Analytical Challenges and Recent Advances in Mass Spectrometry

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Analytical Challenges and Recent Advances in Mass Spectrometry Based Lipidomics Yepy Hardi Rustam, and Gavin Edmund Reid Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b04836 • Publication Date (Web): 22 Nov 2017 Downloaded from http://pubs.acs.org on November 23, 2017

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Analytical Chemistry

Analytical Challenges and Recent Advances in Mass Spectrometry Based Lipidomics

Yepy H. Rustam1 and Gavin E. Reid1,2,3*

1

Department of Biochemistry and Molecular Biology, University of Melbourne, Parkville, Victoria 3010, Australia 2 3

School of Chemistry, University of Melbourne, Parkville, Victoria 3010, Australia

Bio21 Molecular Science and Biotechnology Institute. University of Melbourne, Parkville, Victoria 3010, Australia

1 ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

INTRODUCTION

Although no internationally accepted consensus definition exists, lipids can be broadly defined as “fatty acids and their derivatives, and substances related biosynthetically or functionally to these compounds”1. Lipids play critical roles as (i) the major structural components of biological membranes, and as functional and regulatory components of membrane protein signaling, (ii) bioactive intra- and inter-cellular signaling molecules, and (iii) energy storage molecules for the maintenance of energy homeostasis2. With studies originating almost one century ago, significant insights continue to be made toward elucidating the functional role of lipid metabolism in the dynamic regulation of cellular homeostasis, and in determining the role of dysregulated lipid metabolism in the onset and progression of a range of human diseases, including diabetes, neurodegeneration, and cancer3–6. These diverse functions are highly dependent on the structures of the lipids, their concentrations, and their inter- and intra-cellular spatial and temporal distributions. Lipid structures may be divided into eight main categories: Fatty Acyls (FA), Glycerolipids (GL), Glycerophospholipids (GP), Sphingolipids (SP), Sterol Lipids (ST), Prenol Lipids (ST), Saccharolipids (SL), and Polyketides (PK)7–9, with further subclassifications into lipid class, subclass and sub-subclass, based on the general structural motifs or physiochemical properties (e.g. polarity, charge, shape, size and dynamics) of the molecular species within each category or class. The complexity, and molecular diversity of the structures of lipids within each category, class and subclass is apparent from an examination of the LIPID MAPS database, which serves as compendium of structures and annotations of biologically relevant lipids, that currently includes a total of 40,825 (20,512 curated and 20,313 computationally generated) unique lipid structures (October 19, 2017) along with experimental data and useful tools for their analysis8,9. Over the past three decades, technical advances in analytical method development have enabled large-scale studies aimed at determining the complete set of lipids that may be present within a cell or organism, along with quantification of their abundances, and analysis of their interactions with other lipids, proteins and metabolites. This has led to the field of ‘lipidomics’10, a term whose formal usage first appeared in the literature in 2001. Importantly, therefore, lipidomic analysis can facilitate not only a comprehensive understanding of the biochemical mechanisms underlying lipid metabolism and lipidassociated diseases4, but also lead to the discovery of lipid biomarkers for disease diagnosis, prognostic monitoring, and as targets for novel therapeutic interventions11–13. Figure 1 shows the increasing number of scientific publications relating to lipidomics, indexed in PubMed, between 2000 and 2017, indicating that while the field is still growing, it is now approaching maturity. Notably, although only 5.64% of all ‘lipid’, ‘lipidome’, ‘lipidomic’ or ‘lipidomics’ publications in 2017 also contained the term ‘mass spectrometry’ (MS), this has increased >1.8 fold since 2000. 2 ACS Paragon Plus Environment

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Furthermore, 47.0% of all 2017 ‘lipidome’, ‘lipidomic’ or lipidomics’ publications also contain the term ‘mass spectrometry’, and has remained relatively constant since 2003. In contrast, the number of publications involving the term Nuclear Magnetic Resonance (NMR) along with ‘lipidome’, ‘lipidomic’ or lipidomics’, are significantly lower (only 1.7% in 2017), while overall the use of ‘NMR’ with ‘lipid’, ‘lipidome’, ‘lipidomic’ or lipidomics has been steadily decreasing since 2014. This is most likely due to the generally lower sensitivity and higher complexity of NMR data for lipidomic analysis, despite having several potential advantages including non-destructive sample analysis, the ability to perform direct quantification with high analytical reproducibility, and to obtain information regarding molecular dynamics in complex lipid systems14. On the other hand, MS methods offer higher sensitivity, a broad range of ex vivo and in vivo applications due to the availability of different ionization methods, mass analyzer types, and compatibility with multiple solution- and/or gas-phase separation techniques, as well as simpler data interpretation10,15. By focusing on the word usage frequency in the PubMed-indexed literature titles from 2015-2015, retrieved using the search terms ‘mass spectrometry’ and ‘lipidome’, ‘lipidomic’ or ‘lipidomics’, an overview of the type and frequency of fundamental and applied analytical or biological research being explored using mass spectrometry based lipidomics can be obtained. Visualized using a ‘wordcloud’ representation in Figure 2, the continuing emphasis of analytical developments within the field is evident from high frequency usage of technical words such as ‘analysis’, ‘profiling’, ‘identification’, ‘characterization’, ‘quantitative’, ‘comprehensive’, ‘shotgun’, ‘chromatography’, ‘mobility’ and ‘imaging’, among others. However, multiple high frequency words are also observed that are indicative of an increasing focus on the applications of lipidomics in human biology, including ‘disease’, ‘patients’, ‘biomarkers’, ‘plasma’, ‘cancer’, ‘diabetes’, and ‘Alzheimer’s’, among others. Finally, the appearance of the words ‘metabolism’, ‘metabolomics’, ‘metabolomic’, as well as ‘protein’, ‘lipoprotein’ and ‘interaction’ may be indicative of a growing interest over the past few years in integrating lipidomics with other omics’ datasets (e.g., metabolomics and proteomics). Such ‘multi-omics’ analysis strategies promise to provide a more complete understanding of the complex and highly interactive biological processes that regulate phenotypic functions16,17.



Figure 1.

Summary of the number of lipidome analysis publications from 2000-2017 in PubMed (retrieved October 17, 2017). The search terms were (red) ‘mass spectrometry’ (MS) and ‘lipid’, ‘lipidome’, ‘lipidomic’ or ‘lipidomics’, where the % values represent the fraction of all ‘lipid’, ‘lipidome’, ‘lipidomic’ or ‘lipidomics’ publications, (light blue) ‘lipidome’, ‘lipidomic’ or ‘lipidomics’, (green) ‘mass spectrometry’ and ‘lipidome’, ‘lipidomic’ or ‘lipidomics’, (orange) ‘nuclear magnetic resonance’ (NMR) and ‘lipid’, ‘lipidome’, ‘lipidomic’ or ‘lipidomics’, and (dark blue) ‘nuclear magnetic resonance’ and ‘lipidome’, ‘lipidomic’ or ‘lipidomics’.


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Figure 2.

A ‘Wordcloud’ representation of the most frequently used words in the titles of 2015-2017 publications in PubMed (retrieved October 17, 2017) using the search terms ‘mass spectrometry’ and ‘lipidome’, ‘lipidomic’ or ‘lipidomics’. The word size varies with the frequency of usage. The highest frequency word was lipidomics (181). A minimum word frequency of 10 was required for inclusion.

OVERVIEW OF LIPIDOMIC ANALYSIS WORKFLOWS

An overview of the various analytical techniques currently employed in mass spectrometry based lipidomic analysis workflows are shown in Figure 3. The workflows associated with these techniques can be broadly divided into three major Experimental Design strategies, namely (1) ‘Separation’, (2) ‘Direct Introduction’ (i.e., ‘shotgun’), and (3) ‘Imaging’, based on the specific techniques that are employed, and the desired Research Outcomes. Experimental Design strategies (1) and (2) are largely directed towards ex vivo analysis of extracted lipid samples, thereby requiring the most extensive sample preparation, and 5 ACS Paragon Plus Environment


are typically used in conjunction with (1) on-line chromatographic fractionation- or (2) direct infusionelectrospray ionization (ESI)/nanoESI, or matrix assisted laser desorption ionization (MALDI), for sample introduction to the mass spectrometer. The types of downstream analysis using these experimental design strategies generally involve either ‘targeted’ identification and quantification of a predefined subset of lipids, or ‘untargeted’ profiling of the ‘global’ lipidome that may be present within a given sample15,18,19. On the other hand, experimental design strategy (3) is designed for in vivo analysis, to determine the spatial distributions of lipids within a sample (e.g., tissue sections) of interest20,21, and has the advantage of eliminating most, or all, requirements for sample preparation. Workflows associated with experimental design strategy (3) can also be applied to the acquisition of in-situ lipidomics data e.g., for real-time intraoperative tissue classification during surgical procedures22,23. The specific workflow that is developed for a given experimental design strategy typically involves multiple distinct steps, and a requirement for the selection of specific techniques within each step, starting with Sample Preparation, followed by Ionization, Mass Spectrometry based data acquisition, and finally Data Analysis, to obtain Research Outcomes that provide answers to the initiating biological questions. For example, Sample Preparation steps associated with experimental design strategies (1) and (2) can potentially include (i) ‘sub-lipidome isolation’ e.g., purification of lipoprotein particles or extracellular vesicles from plasma/serum/urine samples, or subcellular fractionation of the nucleus, mitochondria, etc. from tissue or cell samples)24–28, (ii) ‘internal standard addition’ for sample normalization and for downstream ‘relative’ or ‘absolute’ quantitation29, (iii) sample ‘homogenization’ of tissues or cell samples30, (iv) liquid- or solid-phase lipid ‘extraction’31–33, (v) ‘derivatization’ for enhanced ionization and/or tandem mass spectrometry (MS/MS) fragmentation to resolve isomeric mass lipid overlaps34, or to enable multiplexed quantitation35, and finally (vi) ‘matrix addition’ if downstream analysis uses MALDI36,37. Within each of these steps, it is common that an array of different options exist, so the analyst must choose the most appropriate option that is suited not only to the type of sample to be analyzed, but also to the overall goals of the research. For experimental design strategy (3), involving ex situ imaging applications22, appropriate choices for sample preparation regarding ‘section preparation’ (e.g., tissue sectioning and mounting), ‘internal standard addition’ (if quantitation is to be performed), and ‘matrix application’ (for downstream MALDI based analysis methods), must be made. However, in situ imaging applications within experimental design strategy (3) have the advantage of requiring no sample preparation at all. For Ionization, ESI and nano-ESI are most the common techniques associated with experimental designs (1) and (2), along with complementary methods such as atmospheric pressure chemical ionization (APCI) and atmospheric pressure photo-ionization (APPI), while MALDI is commonly used in both experimental design strategy (2) and (3). Experimental design strategy (3) also includes the use of 6 ACS Paragon Plus Environment

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secondary ion mass spectrometry (SIMS)38, as well as various ambient ionization techniques for in vivo lipid analysis including desorption electrospray ionization (DESI), liquid junction micro sampling (LJMS) (e.g., nano-DESI and liquid extraction surface analysis (LESA)) for ex-situ imaging39–41, while rapid evaporative ionization mass spectrometry (REIMS)22,23,42–44, and related techniques45,46, can be employed for in-situ lipid imaging applications. The Mass Spectrometry analysis step also includes a multitude of choices, depending on the performance characteristics of the mass analyzer (e.g., low-, high- or ultra-high- mass resolving power and mass accuracy) performance, and whether or not data acquisition will involve ‘MS only’ and/or MS/MS (or multistage MS/MS (i.e., MSn)), as well as the depth of structural annotation that is required for lipid identification and quantitation. Various approaches for targeted (e.g., neutral loss and precursor ion scans, or selective reaction monitoring (SRM) and multiple reaction monitoring (MRM))11,28,47,48 and untargeted (e.g., data-dependent acquisition (DDA) and data-independent acquisition (DIA))49–51 data acquisition strategies using collision-induced dissociation (CID)- or higher energy collision-induced dissociation (HCD)-MS/MS are also available, depending on the overall goals of the research. In addition to

the

conventional

CID

and

HCD

methods,

an

increasing

array

of

alternative

ion

activation/fragmentation techniques (e.g., Electron-Induced Dissociation (EID)52,53 and Electron Impact Excitation of Ions from Organics (EIEIO)54, Ozone-Induced Dissociation (OzID)55,56, the Paternò–Büchi reaction57, and Ultra-Violet Photo-Dissociation (UVPD)58,59, among others), have also been developed to provide a greater depth of information for precise molecular lipid structural characterization. Finally, several variants of ion mobility spectrometry (e.g., Field Asymmetric waveform Ion Mobility Spectrometry (FAIMS) performed in the electrospray interface60,61, or Drift-Tube Ion Mobility Spectrometry (DTIMS)62 and Traveling Wave Ion Mobility Spectrometry (TWIMS)63 performed in the vacuum system of the mass spectrometer), are available as additional ‘orthogonal’ techniques to chromatographic separation and mass spectrometry analysis, particularly for the resolution of isomeric lipid species. The options for Data Analysis are highly dependent on the availability of appropriate processing methods and software tools or structural libraries to identify lipid species at the annotation level provided from the experimental data acquisition method64–69, and for ‘relative’ or ‘absolute’ quantitation of their abundances29,65. Appropriate software is also needed to generate two- or three-dimensional images of lipid spatial distributions from MS imaging studies70,71 Finally, statistical and bioinformatic analysis strategies are required to derive biologically relevant conclusions from the data, to answer the initial research question64. Increasingly, this also requires integrative analysis of lipidomics datasets with those from their interacting biomolecules (e.g., genomics, transcriptomics, proteomics and metabolomics), not only as a



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means to more comprehensively characterize the overall phenotype of the system of interest, but also to elucidate the functional roles of lipids in complex biomolecular network interactions16,17,72–79.

Figure 3.

Analytical techniques and workflows for mass spectrometry-based lipidomic analysis. These workflows are comprised of four main components, namely (i) Experimental Design and Sample Preparation, (ii) Ionization, (iii) Mass Spectrometry, and (iv) Data Analysis. The analytical options and commonly used techniques within each component are listed in bold black text and normal black text, respectively. Legend: Gas Chromatography (GC), High Performance

Liquid

Chromatography

(HPLC),

(Ultra-High

Performance

Liquid

Chromatography (UHPLC), Two-Dimensional HPLC (2D HPLC), Supercritical Fluid Chromatography (SFC), Electrospray Ionization (ESI), Atmospheric Pressure Chemical Ionization (APCI), Atmospheric Pressure Photo-Ionization (APPI), Matrix Assisted Laser Desorption Ionization (MALDI), Secondary Ion Mass Spectrometry (SIMS), Desorption Electrospray Ionization (DESI), Rapid Evaporative Ionization Mass Spectrometry (REIMS), High Resolution Accurate Mass Spectrometry (HRAMS), Time of Flight (TOF), Ultrahigh


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Resolution Accurate Mass Spectrometry (UHRAMS), Fourier Transform-Ion Cyclotron Resonance (FT-ICR), Mass Spectrometry (MS), Tandem Mass Spectrometry (MS/MS), Multistage Tandem Mass Spectrometry (MSn), Selective Reaction Monitoring (SRM), Multiple Reaction Monitoring (MRM), Data-Dependent Acquisition (DDA), DataIndependent Acquisition (DIA), Collision-Induced Dissociation (CID), Higher energy Collision-induced Dissociation (HCD), Ozone-Induced Dissociation (OzID), ElectronInduced Dissociation (EID), Electron-Impact Excitation of Ions from Organics (EIEIO), Metastable Atom-activated Dissociation (MAD), Charge Transfer Dissociation (CTD), Ultra-Violet Photo-Dissociation (UVPD), Field Asymmetric waveform Ion Mobility Spectrometry (FAIMS), Drift-Tube Ion Mobility Spectrometry DTIMS, Traveling Wave Ion Mobility Spectrometry (TWIMS).

ANALYTICAL CHALLENGES FOR LIPIDOMICS

Two of the significant challenges associated with conventional lipidome analysis strategies, that act as barriers to further advancement within the field, can be defined in terms of their ability to (i) achieve complete analytical coverage, and precise structural characterization, of all the lipids that are present in a given sample, and to (ii) determine the accurate concentrations of each of these lipids, including definition of their spatial distributions within an organ, tissue, cell or sub-cellular region of interest. The first of these challenges arises from the extreme mixture complexity and structural diversity of lipids, and is in part, due to the inability of existing fractionation or separation methods (e.g., chromatography and ion mobility) to fully resolve the individual components of a given lipid extract, that may contain several thousands of individual molecular species, including numerous isobaric (same nominal mass) and isomeric (same exact mass) lipids. Ion suppression or dynamic range limitations during ionization and mass analysis exacerbate this problem, particularly for workflows within Experimental Design strategies (2) and (3) involving little or no separation prior to MS analysis. Furthermore, the relatively limited structural information that is provided from either ‘MS-only’ measurements (even when ultra-high mass resolving power is employed to resolve isobaric mass lipids), or by using conventional CID- or HCD-MS/MS based fragmentation methods and/or complementary chemical derivatization techniques34, typically restrict the level of annotation80 at which a specific lipid identification81 can be made, to only the ‘sum composition’ or ‘molecular lipid’ levels, respectively, rather than the ultimately desired (and more functionally relevant) ‘structurally defined molecular lipid’ level (Figure 4)34. For glycerophospholipids for example, a ‘structurally defined molecular lipid’ level annotation

requires

characterization

of

the

lipid

headgroup


(e.g.,

phosphocholine

(PC),


phosphoethanolamine (PE), phosphoserine (PS), phosphoglycerol (PG), phosphoinositol (PI), and phosphatidic acid (PA)), the carbon chain length and enantiomeric configuration of the individual acyl, alkyl- or O-alk-1-enyl-ether (i.e., plasmalogen) substituents attached to the individual sn-1 and sn-2 positions of the glycerol backbone, as well as the number, location and stereochemistry of any unsaturation’s (i.e., C=C double bonds), and the occurrence of modifications (e.g., branching, cyclization or oxidation) within these chains.

Figure 4.

(A) Annotation hierarchy for lipid identification and characterization based on the structural information derived from the analytical method, and (B) types of isomeric mass lipid overlaps that can hinder structural characterization at various of these annotation levels. 10 ACS Paragon Plus Environment

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Reproduced from Ryan, E.; Reid, G. E. Acc. Chem. Res. 2016, 49, 1596–1604 (ref 34). Copyright 2016 American Chemical Society.

The second challenge i.e., accurate quantitation of lipid abundances, results from the fact that individual species within a complex lipid extract can be present at concentrations ranging across six to eight orders of magnitude, and that mass spectrometry methods using ‘soft’ ionization methods including ESI and MALDI are not inherently quantitative i.e., the abundance of a lipid ion does not directly indicate its concentration. Experimentally observed ion abundances are influenced by a variety of experimental factors including (i) the sample preparation conditions, (ii) differential ionization efficiencies and/or ion suppression effects based on the physiochemical properties of the lipid class or individual lipid structures, as well as the total concentration of the mixture, and the polarity and identity of the ionic adduct, (iii) the overall ion transmission and MS/MS fragmentation efficiencies (that may be mass and structure dependent), and (iv) the detector response. Therefore, either external calibration, or the use of internal standards, are required for accurate quantitation. Due to inherent limitations associated with the correction of sample matrix effects when using external calibration methods, quantitation strategies using internal standards are generally preferred. However, the validity of results obtained using internal standard quantitation strategies are strongly dependent on the consideration, and control, of variances that may be introduced during each of the separate sample preparation, extraction, separation and MS and/or MS/MS analysis steps29. Additional specific challenges for quantitative analysis also exist for workflows in Experimental Design strategy (3) (i.e., Imaging), due to the difficulty of incorporating internal standards to the tissue sample in a way that yields an ionization response similar to that of the endogenous species, as well as the potential for heterogeneous extraction efficiencies across the tissue sample as a function of the tissue morphology, the uniformity of any matrix deposition method, and the identity of the solvents or matrices that are used36,37,82.

RECENT IMPROVEMENTS IN LIPIDOME ANALYSIS WORKFLOWS

Notable developments reported in the literature between 2015 and 2017, focusing on the analytical steps and techniques listed in the workflows shown in Figure 3 that collectively have been directed toward addressing each of the challenges outlined above, and mainly restricted to mammalian lipids and applications in cancer research, are described below.

Experimental Design



The Experimental Design strategy selected for a given application depends on the biological question, and the expected outcomes of the research. In general, Separation and Direct Introduction strategies provide better analytical coverage and quantitative outputs, but at the cost of information regarding cellular lipid spatial localization. In contrast, Imaging strategies preserve this spatial information but generally give lower analytical coverage, slower data acquisition rates, and can present difficulties in performing accurate quantitation37,82. In terms of analytical coverage, untargeted approaches are typically chosen when a ‘global’ lipid profile is desired, such as in studies aimed at biomarker discovery. Although the objective is generally to analyze as many lipids as possible across different lipid classes and sub-classes, in practice, these approaches typically cover only lipid species from 4 lipid categories, i.e., GL, ST, GP, and SP. On the other hand, targeted approaches are more appropriate to answer specific biological questions, or for use in biomarker validation and clinical analysis studies47,48. However, they normally cover a smaller fraction of the species that can be observed using untargeted methods, or focus on the selective, but relatively comprehensive, analysis of specific lipids from within a given class or subclass, e.g., fatty acids (including their oxidized bioactive lipid mediators)83 and sterol lipids84. The nature of the sample introduction in Direct Introduction strategies, in which the sample is directly ionized using ESI, nano-ESI or MALDI, without prior separation, maintains the uniformity of the sample and matrix (and relative ion suppression effects) throughout the entire MS data acquisition period, allowing for internal standard normalization across many samples if they have a relatively similar lipid profile29. This strategy is often preferred in high throughput ‘discovery’ studies due to shorter analysis times and lower sample consumption requirements. However, optimum sample conditions must be established to avoid non-linear ionization response or aggregation effects, that generally occur if the sample is too concentrated29. Furthermore, for studies requiring a deeper annotation level of lipid identification/characterization, these ‘shotgun’ lipidomics approaches can suffer from significant peak overlap due to the presence of isomeric and/or isobaric lipids, thereby complicating their analysis29. On the other hand, Separation strategies, mainly employing HPLC or UHPLC for separation of lipid species based on the properties of their polar headgroups (hydrophilic interaction LC (HILIC) and Normal-Phase (NP)), or hydrophobic acyl- or alkyl-chains (Reversed-Phase (RP)), significantly simplifies the sample matrix before sample introduction. This lower complexity can reduce aggregation effects, minimize isobaric and isomeric mass overlap, and provide a greater dynamic range of detection sensitivity, thus giving better identification and quantification29. However, due to differences in the matrix background as a function of the gradient elution conditions, quantitative limitations for occur based on any difference in retention time between the internal standard and the analyte29. Finally, Imaging strategies are increasingly being used due to the recognition that the determination of lipid spatial information can provide important 12 ACS Paragon Plus Environment

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functional insights into the relationship between tissue or cellular lipid heterogeneity and temporal dynamics, and the phenotypic or pathological condition of the sample36,39–42. This information is typically lost when performing extraction-based lipidomic analyses, due to the limitations associated with physically excising and maintaining the spatial fidelity of small sample areas36.

Sample Preparation Detailed reviews on sample preparation for lipidomics have recently been published elsewhere30,85,86. While most sample preparation techniques are now well established, it is important to emphasize the critical aspect of ensuring that that the lipidome profile of the sample is preserved throughout the processes. For example, lipids are prone to degradation (i.e., oxidation, peroxidation, and hydrolysis), but can be prevented using physical and chemical strategies. For example, samples should be immediately snap-frozen after sample collection to inhibit any enzymatic activity, and stored (as well as their extracts) at a very low temperature (-80oC)86. However, sample thawing can subsequently cause metabolite alterations. To address this in plasma samples, a fast thawing protocol based on ultra-sonication has been reported, resulting in the identification of more features and higher signal abundances87. The addition of antioxidants such as butylated hydroxytoluene (BHT) at low concentration (ranging from 0.1 – 0.01%) into all solvents used in the extraction processes, or inert gas bubbling (e.g., nitrogen) into lipid extracts prior to storage86 can also be used to minimize degradation caused by reactive oxygen species. A variety of sample enrichment techniques have been developed for the lipidomic analysis of subcellular particles such as lipoproteins, cellular compartments, or extra-cellular vesicles. For example, exosomes are a type of extra-cellular vesicle that have recently attracted great attention due to their possible roles in inter-cellular communication, and the pathogenesis of several diseases including cancer88,89. Conventional methods such as ultra-centrifugation, density gradient centrifugation, size exclusion, polymer-based precipitation, and immunoaffinity capture are commonly used to isolate exosomes, including for lipidome analysis27,28, but potentially suffer from limitations that can affect the results of post-isolation analyses, including loss of exosome integrity and co-isolation of non-exosome vesicles90. As an alternative, Flow Field-Flow Fractionation (FlFFF) is a relatively new size-based separation method that has been demonstrated for lipoprotein fractionation and exosome separation, with advantages including fast and robust separations while maintaining particle integrity24,25. Several applications of FlFFF for lipidomics have recently been demonstrated, including for the separation of high-density lipoproteins (HDL), low-density lipoproteins (LDL), and very low-density lipoproteins (VLDL) from plasma, with subsequent lipid extraction and analysis of oxidized phospholipids from



patients with Coronary Artery Disease (CAD)24, and for lipid profiling of exosome fractions of different sizes from prostate cancer patient samples25.

Internal Standard Addition for Quantitative Lipid Analysis

Wang et al. have comprehensively outlined the various factors that can affect the ‘relative’, ‘semiquantitative’, or ‘absolute’ quantification of lipids as a function of the experimental workflow that is used, and have defined a set of guidelines to be used for internal standard selection in each of these workflows29. Preferably, the internal standards consist of stable isotopologues, whose structures and physicochemical properties are representative of the endogenous lipid species of interest, and are added at the earliest possible step during sample preparation. Alternatively, provided that they are not also present as endogenous species in appreciable abundances, short acyl-chain or odd-numbered acyl-chain containing lipids may be used as internal standards. As a rule of thumb, provided that sample analysis is carried out in a low concentration regime to minimize global ionization suppressions effects91, normalization and relative quantitation in full scan MS-based workflows can be performed using one stable isotopologue internal standard per polar lipid class. However, two or more internal standards are required for relatively accurate (±10%) absolute quantification of individual species within a given lipid class or subclass (particularly for LC-MS based workflows), due to the need to determine ‘response factors’ that allow for correction of differences in (i) ionization efficiencies of non-polar lipid species as a function of their acyl or alkyl chain lengths and degree of unsaturation91–93, and (ii) mass dependent ion transmission and/or mass and structure dependent MS/MS fragmentation efficiencies. Also, the concentration of the internal standard used for a given lipid, or lipid class, sub-class or category, must fall within the linear dynamic range of the analyte(s), and so must be empirically determined for each sample in order for the method to be valid. For MALDI imaging workflows, a sample preparation method for more accurate lipid quantification has recently been reported by Jadoul et al., involving spiking of isotopologue lipid standards onto sample sections via a nebulization device, and that was controlled to take into account the biochemical heterogeneity of the tissue and provide detection similar to when using spiked tissue homogenates94. Perhaps the biggest remaining challenge associated with ‘global’ absolute quantitation of all the individual molecular lipid species that may be present in given sample, is the availability (and attendant high cost) of a sufficient number of individually synthesized stable isotope labeled lipid standards. To address this, Rampler et al.95 have recently reported a strategy for the production of complex mixtures of 13

C stable isotope labeled eukaryotic lipid standards (>99.5% enrichment), termed Lipidome Isotope

Labeling of Yeast (LILY). The proof of principle application of this mixture for the normalization and 14 ACS Paragon Plus Environment

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quantification of more than 200 lipids has been demonstrated using a parallel reaction monitoring based LC-MS approach, with a linear dynamic range of four orders of magnitude reported for relative quantification, and an absolute limit of quantification (LOQ) as low as 5 fmol determined for a small subset of glycerophospholipids, from 5 different classes.

Lipid Extraction

Multiple lipid extraction techniques, based on different chemical or physical principles, have been developed for either ‘global’ or ‘targeted’ lipidomic analysis, and have been the subject of many recent reviews30,85,86,96,97. The criteria for selection of a given extraction method must include consideration of the lipid category, class or subclass of interest, and evaluation of sample recovery, repeatability, and capability to remove interfering contaminants (e.g., proteins, salts, polymers). Compatibility with automation, and cost, for high throughput applications is also increasingly a factor to be considered. Biphasic liquid-liquid extraction (LLE) remains the most established and widely used technique, particularly the Bligh-Dyer, and Folch, methods that have been around for about six decades, but continue to undergo modification for specific applications. For example, adjustment of the organic and aqueous solvent ratios can increase the recovery of particular lipid classes30, while acidification can enhance the separation between organic and aqueous phases while increasing the recovery of acidic lipids98, as well as improving the recovery of the main lipid classes in dried blood spots99. Extraction methods replacing chloroform with other organic solvents are increasingly gaining in popularity due to their lower toxicity, and retention of lipids in the upper layer for simpler lipid recovery and resultant cleaner backgrounds. For example, in addition to the established MTBE-methanol (MeOH) method31, an alternate butanol-MeOH (BUME) method (3:1 v/v), compatible with automation using 96-well plate robotic systems, has been shown to provide linear lipid recovery from 10–100 µL of blood plasma, and absolute recoveries for commonly analyzed lipid classes that were similar or better to those obtained using the chloroform-based reference method32. A modification of this method, replacing acetic acid with lithium chloride, has also been proposed for the improved extraction/stabilization of acid-sensitive lipid species100. Cajka and Fiehn have reviewed the common solvents or solvent mixtures used for LLE-based metabolomic and lipidomic analysis, and highlighted the need to consider the polarity indices of these solvents as a critical factor that influences the efficient recovery of lipids within a given category or class97. Alshehry et al., have recently developed a monophasic liquid extraction method for plasma, using butanol:MeOH in a 1:1 (v/v) ratio, that has been shown to be directly compatible for large-scale LC ESIMS/MS-based lipidomic analyses, with no requirement for drying or reconstitution steps prior to injection101. Satomi et al. have also evaluated the utility of various other water-soluble organic solvents, 15 ACS Paragon Plus Environment


namely MeOH, ethanol and isopropanol, for monophasic lipid extraction, and concluded that ethanol was preferred for coverage of a wide range of lipid species102. Solid phase extraction (SPE) techniques based on RP, NP or ion-exchange (weak or strong cation and anion) interactions, and available in various cartridge, column or 96-well plate based formats, continue to be widely used in class-specific or ‘targeted’ lipid extraction approaches, due to their advantages for the enrichment of low abundant analyte species (e.g., eicosanoids and related compounds) in urine, serum and plasma patient samples103–105, and compatibility with high-throughput automation, shorter extraction times and lower solvent consumption compared to LLE85. Another benefit of SPE, to reduce sample preparation induced degradation/modification, has been reported by Lee et al., who developed an on-line lipid extraction method for urinary phospholipids utilizing HILIC and C4 particles in a short capillary extraction column prior to nanoflow LC-MS/MS106. While the number of identified phospholipids was somewhat lower than that observed using off-line LLE, a significant decrease (2-10 fold) in oxidized phospholipid species was observed when on-line vs. off-line extraction was employed.

Extraction for ‘Multi-omics’ applications

For studies involving ‘multi-omic’ data acquisition (e.g., genomics, transcriptomics, proteomics, lipidomics and metabolomics), integrated sample preparation techniques that are compatible with the simultaneous extraction of different types of biomolecule (e.g., DNA, RNA, proteins, metabolites and lipids) from within a single sample, are essential to reduce variability that may be introduced by using separate (i.e., possibly heterogeneous) samples, or individual specialized extraction techniques for each molecular class. To achieve this, Godzien et al. have described an LLE-based extraction method called IVDE (In-vial Dual Extraction) using MTBE/MeOH/ H2O (5:3:1.25, v/v/v), in which lipids were obtained from the MTBE upper phase, and proteins obtained as a pellet after solvent evaporation, for subsequent lipidomic and proteomic analysis of HDL and LDL fractions72. Coman et al. developed SIMPLEX (Simultaneous Metabolite, Protein, Lipid Extraction) as a more integrative extraction method for metabolite, lipid, and protein analysis73. The SIMPLEX method uses MTBE, MeOH, and H2O containing 0.1% of ammonium acetate in a ratio of 5:1.5:1.25 (v/v/v). The MTBE upper layer containing lipids is used for lipidomic analysis, while the lower aqueous layer carrying polar metabolites is used for metabolomic analysis, and finally, the protein pellet is dissolved for proteomic analysis. Notably, the application of the SIMPLEX method has been successfully demonstrated for the identification of 360 lipids, 75 metabolites and 3327 proteins, and quantitation of their changes in abundance, in a model system for adipogenesis- peroxisomal proliferator-activated receptor gamma (PPARG) signaling in mesenchymal stem cells, using only 106 cells. A similar extraction method using MTBE/MeOH/ H2O 16 ACS Paragon Plus Environment

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solvents, but with slightly different ratios, has also been applied to the mutli-omics analysis of polar and semi-polar metabolites, lipids, proteins, starch and cell wall polymers in Arabidopsis thaliana seed samples74. An alternative Folch-based extraction method called MPLEx (metabolite, protein, and lipid extraction) has also been reported for simultaneous metabolite, lipid, and protein extraction75, whereby lipids and metabolites are extracted from the lower organic phase and upper aqueous phases, respectively, while proteins are obtained from the interphase. This method was applied to investigate the differential abundances of 2,670 proteins, 236 lipids and 51 metabolite species in a human lung epithelial cell line infected with MERS Coronavirus.

Derivatization

There are four primary purposes of chemical derivatization in lipidomic analysis: (1) improved sensitivity via the incorporation of a highly efficient ionizable group to the lipid molecules, (2) the resolution of certain types of isomeric lipid species as a result of mass shifts induced via the derivatization process (e.g., types 1 and 2 in Figure 4B), (3) enhanced structural information from MS/MS due to alteration of the chemical structure of the lipid following derivatization, and (4) multiplex quantitation via the introduction of stable isotope-labels as part of the derivatization chemistry34. For example, Trimethylation Enhancement using

13

C‑Diazomethane (13C‑TrEnDi), reported by

Canez et al. (Figure 5) has been shown to simultaneously enhance lipid ionization, to mass-resolve some isobaric and isomeric lipids, and to alter MS/MS fragmentation behaviors107. Figure 5A shows the methylation derivatization reaction for amine and phosphate functional groups of PC and PE lipids, that yield chemically similar structures but containing different number of 13C isotope labels to enable their efficient mass separation of the otherwise type 1 isomeric odd-numbered carbon chain length PC lipids from even-numbered carbon chain length PE species (and vice versa). This charge derivatization was also found to improve the detection sensitivity of derivatized PC, PE, and PS glycerophospholipids (but not sphingomyelin lipids) from within a HeLa cell lipid extract, with a consequential increase in the number of species that could be identified, when using selective precursor ion scan mode MS/MS for their analysis (Figure 5B and C). Enhanced structural characterization of

13

C‑TrEnDi-modified PC, PE, PS

and SM cations has also been reported using negative ionization mode MS/MS, following charge inversion (i.e., charge switch) of the initial cationic lipids using gas-phase ion/ion reactions with dicarboxylate anions108. A one-step methylation strategy using trimethylsilyldiazomethane (TMSdiazomethane) has been used to differentiate bis(monoacylglycero)phosphate (BMP) from its isomeric phosphatidylglycerol (PG), that otherwise have identical masses and negative ionization mode MS/MS fragmentation patterns, and to quantify differences in abundance for individual BMP and PG lipid species 17 ACS Paragon Plus Environment


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in mice fed with a long-term high-fat diet109. This derivatization chemistry has also previously been demonstrated to provide enhanced sensitivity for the profiling of low abundant PI, PIP, and PIP2 phosphoinositide lipids110, as well as for relative quantitation of phosphoinositides111, and other PL classes112,113, using deuterated forms of the diazomethane reagent.

Figure 5.

Sensitivity and selectivity enhancement of glycerophospholipids by 13C-diazomethane (13CTrEnDi) derivatization and CID-MS/MS. (A) Schematic reaction of the

13

C-TrEnDi

derivatization and CID-MS/MS fragmentation of PC(16:0/18:1(9Z)) and PE(16:0/18:1(9Z)) lipids. (B) Positive ionization mode neutral loss (141 Da) MS/MS scan for unmodified PE lipids, and (C) Positive ionization mode neutral loss (202 Da) MS/MS scan for 13C-TrEnDimodified PE lipids. Reproduced from Canez, C. R.; Shields, S. W. J.; Bugno, M.; Wasslen, K. V.; Weinert, H. P.; Willmore, W. G.; Manthorpe, J. M.; Smith, J. C. Anal. Chem. 2016, 88, 6996–7004 (ref 107). Copyright 2016 American Chemical Society.

Another example of ‘charge switch’ derivatization is the use of the N-(4-amino-methylphenyl) pyridinium (AMPP) reagent for the introduction of a permanent charge on lipid species containing carboxylic acid functional groups, thereby providing enhanced ionization sensitivity and signal to noise (S/N) ratios, and more informative MS/MS fragmentation to enable the assignment of double bond 18 ACS Paragon Plus Environment

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positions through charge-remote fragmentation, in positive ionization mode. Example applications of this method include for the improved analysis of oxidized FA’s in inflammatory signaling processes114,115, and characterization of the methyl and hydroxyl branching sites in hydroxyphthioceranoic and phthioceranoic acid containing sulfolipids, following hydrolysis and derivatization of their polymethylated long chain fatty acids groups116. A similar strategy employed a pair of stable isotope probes, 2dimethylaminoethylamine (DMED) and d4-2-dimethylaminoethylamine (d4-DMED), to improve chromatographic retention behavior, and for the identification and differential quantitative analysis of various eicosanoid lipids in type 2 diabetes mellitus patients and myeloid leukemia patient serum samples117. Another derivatization strategy, previously developed for the sequential functional group selective modification of (i) PE and PS aminophospholipids using S,S′-dimethylthiobutanoylhydroxysuccinimide (DMBNHS), and (ii) O-alk-1-enyl (i.e., plasmalogen) ether-containing lipids using iodine/methanol, to resolve type 1 and 2 isomeric mass overlaps prior to nano-ESI-UHRAMS and -MS/MS analysis34, has recently been applied to the ‘sum composition’ level annotation of >500 individual lipids across 36 lipid classes and sub classes, as well as for monitoring their differences in relative abundance, between human LIM1215 colorectal cancer cell lines, and their secreted exosomes27. Isomeric mass stable isotope labeled variants of the DMBNHS reagent have also been developed for the multiplexed quantification of derivatized plasmalogen and/or aminophospholipid abundances, based on ratiometric measurement of pairs of characteristic isotopically encoded product ions formed during MS/MS35. The application of this strategy was demonstrated for monitoring changes in the abundances of specific lipids from a 1:1 lipid extract of a metastatic colorectal cancer-derived cell line, SW620, and an extract from the same cell line following siRNA knockdown of alkylglycerone phosphate synthase (AGPS) (i.e., the rate limiting enzyme in ether-lipid biosynthesis). A similar strategy, utilizing stable isotope derivatization, has also been reported for the relative quantification of PE lipids using D0- and D6-stable isotope containing acetone as the labelling reagent, followed by shotgun ESI-MS/MS detection using a neutral loss scanMS/MS data acquisition strategy on a triple quadrupole MS118. Various derivatization approaches have also been developed for the enhanced analysis of several classes of lipid species that are typically difficult to stably extract or detect using conventional lipidome analysis methods. Yang and co-workers have reported a workflow involving single-phase methyl tertbutyl ether extraction and low temperature diacetyl derivatization to prevent α-hydroxy migration within monoglyceride lipids during sample preparation, to facilitate unambiguous characterization of their regiospecific sn-positional isomers during CID-MS/MS119. Steroids are another ‘hard to detect’ lipid class due to their extensive structural diversity, lack of readily ionizable groups, and the presence of abundant dominating cholesterol. To address this issue, the Girard P (GP) reagent is commonly used to derivatize 19 ACS Paragon Plus Environment


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sterols and oxysterols, resulting in enhanced sensitivity, and more comprehensive global analysis using LC-MS/MS. Recent applications of this approach include the use of isotope-labeled GP reagents for the multiplexed analysis of free oxysterols and sterols present in plasma120, and a report by Roberg-Larsen and coworkers who quantified the abundances of Girard P derivatized 27-hydroxycholesterol (27-OHC) and other side chain-hydroxylated oxysterols in secreted exosomes and cytoplasmic fractions of estrogen receptor positive (ER+) and ER negative (ER-) breast cancer cells, and non-cancerous cell lines121. The observation of increased levels of 27-OHC in ER+ exosomes compared to the ER- and other control exosomes, but not in the cytoplasm of their cells of origin, demonstrates the important diagnostic potential of this sub-lipidome (i.e., exosome) analysis strategy. Finally, carnosine derivatization of 4hydroxynonenal containing lipids, that serve as indicators of oxidative stress, and that can play roles in signaling, inflammation, and immune response, was shown to enhance their ionization efficiency and improve quantification using shotgun ESI-MS in a biomarker discovery study of systemic lupus erythematosus122.

Chromatography

Although gas chromatography (GC)-MS continues to be used for the analysis of both free fatty acids and the fatty acid constituents of complex lipids formed by fatty acid methyl ester transesterification123,124, LC-MS is the primary chromatographic separation-based method of choice for the majority of complex lipid analysis strategies. Improvements in these methods continue to be made, particularly in terms of providing more efficient and faster separations, and with lower sample consumption, through the use of UHPLC and capillary-LC platforms, respectively.

High

Performance

Liquid

Chromatography

(HPLC)

and

Ultra-High

Performance

Liquid

Chromatography (UHPLC)

A wide range of stationary- and mobile-phase LC options are available for lipid separation. These include HILIC that separates lipid classes or sub-classes primarily according to the polarities of their headgroups125. On-line HILIC-MS has been recently applied in lipidomic analysis studies ranging from the fingerprinting of plant derived oils124, through to characterizing the structural diversity of ganglioside lipids in rat retina126, and for biomarker discovery in human cancer tissues and cell lines123,127. HILIC separations are increasingly being coupled on-line with HRAMS- and UHRAMS-analysis methods, due to the ability to resolve isobaric mass overlap among the co-eluting lipid species (e.g., the monoisotopic ion and M+2 isotope ion of lipid species differing by one unsaturation) within a class, and the availability 20 ACS Paragon Plus Environment

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of fast MS/MS and/or MSn fragmentation methods, and polarity switching capabilities, for use with datadependent or data-independent acquisition strategies126,128,129. NP-HPLC can also be used to separate lipids based on the polarity of their head groups, allowing for class-specific separations. For example, Abreu et al., proposed an optimized workflow coupling NP-HPLC with UHRAMS to analyze medium polarity lipids across 30 classes or subclasses in mammalian, vegetable as well as microorganism (yeast and bacteria) lipid samples130. The parallel use of NP-HPLC for non-polar lipid separation and HILIC for the separation of polar lipid classes, has also been reported to provide more comprehensive lipid coverage across a wider polarity range, from samples of human plasma and erythrocytes, and HDL, LDL and VLDL fractions131. RP-HPLC and -UHPLC, where separation is largely dependent on the hydrophobicity of the analyte, typically offers better separation efficiencies for comprehensive MS-based lipidomic analysis since lipid structural diversity significantly results from heterogeneity within their hydrophobic acyl- or alkyl-chain substituents, in addition to differences in backbone and headgroup structures (i.e. lipid category, class and subclass). In addition to high peak capacities, a particular advantage of RP-HPLC and -UHPLC separation methods for lipidome analysis is the predictable retention time behavior of individual lipid species, which may be used as an additional criterion for their confident identification132–134. For example, using a 15 cm RP-UHPLC column packed with sub-2µm C18 particles, and the analysis of over 400 identified lipid species covering 14 polar and nonpolar lipid classes from 5 lipid categories in total lipid extracts of human plasma, human urine and porcine brain, Ovčačíková et al. have demonstrated a general dependence on relative retention times for homologous lipid series differing by relative carbon number and double bond number, to a second degree polynomial fit132. Various RP-HPLC separation strategies have also been developed for targeted quantitative lipidomic analysis, using retention time-dependent SRM-, MRM-, PIS- and NLS-mode MS/MS data acquisition methods, including to improve on traditional risk factors for the prediction of cardiovascular events in type 2 diabetes patients48, as well as for targeted analysis of selected lipid classes such as bioactive oxidized fatty acid lipid mediators83,135, branched fatty acid esters of hydroxy fatty acids98, and sulfatides136. To improve detection sensitivity for comprehensive lipid screening in both positive and negative ionization mode UHPLC–MS, Cajka and Fiehn have evaluated the effect of different mobile-phase modifiers using standards from 16 lipid classes, and 164 lipids from 11 lipid classes in plasma, with optimal coverage obtained using 10 mM ammonium formate in positive ion mode and 10 mM ammonium acetate in negative ion mode137. Nanoflow (300 nL/min) UHPLC utilizing 75 µm or 100 µm internal diameter columns packed with 3 µm or 1.7 µm C-18 particles, and coupled with untargeted MS/MS and SRM-MS/MS data acquisition methods, has also been used to improve detection sensitivity to femtomolar levels for quantitative profiling of differences in the expression of 320 lipids in liver, lung, 21 ACS Paragon Plus Environment


and kidney tissue samples in p53 knockout (KO) mice138,139. Narváez-Rivas and Zhang have recenty evaluated the use of various stationary phase materials, including 2.6 µm C30 core-shell particles packed into a 150 mm × 2.1 mm I.D. column, for the separation of a relatively complex mixture of lipid standards under identical optimized gradient elution conditions, with retention time, retention time reproducibility, and MS height, width and peak area used for evaluation of the performance of each column. The C30 column was demonstrated to yield the narrowest peaks and highest theoretical plate number, with excellent peak capacity and retention time reproducibility (500 lipid species in human plasma samples obtained from malignant and benign breast tumor patients and healthy controls145, and to identify and validate a selected 22 ACS Paragon Plus Environment

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panel of plasma lipids for the diagnosis of lacunar infarction (stroke) patients from healthy controls lacunar infarction146.

Supercritical Fluid Chromatography (SFC)

SFC offers a ‘green’ alternative to HPLC by replacing most of the organic solvents with an inert gas (typically CO2) conditioned to exceed its critical point. Although not as widely used in lipidomic analysis studies compared to HPLC, and requiring the addition of mobile phase modifiers (e.g., methanolammonium acetate) to facilitate the efficient elution of many analytes, the potential of SFC-ESI-MS separation has been demonstrated by Takeda et al. for global profiling of 172 lipids across 4 lipid categories in extracts of plasma lipoprotein fractions (LDL and VLDL) obtained from normal and myocardial infarction-prone rabbits147. Another significant advance toward the more widespread adoption of SFC-MS based strategies for lipidomic analysis has been reported by Lísa and Holčapek148. In this report, ultrahigh-performance supercritical fluid chromatography (UHPSFC) using sub-2µm column particles and only a six-minute analysis time, was coupled with positive and negative ionization mode ESI-MS and -MS/MS for the detection of 436 lipid species from 24 lipid classes in a porcine brain lipid extract.

Ionization

Electrospray Ionization (ESI) and nano-ESI

ESI and nanoESI continue to be the primary techniques used in highly sensitive Separation- and Direct Introduction-based lipidomic analysis workflows. An example of the latter approach, to obtain lipidome information at the single cell level and to demonstrate the heterogeneity of lipidome compositions between single cells, can be found in a report from Phelps and Verbeck, who demonstrated the use of nanomanipulation-coupled nanoESI for in vitro extraction and subsequent MS analysis of individual adipocytes containing lipid droplets, from a single culture plate149. Based on the compositions of the observed TG lipid species, the mass spectra obtained from single cells corresponding to early and late development adipocytes containing small versus large lipid droplets, respectively, were easily distinguishable from each other. Unfortunately, ESI-based direct introduction workflows can suffer from drawbacks due to ion suppression effects, if the total sample concentration exceeds the linear dynamic range for ionization, or when inter-class lipid species are present across a wide dynamic range of abundances91,150,151. To address this problem, Southam et al. have demonstrated the benefits of a ‘spectral 23 ACS Paragon Plus Environment


stitching’ technique, whereby multiple spectra are acquired from each sample in a series of overlapping 50 m/z wide windows, that are subsequently 'stitched' together to create a complete mass spectrum. This has been shown to considerably increase dynamic range and detection sensitivity of up to fivefold for peak detection compared to MS data acquisitions using a single wide m/z range window152. APCI and APPI methods provide complementary information to ESI, particularly for on-line NP-HPLC-MS applications130 and are particularly suited to the analysis of non-polar lipid classes such as Cholesterols153 and TGs141. However, the utilization of APCI for multi-class lipid profiling has also been reported154, including by Beccaria and co-workers who reported a RP-UHPLC separation method suitable for coupling with both positive- and negative-ionization mode APCI and ESI, without need for modification of the chromatographic conditions155.

Matrix Assisted Laser Desorption Ionization (MALDI)

MALDI also continues to be employed for global lipid analysis studies in Direct Introduction workflows. Examples include the application of off-line HPLC and MALDI-TOF MS to quantitatively determine the lipid composition of ~150 lipid species in plasma and VLDL, LDL and HDL lipoprotein fractions from 10 healthy donors156, and the use of positive and negative ionization mode MALDI coupled with FT-ICR UHRAMS for the in situ profiling of 180 lipid species within a human mammary epithelial cell line, and 6 different breast cancer cell lines, grown on conductive indium tin oxide (ITO)coated glass slides, analyzed directly without prior lipid extraction or separation157. Partial least squares discriminant analysis (PLS-DA) PLS-DA of these 180 lipids resulted in separate clustering of each of the 6 different breast cancer cell lines, while only 8 lipids were required to differentiate the breast cancer cell lines from the non-malignant cell. MALDI, however, has perhaps found its most widespread use in mass spectrometry based Imaging applications, to monitor lipid distributions between discrete cell types or anatomical regions within tissue sections of interest. 2D spatial resolutions of 10 µm for individual lipids are now routinely achieved158, as reported by Anderson et al. who demonstrated the ability to distinguish individual fiber cell bundles from surrounding glial cells, and central blood vessels in human optic nerve tissue, based on the distributions of their unique lipid molecular signatures. Patterson et al. have evaluated the use of MALDI mass spectrometry lipid imaging, as an alternative or complementary tool to histopathological assessments, for determining pathological response to preoperative chemotherapy in tissue sections resected from patients with colorectal cancer liver metastasis. Based on the observed lipid signatures from this study, a unique response score was determined that correlated with prognosis, and single lipid species overexpressed in different histopathological features of the tumor were identified as potential new biomarkers for assessing 24 ACS Paragon Plus Environment

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the therapeutic response159. Others have used similar imaging methods to identify particular lipid species that allow classification of the differentiation state of colonocytes involved in human colon cell malignant transformation160. Ellis et al. has demonstrated the analytical benefits of dual polarity MALDI-MSI acquisitions from the same tissue section, together with multivariate analysis techniques that include both positive and negative ion data in the classification approach, as a means to increase lipidome coverage and provide more information regarding molecular composition and spatial distributions throughout biological tissues161. Ellis and co-workers have also developed a new approach, termed MALDI-2, whereby the initial plume of matrix and analyte molecules generated by a standard MALDI laser is further ionized by intersecting with a second laser beam, producing up to a 100 fold increase in sensitivity using a high resolving power orbitrap mass spectrometer, and enhanced lipidome coverage for MALDI-MS imaging in both positive and negative ionization modes, compared to traditional MALDI MS162. Although MALDI-MSI methods produce 2D images, three-dimensional imaging is also possible through reconstruction of the 2D images generated from serial cryosections of a three-dimensional object. For example, Patterson and coworkers have described 3D reconstruction methods using open-source software, that provide high-quality visualization and rapid interpretation through multivariate segmentation of the 3D IMS data, for the analysis of lipids from atherosclerotic plaques70. Similar techniques have also recently been utilized to compare 3D membrane phospholipid (PCs and PIs) distributions in wild type and mutants of Arabidopsis thaliana seeds163.

Secondary Ion Mass Spectrometry (SIMS)

Among the available ionization techniques for mass spectrometry lipid Imaging, SIMS provides the highest spatial resolution (< 1 µm), allowing for imaging at single cell or sub-cellular resolution164, and even for 3-dimensional analyses based on molecular depth profiling165. For example, Robinson et al. have reported the use of positive and negative ionization mode ToF-SIMS to monitor characteristic head group-specific, or fatty acyl chain length and saturation specific, SIMS product ions from intracellular lipid species within eight (4 ER-, PR-, and HER-, 3 ER+ and PR+, and 1 ER+, PR+, and HER2+) breast cancer cell lines38. PCA scores separation was observed between the individual triple negative and receptor positive cell lines, while distinctly different trends were found in the fatty acid and lipid compositions between the three groups of cell lines.

Ambient Ionization Methods



An array of ambient ionization methods based on liquid extraction, laser ablation, and thermal desorption principles166 are increasingly being applied for use in MS-Imaging lipidomic analysis. Although the spatial resolution associated with ambient ionization techniques is generally lower than with MALDI and SIMS, notable advantages include the ability to perform sample ionization in an open environment under atmospheric pressure conditions, with minimal sample preparation prior to analysis.

Spray-Based Liquid Extraction Desorption ESI (DESI)167, involving desorption of analytes from a sample surface by highly-charged droplets generated from an electrospray, typically operates with a spatial resolution range of 150-250 µm. However, the optimization of various geometric parameters of the spray probe has recently been shown to result in more robust imaging capabilities, to achieve to achieve pixel sizes as low as 20 µm168. Although accurate quantification remains challenging due to difficulties in appropriately introducing internal standards into the tissue matrix, a comparison of DESI-MS and LC-MS for lipidomic profiling of human esophageal adenocarcinoma tissue samples has shown a correlation coefficient of 0.70 (P < 0.001) for the relative abundances of GP lipid species when normalized against a common peak, indicating that the technique can provide comparable results for studies only requiring relative quantification. The applications of DESI, typically combined with multivariate statistical analysis methods, have been heavily focused on biomarker discovery for in vivo diagnosis of diseases, especially in cancers including human breast169, prostate170, lung171,172, brain173,174, ovarian175, thyroid26, and esophageal176, as well as in nonhuman cancers such as canine non-Hodgkin’s lymphoma177, and cell culture model systems (e.g., to monitor lipidome profile alteration in breast and retinoblastoma cancer cell lines during oncogene PLK1 knockdown)178. Importantly, cancer tissue discrimination using lipid profiles acquired by DESI-MSI can be achieved with accuracy rates (>95%) that are comparable to those from conventional immunohistochemistry methods. This capability, along with rapid data acquisition and analysis, highlight the great potential of DESI-MSI methods for cancer diagnosis, or for use as an in-vivo analysis tool to provide almost real time information for guiding surgical decision processes (e.g., for defining resection margins between cancer and normal tissues)22.

Direct Liquid Extraction

Several types of Direct Liquid Extraction or Liquid Junction Micro Sampling (LJMS) techniques179,180, involving continuous or discontinuous direct contact of an extraction solvent with a sample in a spatially defined area, with subsequent delivery into the MS inlet via a soft ionization probe, 26 ACS Paragon Plus Environment

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have been employed in lipidomic MS-Imaging applications. These include nanospray-DESI (nano-DESI), liquid extraction surface analysis (LESA) and pressurized LESA (PLESA). Nano-DESI, using a continuous extraction liquid flow delivered onto a sample surface confined between two capillaries and connected to a nano-ESI probe, operates with a typical spatial resolution of 100 to 150 µm. Lanekoff and co-workers have reported the use of nano-DESI for 3D-tissue imaging (by overlying 2D images of serial tissue sections) of fatty acids, lyso-PC and PC lipid distributions from mouse uterine cells at the sites of embryo implantation, in order to understand the molecular changes that occur in endometrial tissue during pregnancy41. Alternatively, LESA involves static solvent droplet extraction of analytes from a sample surface with subsequently infusion into the MS inlet using nano-ESI. Integrated with the commercially available Triversa Nanomate (Advion BioSciences, Ithaca, NY) nanoESI platform, and with an achievable spatial resolution of 1 mm, Hall et al. have recently utilized LESA in a biomarker discovery study aimed at the identification of lipid signatures associated with disease presence and severity in non-alcoholic fatty liver disease (NAFLD)40. Semiquantitative results obtained using an isotopically labeled internal standard mix incorporated into the extraction solvent, combined with multivariate statistical analysis, were able to differentiate healthy and NAFLD liver in mouse and human tissue samples, and could also differentiate between simple steatosis and more severe nonalcoholic steatohepatitis (NASH). Notably, the number and identity of lipid species detected using LESA-MS were comparable to those obtained by using LC-MS of tissue samples extracted using the Folch method, and provided comparable statistical classifications, whereas the number of different lipid species identified using MALDI-MSI was substantially lower, along with a lower statistical correlation. A high-resolution variant of LESA (HR-LESA) has also been recently developed, by utilizing a modified sampling probe (i.e., a 200 µm-ID fused silica capillary usually used in LC-MS fraction collection) to achieve a spatial resolution of 400 µm181. Alternatively, PLESA, developed by Almeida et al., uses a sealed and pressurized

pipet

probe

to

prevent

the

non-polar

extraction

solvent

(i.e.,

1:2:4

(v:v:v)

chloroform:methanol:2-propanol containing 0.75 mM ammonium formate, along with internal lipid standards for quantitation) from spreading across the tissue sections, thereby also providing a spatial resolution of 400 µm. The utility of this technique has been demonstrated for mouse brain lipidome imaging, and was shown to provide comparable results to those obtained when using conventional extraction and shotgun nanoESI-MS analysis methods182.

Laser Ablation

Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) developed by Muddiman and co-workers183,184, and laser ablation ESI (LAESI) developed by Vertes and coworkers185, 27 ACS Paragon Plus Environment


can both be used to ablate neutral lipid molecules from a tissue section into an electrospray plume for subsequent ionization and mass spectrometry analysis. These techniques offer the advantages of eliminating the need for organic matrix application or operation under vacuum conditions compared to MALDI, and with higher spatial resolutions (30-50 µm) compared to ambient ionization methods based on liquid extraction. IR-MALDESI has recently been demonstrated for imaging of silver cationized adducts of unsaturated lipids in mice forebrain tissue sections to examine the involvement of olefinic lipids in developmental deficiencies183, and for cholesterol imaging in hen ovarian tissue184, while LAESI coupled with ion mobility mass spectrometry has been demonstrated for lipid imaging (including structural isomers and conformers) in mouse brain and plant leaf tissue185.

In Situ Ionization

Perhaps the most exciting advances in ambient ionization methods in recent years have been the development and application of techniques capable of performing in situ lipidome profiling, in which specific molecular information is obtained directly from a spatially region of tissue in ‘real time,’ without removing the sample of interest from its biological origin. These techniques have a demonstrated potential for use as intraoperative clinical diagnostic tools, e.g., to define tumor resection margins during cancer surgeries, as an alternative to conventional histopathologic tissue classification methods that are typically slower and more labor intensive. The most well-established of these techniques is Rapid Evaporative Ionization Mass Spectrometry (REIMS) (also known as the iKnife), in which aerosols containing a variety of molecular species, including intact lipids, are generated by thermal ablation of ex situ or in situ tissue samples using an electrosurgical knife then directed using a flexible connector into an on-line ESI probe for subsequent MS and/or MS/MS analysis22,23. Albeit providing relatively poor spatial resolution (~2 mm), the differences in specific lipidome profiles obtained from ex situ or in situ analysis of cancer tissue samples using REIMS, versus those from adjacent normal tissue samples, have been used along with multivariate statistical analysis methods to develop classification models for the intraoperative identification and classification of various cancers, including breast43, colon42, and gastrointestinal44, with high sensitivity and specificity23. Importantly, the results using REIMS show good agreement with those obtained from ex vivo lipidome analysis using conventional extraction and/or other in vivo ionization techniques22,23. An example is shown in Figure 6, in which the lipid profiles obtained by ex situ REIMS of spatially defined regions of tissue samples from patients undergoing elective surgical resection for colorectal cancer (CRC), were used to distinguish malignant tissue from adenoma with an overall accuracy for detection of 94.4%, as well to reliably distinguish between normal adjacent mucosa (NAM) and cancer (AUC 0.96) and between 28 ACS Paragon Plus Environment

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NAM and adenoma (AUC 0.99)42. Notably, the lipidome profiles from REIMS were further able to be used to classify individual patient samples according to their established histological features of prognostic outcome (tumor differentiation, tumor budding, lymphovascular invasion, extramural vascular invasion and lymph node micrometastases, with AUC’s of 0.88, 0.87, 0.83, 0.81 and 0.81, respectively). SpiderMass, first described in 2016 by Fatou et al.46, is another MS-based approach designed for in situ real time guided surgical applications. In this technique, ionization is performed by resonant infrared laser ablation (RIR-LA) to excite the most intense vibrational band (O-H stretching mode) of endogenous water molecules adjacent to the biomolecular analytes of interest, including lipids. The proof of principle capability of SpiderMass has been demonstrated by ex situ lipid profiling of high grade serous ovarian carcinoma and normal tissue biopsies from an ovarian cancer patient, with significant differences observed in their resultant lipid profiles, as well as to perform minimally invasive in situ lipid profiling of human skin with minimal tissue damage or pain. Most recently, Zhang and coworkers45 have described a Direct Liquid Extraction technique for in situ rapid and nondestructive analysis of human cancer tissues. This biocompatible handheld device, named MasSpec Pen, enables controlled delivery of a small volume of water to a tissue surface for extraction of biomolecules, in a way that is roughly analogous to the nano-DESI and PLESA techniques described above. The mass spectra obtained from ex situ molecular analysis of >250 human patient tissue samples, including normal and cancerous tissues from breast, lung, thyroid, and ovary using this MasSpec Pen were shown to contain metabolites, fatty acids and complex lipids, including several that had previously been described as potential disease markers using other ambient ionization MS techniques. The observed molecular species were then used to develop statistical classifiers for cancer prediction. For example, 97.9% sensitivity, 95.7% specificity and 96.8% accuracy was determined for lung cancer, along with 93.8 and 92.2% accuracy for the prediction of squamous cell carcinoma and adenocarcinoma lung cancer histologic subtypes, respectively. The potential of this technique for use in in situ cancer diagnosis during surgery was also demonstrated, using a murine model of human breast cancer.



Figure 6.

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Intraoperative lipidome analysis by Rapid Evaporative Ionization Mass Spectrometry (REIMS). Shown are example mass spectra acquired by ex situ analysis of malignant (top), adenomatous (middle) and normal adjacent mucosa (NAM) (bottom) colon tissue samples. Several representative lipid species that were found to exhibit statistically significant differences in abundance between the different tissue types are shown in boxplots. Reproduced from Alexander, J.; Gildea, L.; Balog, J.; Speller, A.; McKenzie, J.; Muirhead, L.; Scott, A.; Kontovounisios, C.; Rasheed, S.; Teare, J.; Hoare, J.; Veselkov, K.; Goldin, R.; Tekkis, P.; Darzi, A.; Nicholson, J.; Kinross, J.; Takats, Z. Surg. Endosc. 2017, 31, 1361– 1370

(ref

42).

Copyright

2016

by

the

authors.

Available

from

https://link.springer.com/article/10.1007/s00464-016-5121-5 under a Creative Commons Attribution 4.0 (http://creativecommons.org/licenses/by/4.0/).

Mass Spectrometry

The depth of lipidome coverage, and the level of annotation (Figure 4) at which lipid identification, structural characterization and quantitation can be achieved when using full scan MS and/or MS/MS data acquisition methods, is defined by the mass resolving power and mass accuracy of the analyzer that is used, the structural information provided from the MS/MS experiment, and the extent to which a given complex mixture of isobaric and/or isomeric mass species are resolved by means of chromatographic fractionation, chemical derivatization, or ion mobility separation prior to, or during, the analysis process. 30 ACS Paragon Plus Environment

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MS-based lipidomic analysis can employ a range of mass analyzer types with different mass resolving power mass accuracy, and speed performance characteristics. Although no strict definition exists, mass analyzers with mass resolving power of up to 10,000 (e.g., quadrupole, ion trap, and low end TOF), are defined here as low-resolution instruments, while high-resolution TOF (and Q-TOF), and early generation Orbitraps, with mass resolving power of up to 100,000, are defined as HRAMS. Finally, current generation Orbitraps (up to 1,000,000 mass resolving power at m/z 200) and Fourier-transform ion cyclotron resonance (FT-ICR) (>1,000,000 mass resolving power depending on the acquisition method and acquisition rate), are defined here as UHRAMS. Importantly, UHRAMS can provide accurate mass measurements and the capacity to resolve isobaric mass lipid species186 (as well as some types of isomeric lipid species when combined with chemical derivatization)34 compared to low-resolution and HRAMS instruments, resulting in deeper lipidome coverage and higher confidence identifications for untargeted lipidome profiling, particularly when using Direct Introduction workflows for lipid annotations at the ‘sum composition level’. However, UHRAMS instruments typically operate with slower scan rates, that can limit their utility for mass spectrometry imaging applications.

MS/MS Data Acquisition Methods

Triple quadrupole MS-based analysis methods, using UHPLC-ESI-MS/MS with CID-SRM or MRM data acquisition strategies, continue to be most commonly employed for targeted lipid identification and quantitation studies, where the characteristic retention times of the lipids (determined using authentic standards), and their characteristic product ions formed during MS/MS, are used for assigning the identities, and concentrations of specific lipid species. For example, Alshehry et al. have described results from a targeted LC-ESI-MS/MS based lipidomic study of 3779 patient plasma samples, using a 1.8 µm C18, 50 × 2.1 mm RP-UHPLC column and dynamic/scheduled multiple reaction monitoring (dMRM) for the analysis of 310 lipid species in a total run time of only 10 minutes, to determine lipidomic profiles that improve on traditional risk factors for the prediction of cardiovascular events and death associated with type 2 diabetes mellitus48. Regression analysis was used to identify specific sphingolipid, phospholipid (including lyso- and ether- species), cholesteryl ester, and glycerolipid species that were associated with future cardiovascular events, including nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death during a 5-year follow-up period. Multivariable models combining traditional risk factors with 7 of these lipid species was found to significantly increase the ability to predict cardiovascular events, while the incorporation of only 4 lipid species into the base model was found to improve the ability to predict cardiovascular death in these patients. Skotland and coworkers have described a similar analysis strategy, using a hybrid triple quadrupole/linear ion trap mass 31 ACS Paragon Plus Environment


spectrometer, for the quantitative lipidomic analysis of urinary exosome derived lipids as potential noninvasive biomarkers of prostate cancer (PCa)28. In this study, GP, GL, ST and SM lipids were analyzed by direct introduction shotgun analysis in both positive and negative ionization modes using untargeted, but ‘selective’, multiple precursor ion- and neutral loss-scan mode MS/MS data acquisition methods, while neutral glycosphingolipids were analyzed using targeted MRM-MS/MS scans on the same mass spectrometer after on-line RP-UHPLC separation. In both cases, lipid identifications were assigned based on both parent mass and fragment ions, and retention time (where applicable), with quantitation based on normalization against class specific internal standards consisting of deuterium-labelled or heptadecanoylbased synthetic lipids (spiked in prior to lipid extraction) and further normalized to total protein. Biomarker candidates were then selected from lipid species that exhibited significant differences between urinary exosomes isolated from PCa patients and healthy controls, based on unpaired t-tests. Interestingly, the ratio of LacCer (d18:1/16:0) over PS 18:1/18:1 and of PS 18:0_18:2 over PS 18:1/18:1, was shown to provide high sensitivity and specificity (AUC of 0.989) to distinguish between patient and control groups. MS/MS analysis using data-independent acquisition (DIA) (e.g., SWATH-MS51, MS/MSALL187 or MSE50) or data-dependent acquisition (DDA)152,188,189 methods are commonly employed in untargeted lipidome profiling studies, and are generally coupled with HRAMS and UHRAMS detection. DIA methods typically require relatively long acquisition times and hence are most suited for Direct Introduction experimental designs, while MSE, that fragments all precursor ions simultaneously, is relatively fast but generally applicable only when coupled with on-line chromatography. For DDA methods, the number of identifications that can be made is generally limited by the inability to select for fragmentation all the precursor ions that are present during the defined data acquisition period (e.g., within the timeframe of a chromatographic peak elution) or where certain classes of highly abundant or highly ionizable lipid species suppress the signal from low abundance ions, thereby hindering their chance being selected for fragmentation. These issues can be overcome, at least in part, by using ‘gasphase fractionation’ similar to the ‘spectral stitching’ technique described by Southam et al.152, whereby DDA is performed using a series of narrow overlapping m/z ranges instead of a single broad m/z range188. The application of this strategy by Nazari and Muddiman was shown to result in an increase in sensitivity by more than an order of magnitude in both positive and negative ionization modes, and an increase in the number of identified lipids a factor of ~4 compared to a standard DDA method, from shotgun analysis of a crude lipid extract (modified Folch method) of healthy hen ovarian tissue188. Alternatively, automated iterative exclusion (IE) lists may be used in LC-MS/MS analysis workflows, whereby precursor ions selected in a DDA ‘topN’ experiment in a first injection are iteratively excluded in sequential injections such that unique precursors are fragmented until all ions above a user-defined intensity threshold are acquired189. 32 ACS Paragon Plus Environment

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Emerging

Ion

Activation/Fragmentation

Techniques

for

Enhanced

Lipid

Isomer

Structural

Characterization

One of the most challenging tasks for lipidomic analysis has been the characterization of isomeric lipid species at the ‘structurally defined molecular lipid’ level of annotation (Figure 4), including assignment of the sn-regiochemistry and enantiomeric configurations of their acyl- and/or alkyl-chains, and determining the precise locations and stereochemistry (i.e., cis/trans isomers) of double bonds within their unsaturated acyl- and/or alkyl-chains. Conventional ion activation/dissociation methods such as CID and HCD have a very limited ability to provide this information, thereby restricting the identifications reported from most lipidome profiling studies to the ‘molecular lipid’ level of annotation only. Fortunately, recent advances in analytical technologies for the improved gas-phase fragmentation of isomeric lipid species, either by altering the structure of the lipid ion such that it undergoes different fragmentation reactions, either spontaneously, or during subsequent collisional activation, or by using alternate means of ion activation/energy deposition to access novel fragmentation pathways, have opened new opportunities toward addressing this need. Many of these techniques have recently been reviewed by Hancock and co-workers190.

Ozone Induced Dissociation (OzID)

The OzID technique was first reported by Thomas et al. almost a decade ago for the determination of C=C double bond positions within unsaturated lipids, via gas-phase ion-molecule MS/MS reactions between ozone and the alkene moieties contained within mass-selected lipid precursor ions to yield characteristic pairs of ‘aldehyde’ and ‘Criegee’ product ions, differing in mass by 28 Da191. More recently, to improve the reaction efficiency of this technique for application in high throughput Direct Introduction workflows, Vu et al.56 have implemented OzID in the high-pressure Traveling Wave-based stacked ring ion guide region of a commercially available quadrupole ion-mobility time-of-flight mass spectrometer. Due to the increased number density of ozone in this system, and through modification of the height and velocity of the traveling wave, and the trap DC and entrance potentials used for ion transfer, an increase in OzID reaction efficiency of 1000-fold for assignment of C=C double bond positional isomers, and their cis/trans configurations192, of protonated PC lipid standards was achieved. Another report by the same group has described the application of this approach for the structural elucidation of double bond positions in the fatty acyl chains and/or long-chain bases of unsaturated glycosphingolipids193. Poad et al. have similarly reported the implementation of OzID on the same type of 33 ACS Paragon Plus Environment


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ion-mobility mass spectrometer, for applications within on-line UHPLC separation time scales55, as an extension of earlier work by Kozlowski et al. using a hybrid triple quadrupole linear ion-trap mass spectrometer194. Demonstration of the practical benefits of coupling UHPLC with OzID has been clearly shown, by the reduction in spectral complexity associated with unambiguous assignment of the double bond positions within various mixtures of PC18:1/18:1 and PC18:0/18:2, or PC 16:0/18:1 isomers, using reaction times of only 20 ms55. By

performing

sequential

CID-MS/MS

and

OzID-MS3

reactions

in

ion

trapping

instruments195,196,197, or by simultaneous CID/OzID-MS/MS in other mass spectrometry platforms55, diagnostic product ions that are characteristic of the sn-positions of acyl substitution on the glycerol backbone can also be observed, thereby allowing their unambiguous assignment, and allowing for near complete structural identification. An example is shown in Figure 7, where CID-MS/MS was first used to dissociate the mass selected sodiated precursor ions of three TG sn-positional isomers TG(16:0/18:0/18:1), TG(16:0/18:1/18:0) and TG(18:0/16:0/18:1), that vary only by the substitution pattern of the fatty acyl (FA) chains esterified to their glycerol backbones (Figure 7B). OzID-MS3 of the initial 18:0, 18:1, and 16:0 fatty acid neutral loss product ions from each of these spectra e.g., as shown in Figure 7C for the TG(18:0/16:0/18:1) lipid, results in characteristic fragmentation behavior that can be used to determine the identity of the fatty acyl attached to the sn-2 position195. The CID/OzID method has also shown to be compatible with DESI-based mass spectrometry Imaging workflows, as demonstrated by Kozlowski et al. for the analysis of isomers of PC 34:1 desorbed directly from a sheep brain tissue section sample198. Finally, although ozonolysis reactions may also be performed outside the mass spectrometer by passing the lipid analytes through a glass chamber filled with ozone gas following their separation by HPLC and prior to ionization and MS data acquisition199, the structural information provided using this approach can be ambiguous when multiple lipids are present, due to the lack of precursor ion mass selection.


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Figure 7.

Sequential collision- and ozone-induced dissociation to assign relative acyl chain positions in triacylglycerol lipids. (A) CID-MS/MS and OzID-MS3 product ion structures of a



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TG(18:0/16:0/18:1) lipid. (B) CID-MS/MS spectra for the TG(52:1) sn-positional isomers TG(16:0/18:0/18:1) (top), TG(16:0/18:1/18:0) (middle) and TG(18:0/16:0/18:1) (bottom). (C) OzID-MS3 spectra of the TG(18:0/16:0/18:1) 18:0 (top), 18:1 (middle), and 16:0 (bottom) fatty acid neutral loss product ions from panel B (bottom). Reproduced from Marshall, D. L.; Pham, H. T.; Bhujel, M.; Chin, J. S. R.; Yew, J. Y.; Mori, K.; Mitchell, T. W.; Blanksby, S. J. Anal. Chem. 2016, 88, 2685–2692 (ref

195

). Copyright 2016 American

Chemical Society.

Paternò–Büchi Reaction

The double bond positions in unsaturated lipid acyl chains can also be determined by performing CID-MS/MS on the oxetane ring-containing reaction products formed by [2+2] cycloaddition of C=C double bonds with acetone under 254-nm UV irradiation conditions (i.e., the Paternò-Büchi reaction) in the exposed borosilicate glass emitter tip region of a nano-ESI source57,200, or in a fused silica capillary solution transfer line of an ESI system201. CID-MS/MS of these photochemical reaction products produce specific pairs of diagnostic product ions, differing in mass by 26 Da, allowing identification of the original double bond positions. The utility of the technique has been demonstrated for the analysis of a variety of fatty acids and polar glycerophospholipids from rat brain tissue, for lipid extracts from normal and cancerous mouse breast tissues (Figure 8)57, as well as for cholesteryl esters (CEs) found in human plasma202. In addition to the diagnostic ions representing the double bond position, the neutral loss of 58 Da (acetone) is also observed in relatively high abundance upon performing CID-MS/MS of PaternòBüchi reaction tagged fatty acids. This feature has been exploited by Ma et al. to develop a neutral loss scan mode MS/MS experiment on a hybrid triple quadrupole linear ion-trap mass spectrometer, in order to quantitatively analyze differences in the unsaturated fatty acid compositions between normal and cancerous human prostate cells, with higher detection sensitivity than that observed from full scan MS data203. Most recently, Murphy et al. have also employed the Paternò-Büchi reaction, using a mixture of D0/D6-acetone, to facilitate the identification and characterization of C=C double bond positions in polyunsaturated fatty acids in negative ionization mode, including (after saponification) a 20:3 eicosatrienoic acid observed to accumulate in phospholipids of RAW 264.7 cells after 3 days in culture204.


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Figure 8.

Photochemical Paterno-Buchi (PB) reaction and CID-MS/MS for the characterization of double bond locations within unsaturated lipid isomers. (A) Schematic representation of double bond positional isomer diagnostic product ions formed from PB reaction and MS/MS of lipid isomers A and B. CID-MS/MS spectrum of PB-derivatized isomer mixtures of (B) 18:1 (∆9) and (∆11) fatty acids, and (C) PC16:1_18:1(∆9) and PC16:0_18:1(∆11) lipids, extracted from rat brain. Diagnostic ions are colour coded to indicate the individual isomers. (D-F) Relative % of ∆11 isomers from an 18:1 fatty acid, and from PC18:0_18:1 and PC18:1_18:1 lipids, between normal versus cancer mouse breast tissue. Reproduced with permission from Proceedings of the National Academy of Sciences USA Ma, X.; Chong, L.; Tian, R.; Shi, R.; Hu, T. Y.; Ouyang, Z.; Xia, Y. Proc. Natl. Acad. Sci. U. S. A. 2016, 113, 2573–2578. (ref 57).

Atmospheric Pressure Covalent Adduct Chemical Ionization (APCACI)



When acetonitrile is used as the HPLC mobile phase, a C3H5N+• adduct can be generated when lipids are ionized using APCI. These adducts can subsequently be mass selected and subjected to dissociation using CID-MS/MS and MS3 to determine the double bond positions within unsaturated lipid ions based on two types of fragments (alpha and omega ions) formed via cleavages of the C-C bonds vinylic to the original double bond. Originally described by Xu and Brenna in 2007205, APCACI has recently been applied by Háková et al.206 and Kalužíková et al.207 to characterize the detailed structures (including sn-2 linkage positions and double bond locations of unsaturated acyl chains) within a series of isomeric TG lipids from both lipid standards and complex mixtures (olive oil and vernix caseosa)206, as well as numerous low abundant cholesteryl esters of ω-(O-acyl)-hydroxy fatty acids from vernix caseosa (including isomeric 32:1 ω-hydroxy fatty acid species where the double bond was localized at the n-7 or n-9 positions)207.

Electron Induced Dissociation (EID) and Electron Impact Excitation of Ions from Organics (EIEIO)

EID-MS/MS employs high-energy electrons (>20eV) in an FT-ICR mass spectrometer to induce the gas-phase dissociation of intact singly-charged lipid precursor ions in the gas-phase, without requirement for modification of the structure of the lipid prior to analysis52,53. EID has been shown to result in extensive non-selective cleavage along the acyl chain backbone, and the formation of both evenand odd-electron product ions that allow the localization of double-bond positions, while the observation of a more favorable ketene neutral loss of the acyl chains attached to the sn-1 vs sn-2 position of the GP backbone (i.e., opposite to that found by CID- or HCD-MS/MS) could be used to assign the identity and specificity of the sn-linked acyl chains. Jones et al. first demonstrated this technique for the differentiation of isomeric PC18:1(9Z)/18:1(9Z) and PC18:1(6Z)/18:1(6Z) lipid species53, while Wong et al. have more recently demonstrated the use of sequential CID-MS/MS and EID-MS3 to differentiate isomeric ganglioside lipids, and to confirm the location of the double bond the ceramide moiety of these gangliosides52. Thus, combined with UHRAMS, EID promises to provide ‘near complete’ lipid structural information. However, due to the low reaction efficiency of the technique, which is compounded by the necessity for long irradiation times and low signal-to-noise ratios of many of the products due to the extensive non-selective nature of the fragmentation reactions, EID is best suited for application in Direct Introduction workflows. Campbell and Baba54 have also reported the near-complete structural characterization of lipids using EIEIO-MS/MS, via irradiation of mass selected singly charged lipid ions with 9−10 eV electrons within the electron-capture dissociation cell of a modified hybrid quadrupole time-of-flight mass spectrometer208. Similar to EID, EIEIO also provides more structural information compared to CID, allowing identification of lipid class, acyl chain identity and regioisomer positions, and 38 ACS Paragon Plus Environment

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the locations of C=C double bonds, as demonstrated for a series of GP lipid standards, and multiple PC species within an egg yolk lipid extract. Further demonstrations of EIEIO for the structural identification of triacylglycerol isomers209, sphingomyelin lipids210, and to distinguish cis and trans isomers in intact complex lipids211 have also been recently reported.

Metastable Atom-activated Dissociation (MAD) and Charge Transfer Dissociation (CTD)

The gas-phase interaction of mass selected singly charged phospholipid ions stored in a 3D ion trap, with metastable atoms (e.g., He) produced in a metastable atom source, has been reported by Deimler et al. to yield both singly and doubly charged radical fragments allowing determination of the head group and acyl chain identities, and including ions diagnostic of the presence and position of double bonds within their acyl chains, that are consistent with those formed from high-energy collision induced dissociation (HE-CID) or Penning ionization of the singly charged precursor ions212. Subsequently, CIDMS3 of the odd-electron molecular ions [M]+• produced by MAD-MS/MS by Li et. al. was shown to simplify interpretation of the spectra by selectively promoting radical-induced fragmentations over competing mechanisms/pathways and background chemical noise213. Li et al. have also recently demonstrated the dissociation of phospholipid cations by excitation and fragmentation using a beam of 6 keV Helium cations in a process termed charge transfer dissociation (CTD)214. The observed fragmentation using this technique is analogous to that of MAD and also EID/EIEIO, but also produces a series of doubly charged product ions with enhanced abundance at the double bond position, thereby facilitating their assignment.

Ultra-Violet Photo-Dissociation (UVPD)

Klein and Brodbelt have also recently demonstrated the utility of 193 nm UVPD-MS/MS for the detailed structural characterization of GP lipids215. Notably, in addition to many of the same product ions observed by CID-MS/MS, UVPD-MS/MS of protonated PC lipids in positive ionization mode was found to also result in selective cleavage of C-C bonds directly adjacent to C=C double bonds located within their acyl chains, providing a diagnostic pair of product ions differing in mass by 24 Da to enable localization of their double bond positions, including for lipid species contained within isomeric mixtures215. Ryan et al. have similarly described the implementation of positive-ionization mode 193 nm UVPD-MS/MS for the detailed structural characterization of various classes of lithiated or protonated sphingolipid ions, including sphingosine and sphingadiene, ceramide and dihydroceramide, ceramide-1phosphate, sphingomyelin, and several glycosphingolipids, and including endogenous lipids present 39 ACS Paragon Plus Environment


within a porcine brain total lipid extract59. For example, Figure 9 shows a reaction scheme and MS/MS spectra produced by UVPD or HCD of the [M+Li]+ precursor ion of sphingomyelin d18:1(4E)/18:1(9Z). In addition to the same product ions formed by HCD, UVPD-MS/MS also yields structurally diagnostic product ions resulting from cleavage of both the sphingosine and acyl chain C=C double bonds for direct localization of site(s) of unsaturation, as well as products corresponding to diagnostic cleavages of the sphingosine backbone and N-C amide bond linkages59.


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Figure 9.

193 nm Ultra-Violet Photo-Dissociation (UVPD)-MS/MS for the characterization of double bond locations within unsaturated lipids. (A) Schematic representation of the structure and UVPD induced fragmentation of the [M+Li]+ precursor ion of sphingomyelin d18:1(4E)/18:1(9Z). Diagnostic product ions allowing assignment of the sphingosine and acyl chain C=C double bonds are shown in red, while fragmentations of the N-C amide bond and sphingosine backbone are shown in green and blue, respectively. (B and C) 193 nm UVPD- and HCD-MS/MS spectra of sphingomyelin d18:1(4E)/18:1(9Z), respectively. Reproduced from Ryan, E., Nguyen, C. Q. N., Shiea, C., and Reid, G. E. Detailed Structural Characterization of Sphingolipids via 193 nm Ultraviolet Photodissociation and Ultra High Resolution Tandem Mass Spectrometry. J. Am. Soc. Mass Spectrom. 2017, 28, 1406–1419 (ref 59). Copyright American Society for Mass Spectrometry 2017, with permission of Springer.

Unfortunately, information to assign sn-positional isomers cannot be directly obtained by using UVPD-MS/MS alone. To overcome this limitation, Williams et al. have recently developed a hybrid CIDMS/MS and UVPD-MS3 analysis strategy, integrated within an on-line HPLC time frame-compatible workflow, that yields near-complete structural information for glycerophospholipids, including highly specific diagnostic product ions for assignment of the head group, and for determining the sn-positions and site-specific locations of double bonds within their acyl chains. This approach was demonstrated for the differentiation of sn-positional regioisomers of PC 16:0/18:1(n-9) and PC 18:1(n-9)/16:058. Finally, Morrison et al., have explored the utility of negative ionization mode 193 nm UVPD-MS/MS, in combination with CID, for the detailed structural characterization of lipid A molecules. In a recent study, doubly charged [M-2H]2- lipid A precursor ions in negative ionization mode were subjected to UVPD, yielding unique glycosidic and cross-ring fragments in addition to losses of the acyl chains, as well as a charge-reduced [M-2H]-• product ion formed by electron photodetachment, that was immediately subjected to further dissociation by CID without an intermediate ion isolation step. The utility of this strategy was demonstrated using an automated database-independent hierarchical decision-tree MS/MS data acquisition method, for the de novo characterization of 27 lipid A structure variants, including many that were isomeric, and including species containing hydroxy and branched fatty acyl chains, from within an enzymatically modified E. coli strain216. Thus, albeit not capable of differentiating cis/trans isomers, UVPD-MS/MS and -MS3 analysis strategies are quite promising for use in automated, high-throughput analysis workflows for comprehensive lipid structural characterization, due to having short activation and data acquisition timescales, and dissociation efficiencies that can be similar to those found in conventional CID- or HCD-MS/MS. 41 ACS Paragon Plus Environment


Ion Mobility

Ion mobility spectrometry (IMS) techniques coupled with MS analysis are increasingly gaining in popularity for lipidome analysis applications, due to their ability to be employed in any of the Experimental Design strategies defined in the introduction of this review (see Figure 3), and their capacity to provide information that is highly orthogonal to that of MS-based detection and chromatographic separation methods, particularly for isomeric lipids that have the same mass and often identical chromatographic retention properties, but appreciable differences in their three-dimension conformational structures217,218,219. Currently, three IMS-MS technologies have been applied for MS-based lipidomic analysis, namely FAIMS61, (also known as differential IMS (DIMS))60,218, DT-IMS62, and TW-IMS63. Griffiths, et al. have demonstrated the application of FAIMS-MS coupled with Direct Introduction using LESA, to provide improved signal-to noise ratios and reduced chemical noise for the observation of PE, PE and SM lipid species from dried blood spots on filter paper, that could not be detected by LESA-MS analysis in the absence of FAIMS220. Škrášková et al. similarly reported the enhanced capabilities of TWIMS separations coupled with MALDI- and DESI sample introduction, for the simplfied data interpretation of poly-sialylated ganglioside lipids in murine brain tissue, and their acetylated versions, in an Imaging mass spectrometry based workflow221. The utility of FAIMS has also been demonstrated for the resolution of isomeric lipid species that often do not separate chromatographically without using long analysis times, e.g., the silver-ion molecular adducts of pairs of isomeric TG sn-regioisomers extracted from porcine adipose tissue, using 1-butanol or 1- propanol as chemical modifiers to aid the separation60. Another FAIMS approach reported by Bowman et al., using a planar electrode geometry with high electrical fields and 65% He (v/v) in N2 as the buffer gas, has been shown to be capable of resolving four types (i.e., sn-linkage, chain length, double bond position, and cis/trans) of isomeric pairs of GL or GP lipids across three lipid classes, (i.e., DG, TG and PC), with an overall success rate of 75%61. Groessl el al.62 have demonstrated the applicability of DT-IMS-MS in Direct Introduction workflows for the analysis of isomeric phospholipid lipid standards, as well as isomeric lipid pairs found in bovine heart, porcine brain, yeast, egg yolk and E. coli, that differ only in the sn-positions of their acyl chains, or their double bond position or geometry. These authors also reported the baseline separation of several pairs of isobaric mass lipid species, whose small mass differences (0.03 Da) between the pairs would require a mass resolving power of >70 000 for their separation using MS alone. Using this platform, differences in Collision Cross Sections (CCS) of less than 1% were found to be sufficient for baseline separation. For DT-IMS and TW-IMS separations, Collision Cross Section (CCS) values determined for specific lipids have also been shown to be highly reproducible, such that they can be compared against experimental or 42 ACS Paragon Plus Environment

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computationally generated CCS reference libraries as an additional complementary physicochemical parameter to HPLC retention times, and the masses of the intact precursor and/or MS/MS product ions, for improved lipid identification confidence219,222,223,224. TW-IMS has been combined with a HILIC based Separation workflow and untargeted MS/MS data acquisition, in order to provide a third-dimension of separation for the observation of an increased number of individual lipid species within GL, GP and SP lipid classes, and to characterize alterations in the lipid profiles in a neuroblastoma cell line exposed to benzalkonium chlorides with different alkyl chain lengths225. The combination of RP-UHPLC-MS/MS with the orthogonal separation capability of TW-IMS has also been successfully applied in untargeted lipidome analysis strategies to identify relatively small variations in low abundant N-acyl PE lipids, against a background of hundreds of other lipid species in a mouse brain model of neuroinflammation222, and for the detection of a greater number of LPC, TG, and SM lipid species from plasma and lipoprotein samples in a pilot study of hypertriglyceridemic patients treated with extended-release nicotinic acid226. Kyle et al. have reported similar improvement in the quality and number of distinct lipids that could be identified from complex lipidomic samples (a mouse uterine tissue extract) when using DT-IMS227 as part of a three-dimensional separation strategy combining IMS with RP-UHPLC and MS/MS. Finally, a high resolution TW-IMS instrument has recently been developed, that applies traveling waves in a serpentine multi-pass Structures for Lossless Ion Manipulations (SLIM) platform228, and has been demonstrated to provide greatly enhanced resolution for isomeric lipid separations compared to conventional TW-IMS systems229. Examples of this increased separation capacity include the baseline separation of isomeric pairs of PC lipids differing in either their double bond locations or cis/trans stereochemistry (as shown in Figure 10), as well as isomeric mixtures of GD1a and GD1b ganglioside lipids, and the partial separation of isomeric pairs of galactosyl- and glucosyl-sphingosine and -ceramide lipids that differ only in the stereochemistry of their glycan moieties229.



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Figure 10. Ultra-high resolution ion mobility separation of lipid isomers in a serpentine multi-pass Structures for Lossless Ion Manipulations (SLIM) platform. (A) Structures of isomeric pairs of PC lipids differing in either double bond locations [PC(18:1(6Z)/18:1(6Z) and PC(18:1(9Z):18:1(9Z)],

or

cis/trans

stereochemistry

[PC(16:1(9Z):16:1(9Z)

and

PC(16:1(9E):16:1(9E)]. (B and C). Arrival time plots of the above isomeric lipid pairs using a single pass (15.9 m) or two passes (30.6 m), respectively. Reproduced from Wojcik, R.; Webb, I.; Deng, L.; Garimella, S.; Prost, S.; Ibrahim, Y.; Baker, E.; Smith, R. Int. J. Mol. Sci. 2017, 18, 183 (ref 229). Copyright 2017 by the authors. Available from http://www.mdpi.com/1422-0067/18/1/183 under a Creative Commons Attribution 4.0 (http://creativecommons.org/licenses/by/4.0/). 44 ACS Paragon Plus Environment

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CONCLUSIONS As the various analytical strategies outlined above continue to be individually refined and integrated, the goal of achieving truly comprehensive lipidome identification, complete structural characterization and accurate quantitation of all of the individual ‘structurally defined molecular lipid’ species that may be present within a biological sample of interest is now becoming feasible. This will undoubtedly increase our understanding of the underlying functional roles of lipids in biology, and also provide new pathways for the development of improved methods for real-time clinical diagnosis of the onset, progression and therapeutic treatment of disease. However, there does remain a critical need to evaluate the quality of the lipidome datasets that are obtained when using a particular analytical workflow, particularly for those that currently provide only partial structural information, and to define appropriate annotation and reporting standards for the dissemination of such datasets in the peer-reviewed literature. Liebisch et al.230 have recently highlighted the many factors that need to be considered in order to address these issues, while Koelmel et al.231 have discussed several of the common causes of improper (i.e., overstated) lipid annotations when using HRAMS- or UHRAMS-MS/MS techniques. Finally, Bowden et al.232 have recently described the results from an interlaboratory comparison exercise involving 31 laboratories, who used their own global or targeted workflows to collectively identify a total of 1527 unique lipids (at the sum composition level) from a commercially available reference material (i.e., Standard Reference Material (SRM) 1950 Metabolites in Frozen Human Plasma). Based on the lipids commonly identified by five or more of the participating laboratories, consensus estimates of the abundances and associated uncertainties for only 339 of these lipids have been established. Therefore, although these results represent an important advance, and immediately may be used by laboratories to assess whether their data agree with the lipidomics community, the need for continued analytical development and further maturation within the field is clearly apparent.

AUTHOR INFORMATION Corresponding Author *E-mail: [email protected] Notes The authors declare no competing financial interests.



Biographies Yepy H. Rustam is a PhD student within the Department of Biochemistry and Molecular Biology at the University of Melbourne. Under the supervision of Prof. Gavin E. Reid, his research is focused on the application of multi-omics analysis strategies, with lipidomics as the central approach, to investigate the functional significance of aberrant lipid metabolism in colorectal cancer malignancy and metastatic progression. He received an Honors degree in chemistry from the University of Indonesia in 2002, and a Master of Biotechnology degree from the University of Melbourne in 2015. He has also previously worked as a Research Assistant in the Biotechnology Research and Development Department of Charoen Pokphand, Indonesia, and as an Associate Researcher at the Biotechnology Research and Development Department of Wilmar Benih, Indonesia. Gavin E. Reid is the Professor of Bioanalytical Chemistry in the School of Chemistry and the Department of Biochemistry and Molecular Biology, at The University of Melbourne, Australia. Over the past 30 years, he has held a variety of technical research and academic appointments in Australia and the USA, including the Ludwig Institute for Cancer Research, in Melbourne (1987-1997 and 2002-2004) and Michigan State University (2004-2014). He received his PhD in Chemistry in 2000 from the University of Melbourne, then carried out post-doctoral research at Purdue University from 2000-2002. Research in the Reid laboratory is broadly directed toward the development of analytical biochemistry, mass spectrometry and associated chemical strategies for quantitative lipidome and proteome analysis, and their application toward understanding the functional role of lipids and proteins in disease, including colorectal cancer. Gavin is currently President of the Australian and New Zealand Society for Mass Spectrometry, an Associate Editor for the Journal of the American Society for Mass Spectrometry, and is on the Editorial Advisory Boards for the Journal of Lipid Research, the Journal of Mass Spectrometry, and the European Journal of Mass Spectrometry.

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Williams, L. St.; Surma, M. A.; Takeda, H.; Thakare, R.; Thompson, J. W.; Torta, F.; Triebl, A.; Trotzmuller, M.; Ubhayasekera, S. J. K.; Vuckovic, D.; Weir, J. M.; Welti, R.; Wenk, M. R.; Wheelock, C. E.; Yao, L.; Yuan, M.; Zhao, X. H.; Zhou, S. J. Lipid Res. 2017, DOI: 10.1194/jlr.M079012.



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Analytical Challenges and Recent Advances in Mass Spectrometry

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