5 Da, respectively, to obtain a baseline resolved PIS spectrum with best sensitivity. Otherwise, a severe peak tailing (loss of resolution) and loss of sensitivity can occur if the settings outside the range are implemented [19]. 11. A 5-point smoothing gives enough smoothing with minimal loss of resolution. 12. The linked-scan spectra obtained by various PIS and NLS to fish out various ceramide subclasses in the mixture of total ceramides extracted from bovine spinal cord are shown in Figs. 1, 2, and 3. The “pros” and “cons” of screening

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Fig. 1 (a) The ESI-MS spectrum of the bovine spinal cord ceramide extract detected as the [M+H]+ ions in the positive-ion mode, and the tandem mass spectra obtained by PIS of 264 (b) which detects the d18:1/FA-Cer families, and (c) PIS of 266 which fish out the d18:0/FA-Cer families. All spectra are dominated by the [M +H H2O]+ ions due to facile water loss upon subjected to ESI, along with the [M+H]+ ions that are less

Fig. 2 (a) The ESI-MS spectrum of the bovine spinal cord ceramide extract detected as the [M H] ions in the negative-ion mode. The specific d18:1/FA-Cer class consisting of d18:1/nFA-Cer and d18:1/hFA-Cer (labeled in red) subclasses in the extract is seen in the linked-scan MS2 spectra obtained by (b) NLS of 256 with 35 eV ä Fig. 1 (continued) prominent. This approach to analyze ceramides using the protonated species in the positive-ion mode has been described in the literature [20–22], but the method has many faults. For example, the ion of m/z 648 is present in panels (a)–(c), indicating the molecule represents both a d18:1/24:1- and d18:0/24:2-Cer, but has in fact been artificially generated from d18:1/h24:0- and d18:0/h24:1-Cer by loss of H2O. Other noticeable fault is the disparity in the water loss among the various ceramide subclasses upon ESI, for example, water loss is less facile in the Cer[AdS] than Cer[AS] and Cer[NS], as both the ions of m/z 568 (d18:0/18:0-Cer) and 550 (loss of H2O) are present in panel (c), while the [M+H]+ ions of m/z 566 (d18:1/ 18:0-Cer), and m/z 582 (d18:1/h18:0-Cer) are absent in panel (b). Therefore, MS-based approach to analyze ceramide as [M+H]+ ions (or the [M+H H2O]+ ions) is not recommended

Multiple Linked Scans with TSQ for Ceramide Analysis

133

Fig. 3 (a) The ESI-MS spectrum of the bovine spinal cord ceramide extract detected as the [M+Li]+ ions in the positive-ion mode, and the tandem mass spectra obtained by (b) NLS of 48 that fished out the d18:1/nFA-Cer class, (c) PIS of 306 that fished out the d18:1/αhFA-Cer subclass, and (d) PIS of 308 that fished out the d18:0/ αhFA subclass. One advantage to analyze ceramide as [M+Li]+ ions is that there is no discrimination in the ion formation among the various ceramide classes, as the d18:1/nFA-Cer and d18:1/hFA-Cer are evenly presented in panel (a). Panels (b)–(d) cleanly pick up the entire Cer[NS], Cer[AS], and Cer [AdS] subclasses, respectively, and the profiles are similar to that of the ESI-MS (panel a), indicating the fidelity of applying these scans in the profiling ceramide species in a whole extract ä Fig. 2 (continued) collision energy, and the d18:1/nFA-Cer subclass is visualized in that obtained by (c) NLS of 256 with 45 eV collision energy; while MS2 spectra obtained by (d) NLS of 258 fished out the d18:0/nFA-Cer subclass, by (e) NLS of 327 fished out d18:1/hFA (Cer[AS]) subclass, and by (f) NLS of 329 fished out d18:0/ hFA (Cer[AdS]) subclass. Upon subjected to ESI in the negative-ion mode, the LCB/hFA-Cer species are more favorably formed than LCB/nFA-Cer [16], and thus the abundances of the ions of m/z 636, 650, 662, 664, 676, 690 (labeled in red) are much higher than the LCB/nFA-Cer species seen at m/z 564, 620, 646, and 648 in panel (a). However, analysis of ceramide as [M H] ions gives much more sensitivity than that detected as the [M+Li]+ and [M+H]+ (protonated species also suffer facile water loss) ions. In panel (b), both the d18:1/nFA-Cer and the d18:1/hFA-Cer subclasses are seen, due to that neutral loss of 256 is the common fragmentation process for d18:1/FA-Cer family [16]; however, at a higher collision energy (45 eV) (panel c), d18:1/hFA-Cer subclass underwent further degradation and can’t be detected by NLS of 256. Please note that ions of m/z 580 and 664 are present in panels (e) and (f), indicating that the former ion represents both a d18:1/αh18:0- and d18:0/αh18:1-Cer species, while the latter ion represents both a d18:1/αh24:0- and d18:0/αh24:1-Cer isomers. Using these approaches, isomeric structures can thus be identified. To achieve semi-quantitation, internal standard (1–2 species in each ceramide subclass) can be added

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Fig. 4 (a) The full scan ESI-MS spectrum of the whole porcine brain lipid extract in the negative-ion mode, and the linked-scan spectra obtained by (b) NLS of 256 that picked up d18:1/nFA-Cer, (c) NLS of 254 that fished out d18:2/nFA-Cer, (d) NLS of 258 that fished out d18:0/nFA as the [M H] ions, and (e) the NLS of 48 that picked up the entire ceramide family that consists of non-hydroxy FA substituent with all kinds of LCB (LCB/ nFA-Cer) as [M+Li]+ ions in the positive-ion mode. Panel (f) shows the negative-ion ESI-MS spectrum of a crude lipid extract from D. discoideum cells, and (g) the linked-scan spectrum obtained by NLS of 345 that fished out the Cer[AP] subclass. Please note that the ceramide species in the full scan spectra of the crude lipid extract (panels a and f) are nearly invisible. However, the linked-scan spectra (panels b–e and g) clearly fish out the specific ceramide subclasses in the extracts. Panel (e) contains all the corresponding ceramide species (as the [M+Li]+ ions) that are present in panels (b)–(d), and thus the positive/negative-ion approaches are complementary. The results show the power of the linked scan using a TSQ instrument to analyze even the minute species in complex mixtures. Please note: the same species detected as an [M+Li]+ is 8 Da heavier than that detected as an [M H] ion

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Fig. 5 (a) The negative-ion ESI-MS spectrum of the lipid extracted from dandruff flakes, and the linked-scan MS2 spectra obtained by (b) NLS of 256 that picked up Cer[NS], (c) NLS of 327 that fished out d18:1/hFA-Cer, (d) NLS of 355 that fished out d20:1/hFA-Cer in the Cer[AS] subclasses, (e) NLS loss of 345 that picked up t18:0/hFA-Cer, (f) NLS loss of 373 that picked up t20:0/hFA-Cer in the Cer[AP] subclass, (g) NLS loss of 343 that picked up t18:1/hFA-Cer, (h) NLS loss of 357 that picked up t19:1/hFA-Cer subclass, (i) NLS loss of 371 that picked up t20:1/hFA-Cer in the Cer[AH] subclass. Epidermal ceramides are among the most diversified, consisting of numerous isomers in over ten ceramide subclasses and the structures are difficult to define [17]. However, linked scans as described here can streamline the analysis, and hundreds of ceramide species in various specific subclasses can be revealed. For the ceramide species in the Cer [AP] family alone, an estimate of more than 120 molecules can be found, including species in which the LCB ranging from t16:0 to t22:0, and the fatty acyl chains ranging from C14 to C30 (C24 is the most prominent) with 0 to 1 double bond

ceramides as [M+H]+ and [M+Li]+ ions in the positive-ion mode, and as [M H] ions in the negative-ion mode are discussed. Examples that showed the application of the methods also include profiling various ceramide subclasses in the total lipid extract from dandruff flake (Fig. 4), bovine brain (Fig. 5a, b) and from model D. Discoideum cells (Fig. 5c, d).

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Acknowledgments This work was supported by NIH grants P30DK056341 and R24GM136766 to Center of Mass Spectrometry Resource of Washington University School of Medicine. References 1. Gennis RB (1989) Biomembranes—molecular structure and function. Springer-Verlag, New York 2. Mullen TD, Obeid LM (2012) Ceramide and apoptosis: exploring the enigmatic connections between sphingolipid metabolism and programmed cell death. Anti Cancer Agents Med Chem 12(4):340–363 3. Hannun YA, Luberto C (2000) Ceramide in the eukaryotic stress response. Trends Cell Biol 10(2):73–80. https://doi.org/10.1016/ S0962-8924(99)01694-3 4. Hannun YA, Luberto C, Argraves KM (2001) Enzymes of sphingolipid metabolism: from modular to integrative signaling. Biochemistry 40(16):4893–4903 5. Hannun YA, Obeid LM (2008) Principles of bioactive lipid signalling: lessons from sphingolipids. Nat Rev Mol Cell Biol 9(2):139–150 6. Kolesnick RN, Kro¨nke M (1998) Regulation of ceramide production and apoptosis. Annu Rev Physiol 60(1):643–665. https://doi.org/10. 1146/annurev.physiol.60.1.643 7. Chao MV (1995) Ceramide: a potential second messenger in the nervous system. Mol Cell Neurosci 6(2):91–96. https://doi.org/10. 1006/mcne.1995.1009 8. Garidel P, Fo¨lting B, Schaller I, Kerth A (2010) The microstructure of the stratum corneum lipid barrier: mid-infrared spectroscopic studies of hydrated ceramide:palmitic acid:cholesterol model systems. Biophys Chem 150 (1–3):144–156. https://doi.org/10.1016/j. bpc.2010.03.008 9. Imokawa G, Akasaki S, Hattori M, Yoshizuka N (1986) Selective recovery of deranged waterholding properties by stratum corneum lipids. J Invest Dermatol 87(6):758–761 10. Imokawa G, Akasaki S, Minematsu Y, Kawai M (1989) Importance of intercellular lipids in water-retention properties of the stratum corneum: induction and recovery study of surfactant dry skin. Arch Dermatol Res 281 (1):45–51

11. Mizutani Y, Mitsutake S, Tsuji K, Kihara A, Igarashi Y (2009) Ceramide biosynthesis in keratinocyte and its role in skin function. Biochimie 91(6):784–790. https://doi.org/10. 1016/j.biochi.2009.04.001 12. Yost RA, Enke CG (1979) Triple quadrupole mass spectrometry for direct mixture analysis and structure elucidation. Anal Chem 51 (12):1251–1264 13. Bligh EG, Dyer WJ (1959) A rapid method of total lipid extraction and purification. Can J Biochem Physiol 37(8):911–917 14. Hsu F-F (2016) Complete structural characterization of ceramides as [M H] ions by multiple-stage linear ion trap mass spectrometry. Biochimie 130:63–75. https://doi.org/ 10.1016/j.biochi.2016.07.012 15. Hsu FF, Turk J, Stewart ME, Downing DT (2002) Structural studies on ceramides as lithiated adducts by low energy collisionalactivated dissociation tandem mass spectrometry with electrospray ionization. J Am Soc Mass Spectrom 13(6):680–695 16. Hsu F-F, Turk J (2002) Characterization of ceramides by low energy collisional-activated dissociation tandem mass spectrometry with negative-ion electrospray ionization. J Am Soc Mass Spectrom 13(5):558–570. https://doi. org/10.1016/s1044-0305(02)00358-6 17. Lin MH, Hsu FF, Crumrine D, Meyer J, Elias PM, Miner JH (2019) Fatty acid transport protein 4 is required for incorporation of saturated ultralong-chain fatty acids into epidermal ceramides and monoacylglycerols. Sci Rep 9(1):019–49684 18. Lin M-H, Miner JH, Turk J, Hsu F-F (2017) Linear ion-trap MSn with high-resolution MS reveals structural diversity of 1-O-acylceramide family in mouse epidermis. J Lipid Res 58 (4):772–782. https://doi.org/10.1194/jlr. D071647 19. Hsu FF (2018) Mass spectrometry-based shotgun lipidomics – a critical review from the technical point of view. Anal Bioanal Chem 410(25):6387–6409

Multiple Linked Scans with TSQ for Ceramide Analysis 20. Gu M, Kerwin JL, Watts JD, Aebersold R (1997) Ceramide profiling of complex lipid mixtures by electrospray ionization mass spectrometry. Anal Biochem 244(2):347–356. https://doi.org/10.1006/abio.1996.9915 21. Liebisch G, Drobnik W, Reil M, Tru¨mbach B, Arnecke R, Olgemo¨ller B, Roscher A, Schmitz G (1999) Quantitative measurement of different ceramide species from crude cellular

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Chapter 10 Comprehensive Mouse Skin Ceramide Analysis on a Solid-Phase and TLC Separation with High-Resolution Mass Spectrometry Platform Meei-Hua Lin, Jeffrey H. Miner, and Fong-Fu Hsu Abstract Lipidomic analyses by mass spectrometry (MS) of epidermal ceramides, a large family of lipids crucial to the permeability barrier of the skin, have been reported previously. To ensure the accuracy of lipid identification, we describe here the isolation of mouse newborn epidermal lipids followed by fractionation with solidphase extraction columns, and lipidomic analyses by high-resolution MS for structural identification. We also describe here the employment of thin layer chromatography, an old but useful tool, in facilitating the structural characterization of the epidermal lipid species by MS. Key words Epidermal lipids, Protein-bound lipids, Mass spectrometry, Thin layer chromatography, Solid-phase extraction

1

Introduction Epidermal ceramides in the mammalian skin are the major class of lipids known to play diverse roles in the outermost layers of the skin function such as water retention and provision of a physical barrier that are crucial to the permeability barrier of the skin [1–4]. Ceramides also play important roles in the biological systems [5–8], and the subject matter of their biological roles is beyond the scope of this book chapter, interested readers are referred to the cited articles and reviews for details. Ceramides consist of a sphingoid long-chain base (LCB), including sphingosine, phytosphingosine, dihydrosphingosine (also known as sphinganine) and 6-hydroxy sphingosine with or without modification, to which a variety of fatty acyl chain is amidelinked to form a great number of complex and diversified structures in many subclasses [9]. Epidermal ceramides that are present in the form of lipid lamellae are extractable by organic solvents, while

Fong-Fu Hsu (ed.), Mass Spectrometry-Based Lipidomics: Methods and Protocols, Methods in Molecular Biology, vol. 2306, https://doi.org/10.1007/978-1-0716-1410-5_10, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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ceramides that are covalently bound to the cornified envelope in the epidermis are not extractable [10]. Mass spectrometry (MS) has played a major role in the structural elucidation of ceramides [11–16]. MS-based lipidomic analyses of epidermal lipids have also led to the realization of drastic changes in ceramide profiles in the diseased skin [17–19]. Through a comprehensive lipid analysis with high-resolution linear ion-trap (LIT) MSn, we recently reported the abnormality of several ceramide subclasses in mice deficient in fatty acid transport protein 4 (Fatp4
/
) [20]. Although HPLC/MS has become an emerging method for analysis of ceramides, several rare epidermal lipid species has been left unreported [16, 21]. In this chapter, we report the strategy implementing the conventional thin layer chromatography (TLC) and solid-phase extraction (SPE) separation, followed by highresolution LIT MSn toward a complete structural identification and quantitation of epidermal lipids, including several lipid species that have not been identified previously.

2

Materials Solvents are of HPLC or GC grade. All mobile phase solvent mixtures are indicated in v/v ratios. Glass vials and centrifugation tubes with screw caps contain a PTFE/TFE liner. All lipid standards are stored at
20  C and only glasswares are used.

2.1 Collection of Newborn Mouse Epidermis

1. 10 mM EDTA in PBS.

2.2 Extraction of Non-bound (Free, Extractable) Lipids

1. Internal standard mix 1 (1 mL): 0.08 mL of 2:1 chloroform: methanol, 0.2 mL of 1 mg/mL in 2:1 chloroform:methanol tritetradecanoyl glycerol (14:0/14:0/14:0-TAG) (Nu-Chek Prep, Elysian, MN, USA), 0.1 mL of 1 mg/mL in 2:1 chloroform:methanol 1-oleoyl-N-heptadecanoyl-D-erythro-sphingosine (18:1-d18:1/17:0-Cer[1-O-ENS]) (Avanti Polar Lipids, Alabaster, AL, USA), 0.3 mL of 1 mg/mL in chloroform tridecanoic acid (13:0-FA) (Matreya LLC, Pleasant Gap, PA, USA), 0.2 mL of 1 mg/mL in 2:1 chloroform:methanol Ndecanoyl-D-erythro-sphingosine (d18:1/10:0-Cer[NS]) (Matreya), 0.1 mL of 1 mg/mL in methanol N-alpha-hydroxydodecanoyl-D-erythro-sphingosine (d18:1/αh12:0-Cer [AS]) (Matreya), and 0.02 mL of 1 mg/mL in 2:1 chloro0 form:methanol D-glucosyl-β1-1 -N-dodecanoyl-D-erythrosphingosine (d18:1/12:0-GlcCer[NS]) (Avanti) (see Note 1).

2. Dry ice/ethanol bath. 3. Lyophilizer.

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2. Internal standard 2: 1 mg/mL in methanol monoheptadecanoin (17:0-MAG) (Nu-Chek). 3. Nitrogen evaporator (Organomation Associates, Inc., Berlin, MA, USA). 4. 2:1 (v/v) Chloroform:methanol. 2.3 Extraction of Protein-Bound Lipids

1. Internal standard mix 3 (1 mL): 0.7 mL of 2:1 chloroform: methanol, 0.1 mL of 1 mg/mL in chloroform tridecanoic acid (13:0-FA) (Matreya), 0.1 mL of 1 mg/mL in chloroform 17-hydroxyheptadecanoic acid (17:0-OHFA) (Matreya), and 0.1 mL of 1 mg/mL in methanol N-α-hydroxydodecanoyl-Derythro-sphingosine (d18:1/αh12:0-Cer[AS]) (Matreya). 2. 2:1, 1:1, and 1:2 Chloroform: methanol. 3. 50 mM NaOH in methanol. 4. 2 N HCl. 5. Nitrogen evaporator (Organomation Associates).

2.4

TLC Separation

1. Two TLC developing tanks (Fisher Scientific, Pittsburgh, PA, USA). 2. Flexible silica gel 60 plates with aluminum backing (Sigma, St. Louis, MO, USA). 3. Mobile phase solvent mixture 1: 76.5 mL of 40:10:1 chloroform:methanol:water. 4. Mobile phase solvent mixture 2: 80 mL of 95:4.5:0.5 chloroform:methanol:glacial acetic acid. 5. Whatman 3MM chromatography paper: cut into 23  19 cm in size. 6. Cholesterol (Sigma): 1 mg/mL in chloroform. 7. Heptacosanoic acid (27:0-FA) (Sigma): 0.5 mg/mL in chloroform. 8. Oleic acid (18:1-FA) (Nu-Chek Prep): 0.5 mg/mL in chloroform. 9. Mono-, di-, tri-acylglycerol (Sigma) standard mix 4: 0.5 mg/ mL monoolein, 2 mg/mL diolein, and 2 mg/mL triolein in chloroform. 10. Bovine ceramides (Matreya): 0.5 mg/mL in 2:1 chloroform: methanol. 11. Bovine sphingolipid (Matreya) standard mix 5: 0.5 mg/mL each galactosylceramides (cerebrosides), sulfatides, and sphingomyelin in 2:1 chloroform:methanol. 12. 1- to 10-μL glass micropipettes (Drummond Scientific Company, Broomall, PA, USA).

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13. Charring solution in a spray bottle: 3% copper acetate (w/v) in 8% phosphoric acid (v/v). 14. Baking oven. 2.5

Preparative TLC

1. 1:1 and 2:1 (v/v) Chloroform: methanol solution. 2. Razor blades.

2.6 Fractionation of Lipids by SPE Columns

1. Amino Chromabond Sep-Pak glass columns (3 mL/500 mg) (Macherey-Nagel, Duren, Germany). 2. Elution solvent mixture 1: 90:10 hexane:diethyl ether. 3. Elution solvent mixture 2: 75:25 hexane:ethyl acetate. 4. Elution solvent mixture 3: 15:1 chloroform:methanol. 5. Elution solvent mixture 4: 98:5 diisopropyl ether:acetic acid. 6. Elution solvent mixture 5: 9:1.4 acetone:methanol. 7. Elution solvent mixture 6: 2:1 chloroform:methanol. 8. Elution solvent mixture 7: 1:2 chloroform:methanol.

2.7 High-Resolution Mass Spectrometric Analysis

1. Thermo Scientific LTQ Orbitrap Velos mass spectrometer with Xcalibur operating system (Thermo Scientific, Waltham, MA, USA). 2. 1% NH4OH in methanol.

3

Methods All experiments are carried out at room temperature unless otherwise specified. Experiments that require organic solvents are performed in a hood.

3.1 Collection of Mouse Newborn Epidermis

1. Obtain newborn mice and, if necessary, assay for defects in permeability barrier of the skin (see Note 2). 2. Transfer a newborn to a 100-mm dish and sacrifice by decapitation. 3. Remove the limbs and tail. Save a small piece of tissue for genotyping. 4. Cut the skin along the length of the back and carefully peel off skin as a whole sheet using forceps. Rinse the skin in two changes of PBS. 5. Transfer the skin to a 35-mm dish. Spread the skin flat with the dermal side down. Add 1.5 mL of 10 mM EDTA in PBS underneath the skin, and make sure the skin floats on the surface of solution. Incubate at 37  C for 1–1.5 h.

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6. Use fine forceps, carefully peel off the epidermis as a whole sheet under a dissecting microscope. Rinse the epidermis in two changes of PBS. 7. Remove excess PBS with Kimwipe and put the epidermis into a 2-mL screw-capped tube. 8. Quick-freeze in a dry ice/ethanol bath, and store at
80  C prior to lyophilization. 9. Lyophilize the epidermis overnight with a lyophilizer. 10. Weigh samples (~8–12 mg) and store at
80  C. 3.2 Extraction of Non-bound (Free, Extractable) Lipids

1. Transfer lyophilized epidermis to an 8-mL centrifuge tube containing 0.8 mL of water, and soak for 5 min. 2. Add 1 mL of chloroform and 2 mL of methanol to make a 1:2:0.8 chloroform:methanol:water single-phase solvent mixture, and vortex at the maximal speed for 10 s. 3. To extract the non-bound lipids for quantification by MS, add 10μL each of the internal standard mix 1 and internal standard 2 (see Note 3). Do not add any standards if extracting lipids for analyses by TLC. 4. Vortex for 30 s followed by 2 h shaking. 5. Add 1 mL of chloroform and 1 mL of water to make a final 2:2:1.8 chloroform:methanol:water two-phase solution. 6. Vortex for 30 s, followed by 5 min centrifugation at 800  g. 7. Transfer the lower organic phase (~2 mL), and the epidermis to two separate new centrifuge tubes. 8. Add 1 mL of chloroform, 1 mL of methanol, and 0.9 mL of water to the organic layer. 9. Vortex for 30 s, followed by 5 min centrifugation at 800  g. This second extraction is set up to remove the residual proteins and other impurities. 10. Transfer the lower, organic phase containing the non-bound lipids to a 4-mL vial. 11. Blow to dry in a nitrogen evaporator, resuspend in 200μL of 2:1 chloroform:methanol, and store at
80  C.

3.3 Extraction of Protein-Bound Lipids

1. Add 2 mL of 2:1 chloroform:methanol to the tube containing the epidermis obtained in step 7 in Subheading 3.2, shake for 1 h, and discard the solvent. 2. Wash the epidermis sequentially with 2 mL of 1:1, 1:2, and 0:2 chloroform:methanol each for 1 h by shaking, and discard the washes (see Note 4). 3. Add 1 mL of 50 mM NaOH in methanol and 10μL of internal standard mix 3 for quantification by MS to the above tube with

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the washed epidermis. Do not add standards if extracting lipids for analyses by TLC. 4. Vortex at the maximal speed for 10 s, and incubate at 56  C for 2 h in an oven with 10 s mixing every 30 min. 5. Add 30μL of 2 N HCl and vortex to neutralize the reaction. 6. Add 2 mL of chloroform and 2 mL of water. 7. Vortex for 30 s, followed by 5 min centrifugation at 800  g. 8. Transfer the lower, organic phase to a 4-mLvial. 9. Add 2 mL of chloroform to the aqueous phase for second extraction, vortex, centrifuge, and remove the organic layer to combine with the first extract. 10. Dry the pooled extract under nitrogen, resuspend in 200μL of 2:1 chloroform:methanol, and store at
80  C. 3.4

TLC Separation

The Rf index is used for obtaining the tentative identification of lipid species, prior to structure confirmation by mass spectrometry (see Note 5). 1. Clean two TLC tanks with detergent and rinse thoroughly with tap water followed by distilled water. Place it in a hood, wipe it dry, wipe it with 70% ethanol and wait to dry. 2. Bake TLC plates in an oven at 100  C for 30 min to remove moisture to prevent them from blistering upon charring. Use the plates after cool or wrap with aluminum foil for later use (within a month). 3. Prepare mobile phase solvent mix 1 and 2 in two separate TLC tanks. Cover with lid and shake the tank ~30 times. 4. Lean a piece of the cut 23  19 cm chromatography paper against one wall of each of the tanks vertically, and close the lids (see Note 6). Wait until the tanks are equilibrated when the solvent front reaches the top of the paper (~1 h). 5. Use a pencil to mark the sample-loading spots on a TLC plate about 1.5–2 cm apart and 1 cm above the bottom of the plate. Also mark on both sides 7 cm above the bottom of the plate. 6. Load 50μL of extractable non-bound or protein-bound lipids onto the marked loading spots as a ~0.5 cm horizontal line with 10-μL glass micropipettes slowly to reduce the size of the loading spot. 7. Load lipid standards cholesterol, heptacosanoic acid, oleic acid, bovine ceramides, and standard mix 4 and 5 with amounts of 0.5, 1, 2, and 4μg (see Note 7) for preparation of standard curves for quantification. Lipid standards with different mobilities can be mixed and loaded.

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8. Place the plate into the tank equilibrated with solvent mixture 1 (see Note 8). Close the lid and let the solvent migrate until the solvent front rises to the 7-cm mark (~12 min), and remove and air-dry the plate for 5 min. 9. Place the same plate in the second tank equilibrated with solvent mixture 2 until the solvent front reaches the top of the plate (~1.5 h). Air-dry the plate. 10. Place the plate back to tank 2 and develop the plate in the same manner, followed by air-dry (see Note 9). 11. Spray the plate with charring solution to wet the plate evenly, air-dry and char the plate at 180  C for 15 min in an oven to visualize the lipid bands [22] (Fig. 1). 12. Scan the charred plate immediately. Save images in TIF files (see Note 10). 13. Dispose the used mobile phase solvents as hazardous waste (see Note 11). 3.5

Preparative TLC

Lipids in individual bands separated by TLC are recovered for structural characterization by MS (see Note 12). 1. To clean TLC plates, blank plates are overrun by 1:1 chloroform:methanol overnight in a tank. Air-dry for 5 min and bake at 100  C for 30 min to reactivate plates. Use the plate after cool or wrap in foil for later use (within a month). 2. Load samples (crude extract or column-fractionated lipids) with one additional spot as ancillary sample, and various lipid standards to the plate. 3. Proceed to steps 8–10 in Subheading 3.4 for TLC separation (see Note 13). 4. Air-dry the plate. Cut off the ancillary sample and lipid standard lanes and char as described in step 11 in Subheading 3.4. 5. Align the sample lanes (uncharred) with the charred lanes and mark lipid bands on the uncharred plate using a pencil (see Note 14). Scrape the individual lipid bands to separate weighing papers with a razor blade and transfer them to separate 8-mL centrifuge tubes. 6. Add 3 mL of 2:1 chloroform:methanol mix for a 2 cm2 gel scrape. 7. Vortex at the maximal speed for 30 s, followed by centrifugation at 800  g for 5 min. 8. Transfer each extract to a 4-mL vial. 9. Dry under nitrogen, resuspend in 100μL of chloroform or 2:1 chloroform:methanol mix depending on the polarity of lipids (see below), and store at
80  C until use.

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Fig. 1 Alterations in the repertoire of epidermal lipids in Fatp4
/
mice assayed by TLC. The extractable, non-bound (free) and protein-bound (bound) lipids from newborn mouse epidermis along with standards were co-resolved by TLC as described in Subheading 3. Compared to control mice (Fatp4+/
and Fatp4+/
; Tg (IVL-Fatp1), Fatp4
/
mice showed altered levels of NHFA and MAG, decreased amounts of Cer[EOS], Cer[BS], and GlcCer[EOS] subclasses, and increased amounts of Cer[1-O-ENS], Cer[NP], and Cer[AS] subclasses in extractable lipids. Fatp4
/
mice also showed decreased levels of OHFA and Cer[OS] in protein-bound lipids. These abnormalities were all rescued by transgenic expression of Fatp1 in the epidermis (Fatp4
/
; Tg (IVL-Fatp1). Lipid standards are shown on the left 5 lanes as indicated. The identities (by Rf value match with standards) of both bound and free lipids (script on the right with arrow sign) have been confirmed by LIT MSn. Abbreviations: AHFA (α-hydroxy FA), CE (cholesteryl ester), Cer (ceramides), GlcCer (glucosylceramides), GPL (glycerophospholipids), NHFA (non-hydroxy FA), OAHFA (O-acyl-ω-hydroxy FA), OHFA (ω-hydroxy FA), SM (sphingomyelin), and TAG (triacylglycerol)

10. Take a small amount to repeat the TLC separation. Char the plate and evaluate the recovery by comparison with the previously developed ancillary sample plate by visualization. 3.6 Fractionation of Lipids by SPE Columns

Sample preparation by SPE method is complementary to TLC (see Note 15). 1. Dry crude lipid samples (non-bound or protein-bound lipids) under nitrogen stream, and re-dissolve in 500μL of chloroform.

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2. Condition the glass barrel Chromabond NH2 Sep-Pak cartridges with 2  3 mL of hexane. 3. Load samples in chloroform to the columns. 4. To fractionate the extractable (non-bound) lipids, elute the column with the following solvent mixture by gravity and collect each eluate in 4-mL vials according to the previous protocol with modification [23]: (a) 3 mL of mixture 1 (fraction 1 containing cholesteryl ester and triacylglycerol). (b) 3 mL of mixture 2 (fraction 2 containing Cer[1-O-ENS] and cholesterol). (c) 3 mL of mixture 3 (fraction 3 containing Cer[EOS], Cer [NS], MAG, Cer[BS], and Cer[AS]). (d) 2  3 mL of mixture 4 (fractions 4 and 5 containing FAs). (e) 3 mL of mixture 5 (fraction 6 containing all GlcCers). (f) 3 mL of mixture 6 (fraction 7 containing polar lipids). (g) 3 mL of mixture 7 (fraction 8 containing polar lipids). 5. To fractionate protein-bound lipids, elute lipid species as above but stop at step 4(d). Cer[OS] is eluted in fraction 3, and fractions 4 and 5 contain ω-hydroxy FA. 6. Dry the eluates under nitrogen stream, resuspend fractions 1–5 in 200μL of chloroform, and fractions 6–8 in 200μL of 2:1 chloroform:methanol solvent, and store at
80  C. 7. Check the recovery of fractionation with 50μL of the eluates by TLC (Fig. 2) as described in Subheading 3.4., and in parallel, a crude sample is also applied as a control. 3.7 High-Resolution Mass Spectrometric Analysis

Perform on a Thermo Scientific LTQ Orbitrap Velos mass spectrometer with Xcalibur operating system. All ceramides are analyzed as [M
H]
ions in the negative-ion mode excepting Cer [1-O-ENS] and MAG, which should be analyzed as [M+H]+ and [M+NH4]+ ions, respectively. 1. Ion source settings. (a) ESI needle voltage: 4.0 kV. (b) Heated capillary temperature: 300  C. (c) Automatic gain control of the ion trap: 5  104. (d) Maximum injection time: 50 ms. (e) Buffer and collision gas: Helium at a pressure of 1  10
3 mbar (0.75 mTorr).

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Fig. 2 TLC analyses of the extractable lipids from newborn mouse epidermis fractionated by SPE columns (see Subheading 3.6 for SPE fractionation and steps 8–10 in Subheading 3.4 for TLC separation). The altered profile of the extractable lipids from Fatp4
/
mice, and the amelioration of the lipid profile in Fatp1 transgene-rescued Fatp4
/
mice (Fatp4
/
;Tg(IVL-Fatp1)) are evidenced in fractions 3–6 as shown. An unfractionated sample (first lane from right) was loaded as control. Lipid standards are presented on the left five lanes of the plate as indicated, and the identities of the epidermal lipids (script on the right with arrow sign) have been confirmed by LIT MSn. Fraction 3 also contained 17:0-MAG standard (marked with black arrow) which was added before extraction. For lipid abbreviations, please see captions in Fig. 1

2. Sample (SPE column-fractionated lipids or lipids recovered from TLC plates) injection. (a) Dilute lipid samples with 2–3 volumes of 1% NH4OH in methanol. (b) Loop-inject diluted samples into ESI source with a builtin syringe pump that delivers a 15μL/min flow of methanol with 1% NH4OH.

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(c) Wash the sample loop >2 times between injections of different samples to ensure no sample carryover from the previous samples. 3. Mass spectra acquisition. (a) Acquire high-resolution (R ¼ 100,000 at m/z 400) mass spectra. (b) Acquire MSn (n ¼ 2, and 3 and 4 if necessary) spectra for structural identification, with optimized relative collision energy (30–45%), activation q value (0.25), and activation time (10 ms) to leave a residual precursor ion abundance around 20%. Set the precursor ion selection window to 1 Da for CID for unit resolution detection in the ion trap or high-resolution accurate mass detection in the Orbitrap mass analyzer. Accumulate mass spectra in the profile mode, typically for 1–5 min for MSn spectra (n ¼ 2–4). 4. Data processing. (a) Process data with the Thermo Xcalibur software and perform offline recalibration with an internal ion of known m/z for high-resolution mass measurements. (b) Export measured mass list (four decimal), with relative intensity, theoretical m/z, mass deviation, and extracted elemental composition to an Excel file. (c) Sort out candidate lipid ions (full scan ESI-MS) within