Profiling of phospholipids and related lipid structures using multidimensional ion mobility spectrometry-mass spectrometry
Introduction
The identification and biological characterization of novel lipids [1], [2], [3], [4], [5], [6], [7], [8], [9], greatly depends on developments in mass spectrometry (MS). These developments gave rise to the field of lipidomics, one goal of which is the identification of all endogenous lipids in an effort to elucidate new signaling cascades. Recent lipidomics approaches targeting putative endogenous N-acyl amino acids utilizing commercial high resolution mass spectrometers and a custom-built program for database searching of m/z fingerprints characterized 58 novel and 8 previously identified endogenous N-acyl amino acids from rodent and bovine nervous tissue [10], [11].
Despite these achievements, MS methods are limited to measuring only ion intensity and mass-to-charge (m/z) ratios thus providing indirect information related to structure. Additional structural information is frequently obtained through a combination of techniques such as nuclear magnetic resonance spectroscopy, liquid chromatography (LC), and multidimensional MS [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21]. The differentiation of isomers is often difficult even using hyphenated LC-MSn approaches [10], [11], [22], [23], [24]. Challenges are also encountered when using electrospray ionization (ESI) because of ionization suppression and chemical background which cause information loss due to convolution of lipid complexity, insufficient ionization, and poor sensitivity for certain lipids [25], [26], [27].
Matrix-assisted laser desorption/ionization (MALDI), with often superb sensitivity and high throughput capabilities [28], [29], [30], finds similar limitations [31], [32], particularly at low m/z where background interferences can be significant, thus allowing other laser desorption methods to become attractive choices [33]. For example, a matrix-ionization laser desorption method showed remarkable reduction in background and permitted the elucidation of the localization of the double bond position(s) of fatty acids utilizing charge-remote fragmentation (CRF) on a commercial time-of-flight (TOF)/TOF instrument [17]. The fundamental importance of relating molecular structure to understanding biosynthetic pathways [34], [35], [36], [37] and the need for rapid analysis of structurally, chemically, and dynamically complex systems has led to a search for novel means of characterizing lipid compositions on a molecular level.
Recently, we reported the analysis of complex materials including human plasma [38] and synthetic polymers [39] based on gas-phase separation using ESI-ion mobility spectrometry (IMS) MS. The output of the joint measurements is a multidimensional separation that contains IMS drift time (td, in ms) and MS data (m/z, in ns) displayed in a nested fashion td(m/z) with a false color image of ion abundances providing a three-dimensional (3D) image. This high-efficiency separation coupled to MS detection requires only a few milliseconds revealing detailed molecular makeup. Closely related structures, even as mixtures of structural isomers, are readily discerned with IMS-MS by exploiting differences in molecular shapes, sizes, and abundances [39]. Similar IMS-MS instrumentation have been developed by different groups but only a few applications have been aimed at small molecule analysis [40], [41], [42].
Here, we demonstrate the utility of multidimensional IMS-MS for lipid analysis providing a detailed view of molecular components in chemical, structural, and dynamic complex mixtures that is based on a combined analysis of the 3D geometries and masses of lipid components adducted with cations in the gas-phase. Examples (Scheme 1) focus on the characterization of phospholipids and N-arachidonoyl ethanolamide (anandamide, AEA), precursor molecules to N-acyl amino acids [2], [5], [10], [11], and reveal the ability to distinguish structural isomers. Additional examples are provided showing the detection of low-abundance, ionization-retarded N-acyl amino acids in the presence of higher-abundant mixture components such as phosphatidylcholine. Multidimensional IMS3-MS methodology expands the abilities for obtaining mass measurements with great efficiencies from a targeted lipidomics analysis into areas with significantly increased complexity, as is the case in global lipidomics analysis, while the analytical information for challenging N-acyl amino acid lipids is maintained.
Section snippets
Materials
Chloroform, tetrahydrofuran, dichloromethane, 2-butyl alcohol, and HPLC grade methanol were purchased from VWR International, Plainview, NY. Mass spectrometry/HPLC grade ammonium acetate, glycine ethyl ester hydrochloride, and LiOH were purchased from Sigma-Aldrich (St. Louis, MO). HPLC grade water was purified to a quality of ≥18.0 MΩ cm (e.g., Milli-Q system, Millipore, Billerica, MA, USA). Palmitic acid was purchased from Nu-Chek Prep (Elysian, MN). N-arachidonoyl ethanolamide (anandamide,
General characterization of aggregates
The IMS-MS analyses of lipids lead to several interesting trends related to size and charge-state distributions. Phosphatidylinositols (PI) showed intense aggregation with features up to [PI16+6cat]6+ (Fig. 1). This dataset is visually remarkable in that the charge states running diagonally through the image are baseline resolved for the entire PI sample, even for isobaric compositions, e.g., [(PI)4+2cat]2+, [(PI)6+3cat]3+, vs. [(PI)8+4cat]4+.
Problems associated with charge-state convolution
Conclusions
Multidimensional IMS-MS is shown to reduce the complexity of lipid mixtures, including those of acidic, neutral, and basic lipids, and provides considerable detail relating to their structural composition. IMS-MS analysis makes it possible to observe a range of low-abundance lipids that would normally be lost in the background if only MS is used for the analysis. Perhaps most important here is the snapshot of sample composition with an extreme wealth of extractable data for qualitative (m/z)
Acknowledgments
The authors are grateful for having been inspired by Prof. J. Michael Walker (1950-2008), a gifted mentor, kind colleague, and friend. The authors are thankful for the support of grants from the National Institutes of Health/National Institute on Drug Abuse (DA16825, DA018224), the Linda and Jack Gill Center for Biomolecular Science, Indiana University, and the MetaCyt Grant to Indiana University from the Lilly Foundation Inc., Indianapolis IN. We thank Dr. Susan M. Huang (Johns Hopkins
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