Anal Bioanal Chem DOI 10.1007/s00216-015-8664-8

REVIEW

Applications of ion-mobility mass spectrometry for lipid analysis Giuseppe Paglia 1,2 & Michal Kliman 3 & Emmanuelle Claude 4 & Scott Geromanos 4 & Giuseppe Astarita 4,5

Received: 17 February 2015 / Revised: 19 March 2015 / Accepted: 26 March 2015 # Springer-Verlag Berlin Heidelberg 2015

Abstract The high chemical complexity of the lipidome is one of the major challenges in lipidomics research. Ionmobility spectrometry (IMS), a gas-phase electrophoretic technique, makes possible the separation of ions in the gas phase according to their charge, shape, and size. IMS can be combined with mass spectrometry (MS), adding three major benefits to traditional lipidomic approaches. First, IMS–MS allows the determination of the collision cross section (CCS), a physicochemical measure related to the conformational structure of lipid ions. The CCS is used to improve the confidence of lipid identification. Second, IMS–MS provides a new set of hybrid fragmentation experiments. These experiments, which combine collision-induced dissociation with ionmobility separation, improve the specificity of MS/MSbased approaches. Third, IMS–MS improves the peak capacity and signal-to-noise ratio of traditional analytical approaches. In doing so, it allows the separation of complex lipid extracts from interfering isobaric species. Developing in parallel with advances in instrumentation, informatics solutions Published in the topical collection Lipidomics with guest editor Michal Holčapek. * Giuseppe Astarita [email protected] 1

Istituto Zooprofilattico Sperimentale della Puglia e Della Basilicata, Via Manfredonia 20, 71121 Foggia, Italy

2

Center for Systems Biology, University of Iceland, Sturlugata 8, 101 Reykjavik, Iceland

3

Translational and Bioanalytical Sciences, Non-Clinical Development, Allergan Inc., Irvine, CA 92612, USA

4

Waters Corporation, Health Sciences, 34 Maple Street, Milford, MA 01757, USA

5

Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, 3900 Reservoir Road, N.W., Washington, DC 20057, USA

enable analysts to process and exploit IMS–MS data for qualitative and quantitative applications. Here we review the current approaches for lipidomics research based on IMS–MS, including liquid chromatography–MS and direct-MS analyses of Bshotgun^ lipidomics and MS imaging. Keywords Lipid . Lipidomics . Ion mobility . Mass spectrometry

Ion-mobility mass spectrometry for lipidomics The goal of lipidomics is to analyze the wide and complex array of lipid species present in biological samples (Fig. 1) [1–7]. To successfully perform such analyses is challenging. Nevertheless, in recent years, ion-mobility spectrometry (IMS) technologies have supported the effort to do so [8–16]. IMS is a gas-phase electrophoretic technique that makes possible the separation of gas-phase lipid ions within a chamber pressurized with a buffer gas such as nitrogen [17, 18]. The IMS separation occurs on a timescale of milliseconds, making it suitable for coupling with mass spectrometry (MS), wherein detection usually occurs within microseconds. In IMS–MS, lipid ions traverse an ion-mobility separation cell before their MS detection. Currently, MS detection employs one of three major IMSseparation approaches: (1) drift-time IMS (DT-IMS) [19]; (2) traveling-wave IMS (TW-IMS) [20, 21]; and (3) fieldasymmetric IMS, also known as differential-mobility spectrometry [22]. In DT-IMS, ions migrate through a buffer gas in the presence of an axial, linear electric-field gradient. In TW-IMS, a sequence of applied voltages generates a Btraveling wave^ that propels the ions through the buffer gas. Thus, both DT-IMS and TW-IMS allow all the ions to pass through the mobility cell. Field-asymmetric IMS, however, operates by varying the compensation voltage, filtering

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Prenol lipids: β-carotene

Fatty acyl: fatty acid (FFA)

Glycerolipids: diacylglycerol (DG)

Polyketides: resveratrol

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Glycerophospholipids: phosphatidylcholine (PC)

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Fig. 1 Categorization of major lipid classes according to their chemical structures

selected ions in a space-dispersive fashion [17, 18, 23]. Comprehensive reviews addressing, in detail, the various ionmobility techniques have been presented by Kanu et al. [17], Lapthorn et al. [18], and May and McLean [23]. Early applications of IMS–MS in biology focused principally on peptides and small proteins [24–26], oligosaccharides [27], and oligonucleotides [28]. Gradually, use of IMS–MS expanded into proteomics [29, 30] and the characterization of protein complexes [31]. In recent years, exploiting advances in both hardware and software, researchers have increasingly used IMS–MS in traditional lipidomic and metabolomic approaches, including liquid chromatography–MS and directMS analyses (Fig. 2) [8–16, 32–38]. In the following sections, we review applications of IMS– MS for lipid analysis from the perspective of our experience in the laboratory.

Experimental collision cross sections In DT-IMS and TW-IMS instruments, the time required for lipid ions to cross the ion-mobility separation cell—the drift time—depends principally on the collision frequency between the ions and the buffer gas. Thus, drift times are directly related to the shape, size, and charge of the lipid ions as well as to the nature of the buffer gas. From their drift time, it is possible to calculate the rotationally averaged collision cross section (CCS), which represents the effective area for the interaction

between an individual ion and the neutral gas through which it travels [39]. The CCS, an important physicochemical property of the lipid species, is related to its chemical structure and three-dimensional conformation. In traditional DT-IMS instruments with a uniform electrostatic field, the CCS can be derived directly from the drift time [40]. In TW-IMS–MS, the applied electric field waveform function is more complex than in a drift tube, and CCSs are calculated from drift times of compounds with known CCS [41–45]. Previous studies have shown that calibration of the TW-IMS–MS device provides accurate lipid CCSs in good agreement with those obtained from drift-tube studies [42, 45]. These calibrations correct for the variation in drift times among TW-IMS–MS instruments located in independent laboratories, where the instruments’ ionmobility parameter settings might differ, resulting in highly reproducible CCS measurements [43, 46] (Fig. 3a). In state-of-theart TW-IMS–MS systems, the calibration step is fully automated, and drift times are tabulated directly as CCSs [46, 48]. When searched against databases, experimentally derived CCSs can provide an additional coordinate to support lipid identification [46]. Databases can be created using CCSs obtained by running synthetic standards or from computationally calculated values. The difference between the reference CCS (contained in the database) and the experimental CCS (ΔCCS) can contribute to the identification score [45, 46, 48]. Thus, it is possible to filter and score identifications when querying the database with CCS information, reducing the number of false positives and false negatives [45, 46].

Applications of ion-mobility mass spectrometry for lipid analysis

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Fig. 2 Ion-mobility spectrometry (IMS)–mass spectrometry (MS) can be used with traditional lipidomic approaches, such as online separation [e.g., ultra-high-performance liquid chromatography (UHPLC)– IMS–MS], direct infusion (i.e., shotgun lipidomics), and desorption ionization [e.g., matrixassisted laser desorption ionization (MALDI) IMS–MS, desorption electrospray ionization (DESI) IMS–MS, and direct analysis in real time (DART) IMS–MS]. Independently from the inlet source and the ionization mode, lipid ions are separated according to their charge, size, and shape before MS detection. CSS collision cross section, RT retention time

Theoretical CCSs Computational modeling of ions in the gas phase for CCS elucidation [24, 49, 50] was initially applied to large Fig. 3 a Reproducibility of experimentally derived CCS measurements across different instruments. Different drift-time values for the same molecule were obtained using different IMS parameter settings. Polyalanine was used as a calibrator to correct the final CCS measurements. b Workflow for conformational sampling using distance geometry or molecular dynamics (MD)-based calculations. (a Reproduced with permission from [46], copyright 2014, American Chemical Society; b Reproduced with permission from [47], copyright 2014, American Chemical Society)

CCS 2D Separation

macromolecular ions such as carbon clusters [51], polymers [52], peptides [53], proteins [25], nucleotides [28], and carbohydrates [27]. Lipids, however, are unique biomolecules [54–56]. They lack the repeating biopolymeric units that, in

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the gas phase, are characterized by strong intramolecular interactions—hydrogen bonding, salt bridging, and π–π stacking—which, to differing degrees, are observed in peptides, carbohydrates, and nucleotides [42]. In addition, lipids are structurally diverse, necessarily so because they fulfill numerous biochemical roles, including structural support and organization of membranes, membrane-embedded proteins, local and systemic signaling, and energy storage [4, 6, 7]. The structural variety of lipids poses a unique challenge to the computational prediction of how lipid ions behave in the gas phase. In silico modeling of lipids requires molecular dynamics (MD) parameters that describe the gas-phase behavior of atoms within specific lipid features, such as conjugated systems, cis/trans double bonds, and ether and plasmalogen linkages. Whereas reasonably accurate MD parameters exist for the modeling of amino acids, nucleotides, and carbohydrates, parameterization of unique lipid features is progressing slowly [57, 58]. Nevertheless, parameters for modeling of glycerolipids and glycerophospholipids with ester-linked fatty acids and common head groups are now available [59]. For sterol lipids, prenol lipids, and saccharolipids, the already available small-molecule and oligosaccharide parameters are sufficient for modeling. In addition, the bioinformatics community is also proactively pursuing the means to incorporate new lipid parameters for MD-based modeling of membrane proteins in lipid membranes [59, 60]. A typical modeling workflow for an in silico lipid structure begins with a quantum-mechanical calculation and point-

charge derivation of atomic partial charges. This process, which uses quantum-mechanical prediction software such as Gaussian®, Cerius®, or Jaguar® (Fig. 3b), is followed by MDbased energy minimization that relies on an informatics product such as AMBER®, CHARMM®, or ACCELRYS®. During the process, a charge-parameterized and energyminimized structure is subjected to a temperature scheme that allows adequate conformational sampling. The sampling, usually performed at high energy, is followed by a gradual cooling to low energy, typically yielding a set of thousands of structural conformers. As an alternative to MD-based sampling, theoretical CCSs can be calculated by means of distance geometry, wherein structural conformers are generated by sampling progressively larger distance bounds for pairwise distances between the atoms within a lipid structure [47] (Fig. 3b). After the conformational sampling, the theoretical CCSs of the modeled structures are calculated using MOBCAL [49, 50], SIGMA [51, 52], or PSA [61] optimized for the correct buffer gas (helium or nitrogen) (Fig. 3b). A subset of the conformers, whose theoretical CCSs match the experimental CCS range, are then further analyzed for common structural features by clustering over shared conformational attributes. Clustering analyses can encompass all atoms of a given lipid structure or only user-specified atoms relevant to the observed separation. Such in silico approaches allow conformational and clustering analysis of thousands of lipid structures over relatively short durations and the interpretation of experimental CCS data [49, 51, 52, 62, 63].

Fig. 4 Representative data-independent acquisition (DIA) and IMS-DIA of co-eluted phosphatidylethanolamine (PE) species (PE plasmalogen 18:0/20:4 and PE plasmalogen 18:0/18:1) from human brain in negative electrospray ionization mode. a Applying DIA to complex lipid mixtures leads to MS/MS spectra containing a mixture of collision-induced fragment ions derived from multiple co-eluted precursors, which complicates

the interpretation of the spectra. b To help identify complex mixtures of lipids, fragmentation of precursor ions can be performed after the IMS separation. The inclusion of an ion-mobility separation of co-eluted precursor ions before the fragmentation produces cleaner MS/MS production spectra, which facilitates lipid identification and reduces false-positive assignments. FA fatty acid, TOF-MS time-of-flight mass spectrometer

Applications of ion-mobility mass spectrometry for lipid analysis

IMS–MS for increasing the specificity of fragmentation Combined with ultra-high-performance liquid chromatography (UHPLC), a data-independent acquisition (DIA) mode that alternates between low collision energy and elevated collision energy has been successfully demonstrated in lipidomic analysis [12, 64, 65]. In a single application, DIA provides accurate-mass measurement of precursor ions and product ions obtained from the low-collision-energy and the elevated-collision-energy data [64, 65]. In this acquisition mode, product ions can be subsequently matched with the appropriate precursor ions according to retention-time correlation [64, 65]. However, applying the DIA mode for the MS analysis of lipid extracts could result in MS/MS spectra containing a mixture of product ions derived from multiple coeluted lipid precursors, complicating interpretation of the spectra and the overall identification process (Fig. 4a). Combining IMS–MS with DIA might allow the separation of co-eluted lipid precursor ions before fragmentation, resulting in a drift-time correlation of product ions with their respective precursor ions and thus cleaner MS/MS product-ion spectra [8,

9, 12, 66–70] (Fig. 4b). These clean MS/MS spectra might, in turn, facilitate lipid identification and reduce false-positive assignments in complex matrices [8, 9, 12, 66–73]. Recently, IMS–MS has been applied to lipid profiling using data-dependent acquisition (DDA) [74, 75]. DDA experiments switch from full-scan MS mode to MS/MS mode after specific criteria have been met. Such switching could result in poor duty cycles and a decrease in sensitivity, especially when UHPLC with its narrow elution peaks is used. By reducing background noise interferences in biological samples, however, IMS–MS in DDA mode might achieve an overall improvement in detection limits [45, 74].

IMS–MS in traditional lipidomic approaches Online separation and IMS–MS Reversed-phase UHPLC has been extensively used to separate lipid species mostly according to their hydrophobicity, which is related to the number of carbons and double bonds in the acyl chains (Fig. 5) [10, 76–78]. As an alternative,

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Retention time (min) Fig. 5 Representative online separation IMS–MS chromatograms for complex lipid mixtures obtained using hydrophilic interaction chromatography–UHPLC–IMS–MS (a) and reversed-phase UHPLC–

IMS–MS (b). Cer ceramide, ChoE cholesteryl ester, PC phosphatidylcholine, PG phosphatidylglycerol, SM sphingomyelin, TG triacylglycerol

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normal-phase chromatography, hydrophilic interaction chromatography, or supercritical fluid chromatography can be used to separate lipid classes according to their polarity [79–84] (Fig. 5). Yet even the most advanced chromatographic technique cannot completely separate the wide array of lipids in biological samples, an insufficiency that is exacerbated when short chromatographic runs are needed to increase throughput [81]. Because chromatographic separations occur in seconds and IMS separations occur in milliseconds, IMS can be coupled with chromatography, providing an additional degree of separation to increase peak capacity and the specificity of lipid identification [8–10, 44, 74, 81, 85–87]. IMS–MS in shotgun lipidomics In shotgun lipidomics experiments, lipid extracts are directly infused into the ionization source of a mass spectrometer. Such an analytical approach provides high-throughput lipidomic analysis of biological samples. Nevertheless, the approach is also often associated with the inability to distinguish isobaric species [35, 42, 56]. The use of IMS–MS with direct-infusion experiments could partially overcome this drawback by separating lipids in a two-dimensional space according to their mobility times and masses [54]. The inclusion of ion mobility in a shotgun lipidomics experiment may provide as much as a fivefold increase in peak capacity [33–45]. Lipids can then be identified not only by accurate mass but also by their drift times or CCSs [45]. By plotting mobility versus mass, one can differentiate lipids from other classes of biomolecules such as peptides, carbohydrates, and oligosaccharides [42, 56, 88]. Lipid classes (e.g., phosphatidylcholines and sphingomyelins) and lipid subclasses (e.g., vinyl ether phosphatidylethanolamines and acyl phosphatidylethanolamines) fall into distinct trend lines on a m/z–mobility plot, facilitating the feature annotation of unknown lipid structures [11, 33, 45, 54, 56, 89] (Fig. 6). The degree of unsaturation also affects the separation of lipid species in the mobility space [11, 45]. The observed increase in peak capacity and specificity of analysis ultimately improves lipid fingerprinting and identification in shotgun lipidomics applications [42, 56] (Figs. 5 and 7). Additionally, IMS may add selectivity to the separation and detection of structural isomers, isobars, and conformers [90–97]. Derivatization methods and alternative ion-mobility gases have both been used to maximize the separation of isobaric and isomeric lipid species by IMS [14, 74, 75, 89]. The derivatization increases the CCS of the isomers, affecting the interactions of the lipid ion with the drift gas and thus improving their separation in the ion-mobility cell. In addition, the use of volatile modifiers in the mobility cell [98, 99] and IMS gases of different polarizabilities and changes in the pressure of these gases have also been used to affect the separation of lipid isomers [14, 100, 101].

Desorption ionization with IMS–MS IMS–MS imaging Lipids localize in different compositions and concentrations across the surface of biological samples. MS imaging allows topographic mapping of the lipid content of cell cultures and tissue sections. In a typical IMS–MS imaging experiment, a focused excitatory beam (e.g., a laser or a stream of charged-solvent droplets) is directed at the biological sample to scan the surface along a user-defined two dimensional array [9, 102, 103]. On impact of the excitatory beam, biomolecular ions are desorbed from the sample surface and ionized and are directed into the mass spectrometer. The addition of IMS to a typical MS imaging experiment allows separation of the lipid ions of interest from the interfering background before MS detection, resulting in a greater signal-to-noise ratio and more accurate lipid localization (Fig. 8a). In addition to accurate mass, the experimental CCS of each detected lipid ion can be searched against CCS databases to support lipid identification [9] (Fig. 8a). Various desorption ionization techniques have been combined with IMS–MS for imaging of lipids, including matrix-assisted laser desorption ionization [9, 105–108] desorption electrospray ionization (Fig. 8a), and laser ablation electrospray ionization [109].

Real-time IMS–MS For high-throughput fingerprinting of biological samples, various ambient MS techniques allow the rapid sampling and ionization of lipids directly from solid or liquid samples without prior sample treatment [110–112]. Recently, IMS has been combined with ambient ionization as a postionization separation tool, improving the specificity of lipid fingerprinting and the signal-to-noise ratio [94, 113–117]. In the example depicted in Fig. 8b, IMS made possible the separation of the entire molecular profile of sebum (the oil present on human skin) in milliseconds, and a complete, combined IMS–MS and direct analysis in real time experiment required only a few seconds of operation [104]. Fig. 6 Lipids class separation by distinct trend lines or domains using„ field-asymmetric IMS (FA-IMS)–MS (a), drift-time IMS (DT-IMS)–MS (b), and traveling-wave IMS (TW-IMS)–MS (c). In FA-IMS–MS experiments, mobility times are expressed as a function of the compensation voltages that were scanned to filter the various lipid ions. In DT-IMS–MS and TW-IMS–MS experiments, mobility times are expressed as either drift times or CCSs. DTIMS drift-time IMS, FAIMS field-asymmetric IMS, TWIMS traveling-wave IMS. (a Reproduced with permission from [89], copyright 2015, Springer; b Reproduced with permission from [54], copyright 2015, Springer; c Reproduced with permission from [45], copyright 2015, American Chemical Society)

Applications of ion-mobility mass spectrometry for lipid analysis

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Fig. 7 Representative IMS–MS shotgun lipidomics analysis of lipid extracted from porcine brain in positive electrospray ionization mode. Lipids were separated into two dimensions according to both CCS and

m/z. As previously shown [45], the additional CCS coordinate allowed the separation of isobaric background interferences (inserts), which improves the specificity of lipid analysis

Fig. 8 a Representative DESI-IMS–MS image of a rat brain section. Desorbed ions generate complex spectra composed of metabolites, lipids, and proteins that can be separated in a two-dimensional plot by both m/z and CCS. MS imaging of rat brain generated using m/z values with CCS selection increases the specificity and the signal-to-noise ratio, which ultimately affect the spatial resolution. b. Real-time IMS–MS lipidomic analysis can be conducted using ambient desorption ionization sources, including a DART ion source (IonSense, Saugus, MA, USA), an atmospheric solids analysis probe (ASAP), and rapid

evaporative ionization mass spectrometry (REIMS). In this example, sebum samples were swiped on a capillary and placed near the DART ion source. After ionization, the molecular content of the sebum is separated using ion mobility. The data were processed as threedimensional molecular maps using CCS, m/z, and intensity as coordinates [104]. Sebum skin oils from two human subjects can be compared by overlaying individual molecular maps to show areas where the samples are significantly different [104]. TWIM travelingwave ion mobility

Applications of ion-mobility mass spectrometry for lipid analysis

Conclusions and future directions IMS has been increasingly adopted for lipidomic applications, the result, largely, of hardware enhancements. Nevertheless, the concomitant development of user-friendly software—to automate processing of IMS–MS data—is also a factor in the use of ion-mobility information in large-scale lipidomic studies. The combination of IMS with traditional MS-based analytical approaches highlights important future directions for lipidomic applications. First, IMS has been increasingly used to determine the CCS, an additional physicochemical identifier, to support lipid identification. The high reproducibility of CCS determinations allows the creation of databases that can be used by multiple laboratories. Extending current CCS databases will significantly benefit the use of such an approach for lipid identification. Because it is not practical to prepare a comprehensive database containing experimental CCSs for all of the lipids present in biological samples, a complementary strategy will likely take the form of computationally predicted CCSs and the development of novel in silico models for theoretical CCS calculations. On the basis of current knowledge of where individual lipid classes reside in the m/z–CCS two-dimensional space, probability calculations for the likelihood of an unknown belonging to a specific lipid class can be performed [42, 89]. Such an endeavor would rely on the development of novel software tools for probability distribution calculations based on published experimental CCSs of lipid species. Second, the combination of UHPLC–IMS with various data-dependent and data-independent MS/MS acquisition modes is providing a new set of tools for the structural characterization of lipid species in routine lipidomic applications. No instrument currently provides high-resolution isolation of target precursor ions. The capability of IMS to reduce background-noise interferences prior to fragmentation would, therefore, also benefit quantitative lipidomic approaches. Lipid ions could then be selected on the basis of their mobility times before MS/MS fragmentation, thus increasing the selectivity and specificity of analyses. Third, IMS has been increasingly applied in direct-MS analyses, including shotgun lipidomics and MS imaging experiments, as a postionization separation tool to increase peak capacity and the signal-to-noise ratio. This application leads to improved lipid fingerprinting, quantification, and spatial localization capabilities. Novel, exciting venues of research for IMS–MS in lipidomics include the ability to perform multi-omics studies and to investigate the interplay of lipid species with other macromolecules such as sugars, nucleic acids, proteins, and enzymes [118, 119]. We anticipate that incorporating IMS technology into traditional MS workflows will provide novel opportunities of research for both qualitative and quantitative lipidomic applications.

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