Abstract
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.
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