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Automated lipid identification and quantification by multidimensional mass spectrometry-based shotgun lipidomics.

ABSTRACT: This article presents the strategies underlying the automated identification and quantification of individual lipid molecular species through array analysis of multidimensional mass spectrometry-based shotgun lipidomics (MDMS-SL) data, which are acquired directly from lipid extracts after direct infusion and intrasource separation. The automated analyses of individual lipid molecular species in the program employ a strategy in which MDMS-SL data from building block analyses using precursor ion scans, neutral loss scans, or both are used to identify individual molecular species, followed by quantitation. Through this strategy, the program screens and identifies species in a high-throughput fashion from a built-in database of over 36,000 potential lipid molecular species constructed employing known building blocks. The program then uses a two-step procedure for quantitation of the identified species possessing a linear dynamic range over 3 orders of magnitude and reverifies the results when necessary through redundant quantification of multidimensional mass spectra. This program is designed to be easily adaptable for other shotgun lipidomics approaches that are currently used for mass spectrometric analysis of lipids. Accordingly, the development of this program should greatly accelerate high-throughput analysis of lipids using MDMS-based shotgun lipidomics.

SUBMITTER: Yang K

PROVIDER: S-EPMC2728582 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Automated lipid identification and quantification by multidimensional mass spectrometry-based shotgun lipidomics.

Yang Kui K Cheng Hua H Gross Richard W RW Han Xianlin X

Analytical chemistry 20090601 11

This article presents the strategies underlying the automated identification and quantification of individual lipid molecular species through array analysis of multidimensional mass spectrometry-based shotgun lipidomics (MDMS-SL) data, which are acquired directly from lipid extracts after direct infusion and intrasource separation. The automated analyses of individual lipid molecular species in the program employ a strategy in which MDMS-SL data from building block analyses using precursor ion s ...[more]

PMID: 19408941

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Project description:BackgroundLipids are ubiquitous and serve numerous biological functions; thus lipids have been shown to have great potential as candidates for elucidating biomarkers and pathway perturbations associated with disease. Methods expanding coverage of the lipidome increase the likelihood of biomarker discovery and could lead to more comprehensive understanding of disease etiology.ResultsWe introduce LipidMatch, an R-based tool for lipid identification for liquid chromatography tandem mass spectrometry workflows. LipidMatch currently has over 250,000 lipid species spanning 56 lipid types contained in in silico fragmentation libraries. Unique fragmentation libraries, compared to other open source software, include oxidized lipids, bile acids, sphingosines, and previously uncharacterized adducts, including ammoniated cardiolipins. LipidMatch uses rule-based identification. For each lipid type, the user can select which fragments must be observed for identification. Rule-based identification allows for correct annotation of lipids based on the fragments observed, unlike typical identification based solely on spectral similarity scores, where over-reporting structural details that are not conferred by fragmentation data is common. Another unique feature of LipidMatch is ranking lipid identifications for a given feature by the sum of fragment intensities. For each lipid candidate, the intensities of experimental fragments with exact mass matches to expected in silico fragments are summed. The lipid identifications with the greatest summed intensity using this ranking algorithm were comparable to other lipid identification software annotations, MS-DIAL and Greazy. For example, for features with identifications from all 3 software, 92% of LipidMatch identifications by fatty acyl constituents were corroborated by at least one other software in positive mode and 98% in negative ion mode.ConclusionsLipidMatch allows users to annotate lipids across a wide range of high resolution tandem mass spectrometry experiments, including imaging experiments, direct infusion experiments, and experiments employing liquid chromatography. LipidMatch leverages the most extensive in silico fragmentation libraries of freely available software. When integrated into a larger lipidomics workflow, LipidMatch may increase the probability of finding lipid-based biomarkers and determining etiology of disease by covering a greater portion of the lipidome and using annotation which does not over-report biologically relevant structural details of identified lipid molecules.

Dataset Information

Automated lipid identification and quantification by multidimensional mass spectrometry-based shotgun lipidomics.

Publications

Automated lipid identification and quantification by multidimensional mass spectrometry-based shotgun lipidomics.

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