Chemical Derivatization and Ultrahigh Resolution and Accurate Mass

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Chemical Derivatization and Ultrahigh Resolution and Accurate Mass Spectrometry Strategies for “Shotgun” Lipidome Analysis Eileen Ryan† and Gavin E. Reid*,†,‡,§ †

School of Chemistry, The University of Melbourne, Parkville, Victoria 3010, Australia Department of Biochemistry and Molecular Biology, The University of Melbourne, Parkville, Victoria 3010, Australia § Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria 3010, Australia ‡

CONSPECTUS: Lipids play critical structural and functional roles in the regulation of cellular homeostasis, and it is increasingly recognized that the disruption of lipid metabolism or signaling or both is associated with the onset and progression of certain metabolically linked diseases. As a result, the field of lipidomics has emerged to comprehensively identify and structurally characterize the diverse range of lipid species within a sample of interest and to quantitatively monitor their abundances under different physiological or pathological conditions. Mass spectrometry (MS) has become a critical enabling platform technology for lipidomic researchers. However, the presence of isobaric (i.e., same nominal mass) and isomeric (i.e., same exact mass) lipids within complex lipid extracts means that MS-based identification and quantification of individual lipid species remains a significant analytical challenge. Ultrahigh resolution and accurate mass spectrometry (UHRAMS) offers a convenient solution to the isobaric mass overlap problem, while a range of chromatographic separation, differential extraction, intrasource separation and selective ionization methods, or tandem mass spectrometry (MS/MS) strategies may be used to address some types of isomeric mass lipid overlaps. Alternatively, chemical derivatization strategies represent a more recent approach for the separation of lipids within complex mixtures, including for isomeric lipids. In this Account, we highlight the key components of a lipidomics workflow developed in our laboratory, whereby certain lipid classes or subclasses, namely, aminophospholipids and O-alk-1′-enyl (i.e., plasmalogen) ether-containing lipids, are shifted in mass following sequential functional group selective chemical derivatization reactions prior to “shotgun” nano-ESI-UHRAMS analysis, “targeted” MS/MS, and automated database searching. This combined derivatization and UHRAMS approach resolves both isobaric mass lipids and certain categories of isomeric mass lipids within crude lipid extracts, with no requirement for extensive sample handling prior to analysis, with additional potential for enhanced ionization efficiencies, improved molecular level structural characterization, and multiplexed relative quantification. When integrated with a monophasic method for the simultaneous global extraction of both highly polar and nonpolar lipids, this workflow has been shown to enable the sum composition level identification and relative quantification of 500−600 individual lipid species across four lipid categories and from 36 lipid classes and subclasses, in only 1−2 min data acquisition time and with minimal sample consumption. Thus, while some analytical challenges remain to be addressed, shotgun lipidomics workflows encompassing chemical derivatization strategies have particular promise for the analysis of samples with limited availability that require rapid and unbiased assessment of global lipid metabolism. biosynthetic origins.1 Lipids play diverse physiological roles in the maintenance of cellular function, including as structural and functional components of cellular membranes and membrane

1. INTRODUCTION Lipids are hydrophobic or amphipathic small molecules that may be grouped into eight main categories (fatty acids (FA), glycerolipids (GL), glycerophospholipids (GP), sphingolipids (SP), sterol lipids (ST), prenol lipids (PR), saccharolipids (SR), and polyketides (PK)) based on their chemical structures and © XXXX American Chemical Society

Received: January 19, 2016

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DOI: 10.1021/acs.accounts.6b00030 Acc. Chem. Res. XXXX, XXX, XXX−XXX

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Accounts of Chemical Research proteins, for energy storage, and as intra- and intercellular signaling molecules.2 It is not surprising therefore, that aberrant lipid metabolism has been associated with the pathophysiology of certain metabolically linked diseases including cancer, diabetes, obesity, neurodegeneration, and cardiovascular disease.3−6 The lipid complement of a cell, tissue, or bodily fluid is potentially comprised of thousands of individual molecular species, with a concentration range of at least 8 orders of magnitude. In an attempt to map entire cellular lipid compositions and monitor their changes in abundance under different physiological or pathological conditions, “lipidomics”, defined as “the large-scale study of pathways and networks of cellular lipids in biological systems”, has emerged as an independent discipline at the interface of lipid biology, chemistry, and technology.7−9 Ultimately, lipidomics can provide critical insights toward developing an improved understanding of lipid metabolism in the regulation of cellular homeostasis and for the identification of novel biomarkers6 or therapeutic targets of disease.10 MS coupled with electrospray ionization (ESI) or matrixassisted laser desorption ionization (MALDI) has emerged as a key enabling platform technology for lipidome analysis.8,9 ESIMS based lipidomics can be implemented in a variety of ways, including “shotgun” (i.e., direct infusion) or liquid chromatography (LC)-MS sample introduction,7,11 “targeted” or “nontargeted” data acquisition in “top-down” or “bottom-up” analysis modes, and using mass spectrometers with “low resolution” (i.e., unit mass), “high resolution and accurate mass (HRAM)” (>10 000 mass resolution and 100 000 mass resolution and 500 individual lipid species from four lipid categories (GL, GP, SP, and ST) and from 36 lipid classes and subclasses can be identified and quantified from plasma, tissue, or cell culture samples or their secreted exosomes.25,26,35 Important for high throughput, data acquisition times of only 1−2 min per sample are required, without extensive sample handling or chromatographic fractionation prior to analysis. Optionally, targeted MS/MS experiments can then be performed to confirm the initial sum composition level lipid assignments and for structural characterization or relative quantification at the “molecular lipid” level (albeit typically without sn-position assignment), thereby addressing type-3 isomeric mass overlaps in Figure 2B.25,26,35−37 2.1. Sample Preparation and Lipid Extraction

Biphasic chloroform−methanol/aqueous extraction has been the most widely used method to date for lipidomic sample preparation,38 although biphasic methods utilizing alternate D


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Accounts of Chemical Research

Figure 5. Functional group selective DMBNHS and iodine/methanol derivatization and targeted HCD-MS/MS, for enhanced structural confirmation and elucidation. Adapted from ref 26. Copyright 2015 Elsevier Inc. Panel A and its insets show the sum composition identities, abundances, and distributions of individual PE lipids from Figure 4A. Panel B shows the HCD-MS/MS spectrum for the PE(P‑34:1) + H+ lipid ion at m/z 991.5148.

thereby facilitating the assignment of numerous PE and PS lipids that otherwise would be too low in abundance to assign from the underivatized samples.25,26,35 Under optimized reaction conditions, the completeness of the DMNBHS derivatization reaction has been demonstrated to be >95%, by monitoring for disappearance of the MS precursor ion or MS/ MS product ion abundances from PE and PS internal standards.25 Similarly, the differentiation of plasmalogen- versus unsaturated O-alkyl ether-containing lipids (e.g., type-2 isomeric mass overlap described in Figure 2B) can be readily achieved via selective derivatization of the plasmalogen O-alk-1′-enyl double bond using iodine and methanol (shown in the workflow in Figure 3).26,36,37,42 Importantly, this reaction can be performed sequentially with the DMBNHS derivatization reaction, without additional sample handling or clean up between reaction steps prior to MS. Furthermore, both reactions are simple, can be batch processed in 96 well plates, are fast (30 and 5 min, respectively), and proceed to completion without nonspecific modification of other lipid classes.26,36,37 An example of the combined benefits of sequential aminophospholipid and plasmalogen lipid derivatization using 13 C1-DMBNHS and iodine/methanol, with UHRAMS analysis, is shown in Figure 4 for a crude lipid extract of LIM1215 cancer cell derived exosomes.26 Without derivatization, a low abundance ion observed at m/z 716.5590 (not labeled) could be assigned as any of the type-1 and type-2 isomeric mass even numbered carbon chain length ether-containing lipids, PC(O‑32:2) + H+ or PC(P‑32:1) + H+, or the odd numbered carbon chain length (or branched chain) ether-containing lipids, PE(O‑35:2) + H+ or PE(P‑35:1) + H+ (other potential type-1 isomeric mass phosphoric acid (PA) containing PA(O‑37:3) + NH4+ and PA(P‑37:2) + NH4+ lipid ions are typically not observed at any appreciable abundance in positive ion mode but can be clearly differentiated in negative ion mode).25,26 Although it is often assumed that odd numbered fatty acid

ions in negative mode are observed, with negligible contributions from [M + Na]+, [M + K]+, or [M + Cl]− ions.26,35 Notably, ammonium formate provides an approximately 2-fold increase in lipid ion abundances in positive ionization mode compared with using ammonium acetate.25 2.2. Sequential Aminophospholipid and Plasmalogen Lipid Derivatization To Resolve Isomeric Mass Lipid Overlap

As mentioned above, the presence of isomeric species means that the use of UHRAMS alone for top-down lipidomics results in significant ambiguity with respect to sum composition level lipid identifications.16,25 Chemical derivatization can eliminate this ambiguity, particularly for isomeric species within the first two types in Figure 2B. For example, functional group selective chemical derivatization of phosphoethanolamine (PE) and phosphoserine (PS) aminophospholipids using a “fixed charge” sulfonium ion containing 13C1−S,S′-dimethylthiobutanoyl-Nhydroxysuccinimide ester (13C1-DMBNHS) reagent (shown in Figure 3) can be used to resolve these lipid classes from overlapping type-1 isomeric mass species within crude cellular lipid extracts (Figures 4 and 5).25,26 Several other techniques for aminophospholipid derivatization have also been reported, for example, using fluorenylmethoxylcarbonyl (Fmoc) chloride with subsequent analysis in negative ionization mode ESI-MS,28 N-methylpiperazine acetic acid NHS ester 29 or 4(dimethylamino)benzoic acid NHS ester30 derivatization in positive ionization mode, or noncovalent complexation with 18crown-6-ether.31 Another benefit of aminophospholipid derivatization is an increase in ESI-MS detection sensitivity. For example, an average fold change increase of 6.1 ± 0.39 and 3.3 ± 0.37 has been reported for PE and PS lipids, respectively, following derivatization with DMBNHS.25 Similar or greater increases have also been reported using other aminophospholipid derivatization strategies.28,31,32 Notably, increased ion abundances result in an enhanced dynamic range for detection, E


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biological sample, using a 90 min RP capillary UPLC separation interfaced with a LTQ Orbitrap MS.20 Thus, a comparable number of lipids are identified using our approach compared with typical LC-MS methods (albeit at a lower lipid structural characterization level) but with the potential for significantly higher sample throughput (1−2 min vs typically >20 min) of shotgun infusion-MS analysis over LC-MS methods as a “first tier” analysis strategy for “discovery lipidomics”. Subsequent targeted MS/MS scans on selected lipids can then potentially then give more detailed molecular lipid information, particularly for very low abundance species, in a comparable acquisition time scale to LC-MS and with improved data quality due to the ability to perform spectral averaging on the infused samples, rather than from the transient chromatographic peak.

moieties are not observed in mammalian lipids and therefore to assign the m/z 716.5589 ions as the even chain PC ions, their observation as moderately abundant species and their potential role in health and disease has increasingly been reported.25,26,43 However, upon derivatization, each of these potential isomeric mass lipids are clearly resolved, with the PC(O‑32:2) + H+ lipid at m/z 716.5589, the iodine/methanol derivatized PC(P‑32:1) + H+ lipid at m/z 874.4820, and the DMBNHS and iodine/methanol derivatized PE(P‑35:1) + H+ lipid at m/z 1005.5306. The DMBNHS derivatized PE(O‑35:2) + H+ lipid (calcd m/z 847.6074) was not observed in this sample. From the data-rich UHRAMS spectrum in Figure 4A, the sum composition level assignment of 527 individual lipids from four lipid categories (GL, GP, SP, and ST) and 36 lipid classes and subclasses (Figure 4B) was achieved using the Lipid Mass Spectrum Analysis (LIMSA) software, 24 via automated searching against a user-defined database of hypothetical lipid compounds, with definition of peak finding, integration, and assignment parameters based on the experimentally observed mass resolving power and mass accuracy, correction of 13C isotope effects via an inbuilt linear fit algorithm, and relative quantification performed by normalization of the identified lipid ion peak areas to an internal standard.24,26 Analogous software tools for annotation of lipids at the sum composition level from UHRMS data have also been recently reported.15 Notably, information regarding the identities and abundances of the individual lipids within each class (Figure 5A) and the distribution of diacyl-, plasmalogen-, and alkyl ether-containing species within each lipid class and subclass (inset to Figure 5A) is directly obtained from the mass spectrum. Confirmation of these initial sum composition assignments was achieved by performing a series of targeted HCD-MS/MS experiments, with subsequent expert manual annotation of the resultant spectra based on the well characterized fragmentation behaviors previously reported for gas-phase lipid ions,17 along with further information regarding the long chain/short chain ratios, the ratios of unsaturated/saturated, monounsaturated (MUFA)/saturated, and polyunsaturated (PUFA)/saturated chain content (inset to Figure 5A) within each lipid class and subclass,26 and the identities of the acyl- or alkyl-chain compositions within individual lipids (Figure 5B). Although a lower level of lipid structural characterization is obtained at the sum composition level, the number of LIM1215 colon cancer cell derived exosome lipids assigned here is almost 2 fold greater (but with similar distributions of lipid classes and subclasses) than those previously reported from conventional direct infusion shotgun MS and MS/MS analysis methods,13,14 including a report of exosomes released by PC-3 prostate cancer cells where 280 molecular lipid species were assigned using multiple precursor ion scanning (MPIS) and neutral loss (NL) based methods.19 No direct comparison of this approach with LC-MS based methods on the same samples has been carried out to date. Note, however, that this is difficult to achieve given that sum composition level annotations are most often associated with shotgun methods,13,15,25,26 while molecular lipid level annotations are commonly reported from LCMS methods.6,11,20 However, a recent publication from Cajka and Fiehn, who performed a review of 185 original papers and application notes from LC-MS based comprehensive lipidomics, reported a number of annotated lipids at the molecular lipid level in the range of 32−722, with typical separation times of 20−90 min.11 For example, one report has described the identification of 370−446 lipid species, depending on the

2.3. Chemical Derivatization for Enhanced MS/MS-based Molecular Lipid Structural Characterization

In addition to the advantages described above (i.e., resolution of isomeric mass lipids and enhanced detection sensitivity), chemical derivatization can also yield improved lipid structural characterization and relative quantification by altering the MS/ MS gas-phase fragmentation behavior of the derivatized lipids toward the formation of more analytically useful product ions. Using CID- and HCD-MS/MS and multistage-MS/MS coupled with UHRAMS, we have previously characterized the fragmentation reactions of DMBNHS derivatized diacyl- and O-alkyl-ether-containing PE and PS lipids, sequential DMBNHS and iodine/methanol derivatized plasmalogen ether-containing PE and PS lipids, and iodine/methanol derivatized plasmalogen PC lipids versus their underivatized ions.36,37 For iodine/methanol derivatized plasmalogen PE and PC species, CID- or HCD-MS/MS directly provides information for molecular lipid structural characterization, via the formation of an abundant product ion corresponding to the characteristic neutral loss of an R1′CICH(OCH3) alkene from the derivatized O-alk-1′-enyl ether chain (this loss is preceded by initial facile loss of CH3SCH3 from the DMBNHS derivatized plasmalogen PE species).36,37 An example is shown in Figure 5B for the DMBNHS and iodine/methanol derivatized PE(P‑34:1) + H+ lipid ion from Figure 4A, that was found to contain a mixture of two type-4 isomeric mass PE(P‑16:0_18:1) and PE(P‑18:0_16:1) molecular lipid species. Note that information regarding sn-1 vs sn-2 regiochemistry of the O-alk-1′-enyl chain cannot be unambiguously obtained from the mass spectra. Therefore, an underscore is used for these lipid identifications instead of a slash, as proposed by Liebisch.23 It was previously demonstrated that the molecular lipid identity of underivatized plasmalogen PE lipids could be directly achieved by positive ionization mode CID-MS/MS via dominant loss of the O-alk1′-enyl chain as a neutral alkenylalcohol.17 In contrast, CIDMS/MS of underivatized protonated plasmalogen PC lipids yield a dominant phosphocholine headgroup specific product ion, with the molecular lipid determining product ion corresponding to loss of the O-alk-1′-enyl chain observed only at very low abundance.37 For DMBNHS derivatized diacyl- and O-alkyl ether-linked PE and PS lipids, the facile neutral loss of dimethylsulfide (CH3SCH3) is observed as the primary fragmentation pathway during MS/MS from both lipids classes. However, automated data dependent CID- or HCD-MS3 of this initial neutral loss product can be used to provide molecular lipid level F


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Figure 6. Isomeric mass d6-heavy/d6-light DMBNHS and iodine/methanol derivatization, UHRAMS, and HCD-MS/MS for multiplexed aminophospholipid relative quantification. Adapted from ref 37. Copyright 2015 Elsevier Inc. (A) Positive ionization mode UHRAM ESI-MS. (B) HCD-MS/MS of PE(P‑35:1) (m/z 1010.5662) from panel A.

abundance between samples using only a relative quantification strategy.26 This strategy involves normalization to an internal standard only to correct for technical variance between samples, with secondary normalization to protein content, cell number, or tissue weight to correct for biological variance. Alternatively, an emerging method for improving the accuracy of MS based relative lipid quantification involves isomeric mass stable isotope label-containing derivatization reagents (e.g., the iTRAQ reagents employed for multiplexed peptide quantification), with ratiometric measurement of the MS/MS abundances of isotopically encoded low m/z reporter ions from each sample.29,30,34 Using these methods, the technical variance associated with relative quantification is greatly reduced, because samples are simultaneously ionized and mass analyzed after separate derivatization and mixing. However, depending on the mixture complexity, these methods can suffer from interferences in reporter ion abundances due to the presence of coisolated isobaric or isomeric mass precursor ions, similar to that for complex peptide mixtures.48 To overcome these limitations, we recently described the “proof of concept” application of HCD-MS/MS, CID-MS/MS, and MS3 for simultaneous relative quantification of aminophospholipids from within different crude lipid extracts, using isomeric mass stable isotope labeled “d6-heavy” and “d6-light” variants of the DMBNHS reagent.37 Importantly, the problem of reporter ion overlap from coisolated precursors is reduced or eliminated, as relative quantitative information from isobaric mass ions is obtained at the sum composition level via ratiometric measurement of isotopically encoded high m/z neutral loss reporter ions formed by the loss of S(CD3)2 and S(CH3)2 under UHRAM MS/MS conditions, rather than low m/z products. Molecular lipid structural information and relative quantification is also directly obtained for isomeric

determining product ions for structural characterization of the acyl- and O-alkyl ether chain constituents.36,37 Finally, chemically aided approaches have also been reported to provide information at the structurally defined molecular lipid level, including ozone induced dissociation (OzID), radical directed dissociation (RDD), and the Paternò−Büchi reaction, each yielding characteristic MS/MS or MSn product ions indicative of carbon−carbon double bond positional isomers or the presence of alkyl chain branching.8,44,45 2.4. Chemical Derivatization for “Multiplexed” Sum Composition and Molecular Lipid Relative quantification

Due to the lack of commercially available isotopically labeled lipid standards that are chemically identical to each of the individual lipid species that may be present within a complex extract, accurate absolute quantification for the majority of lipids observed in modern lipidome analysis studies is typically not feasible. As an alternative, individual single lipids are often used as internal standards for all the lipids within a class or subclass to determine their absolute concentrations.11,46 However, the use of individual standards typically does not allow correction of differences in extraction, ionization, ion transfer, or MS/MS dissociation efficiencies that can result from different acyl or alkyl chain lengths, stereochemistry, or sites of unsaturation within endogenous lipids compared with the standard47 or due to concentration and matrix effects.46 Thus, the use of these methods should be considered as only semiquantitative and could result in over-reporting of absolute quantitative data in the literature, unless systematic appraisal of the MS and MS/MS response factors has been described. These challenges, and the steps required to resolve them, have been comprehensively discussed in the recent literature.11,46 For the results described above in Figures 4 and 5, therefore, we have taken a pragmatic approach and reported changes in G


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Accounts of Chemical Research mass plasmalogen aminophospholipids, by ratiometric measurement of pairs of isotopically encoded product ions formed via subsequent neutral loss of R1′CICH(OCH3) alkene moieties from the derivatized O-alk-1′-enyl ether chains within each lipid. Further structural characterization and secondary molecular lipid relative quantification of isomeric mass diacyland O-alkyl ether-containing aminophospholipids is readily performed via MS3 dissociation of the initial neutral loss reporter ions.37 For example, Figure 6 shows a 1:1 mixture (normalized to protein concentration) of crude lipid extracts from a metastatic colorectal cancer cell line, SW620 and an alkyglycerone phosphate synthase (AGPS) siRNA knockdown (i.e., the rate limiting enzyme involved in ether lipid biosynthesis), derivatized with d6-light and d6-heavy DMBNHS reagents, respectively.37 After database searching of the ESI-MS spectrum in panel A to assign lipid ions at the sum composition level, a series of “targeted” HCD-MS/MS spectra were then acquired for each of the identified PE and PS lipids. Panel B shows the resultant mass spectrum from the DMBNHS and iodine/ methanol derivatized plasmalogen lipid PE(P‑35:1) at m/z 1010.5662. From this spectrum, relative quantification and simultaneous structural characterization was achieved for four distinct isomeric mass plasmalogen molecular lipid species, PE(P‑19:0_16:1), PE(P‑18:0_17:1), PE(P‑17:0_18:1), and PE(P‑16:0_19:1).

Chemistry from the University of Melbourne, followed by postdoctoral research at Purdue University, and has previously held technical or academic research appointments at the Ludwig Institute for Cancer Research in Melbourne, Australia (1987−1997 and 2002− 2004) and at Michigan State University (2004−2014). Research in the Reid laboratory is broadly directed toward the development and application of bioanalytical mass spectrometry strategies for quantitative proteome and lipidome analysis, toward understanding the functional role of proteins and lipids in disease.

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ACKNOWLEDGMENTS Funding was provided from the National Institutes of Health (Grant GM103508) and the Australian Research Council (Grant DP130100535). We thank former members of the Reid lab, Dr. Cassie Fhaner, Dr. Todd Lydic, and Dr. Shuai Nie, who were largely responsible for initial development of the derivatization chemistries and lipidomics workflow described herein.

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3. FUTURE PROSPECTS With many of the analytical challenges associated with data acquisition now largely addressed, the volume of data that can be generated from a typical shotgun lipidome profiling experiment means that data processing is now the remaining bottleneck for comprehensive high throughput lipidomics research. Although multiple database search software and bioinformatics analysis tools have been reported,15,16,24 there is still a need for continued development of such tools to (i) accommodate derivatization chemistries, (ii) explicitly take into consideration the information provided from both MS and MS/MS modes when using these chemistries for lipid identification and quantification, and (iii) seamlessly integrate lipid identification and quantification information with their biological functions and with other “systems biology level” (e.g., genomic, transcriptomic, and proteomic) information in order to enable further significant advances in the field.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest. Biographies Eileen Ryan received B.Sc. and Ph.D. degrees in Nutritional Sciences from University College, Cork (UCC), Ireland, then worked as a Research Officer at the Centre for Drug Candidate Optimisation (CDCO), Monash Institute of Pharmaceutical Sciences, Monash University, Australia, with a focus on in vitro drug metabolism and MSbased identification and structural elucidation. Now at the University of Melbourne, Australia, her research interests include MS-based lipidomics, biomarker discovery, metabolic pathways, and nutritional biochemistry. Gavin E. Reid is the Professor of Bioanalytical Chemistry at the University of Melbourne, Australia. He received his Ph.D. in H


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Chemical Derivatization and Ultrahigh Resolution and Accurate Mass

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