MassIVE MSV000097637

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GNPS - Computationally unmasking each fatty acyl C=C position in complex lipids by routine LC-MS/MS lipidomics

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Description

Identifying carbon-carbon double bond (C=C) positions in complex lipids is essential for elucidating physiological and pathological processes. Currently, this is impossible in high-throughput analyses of native lipids without specialized instrumentation that compromises ion yields. Here, we demonstrate automated, chain-specific identification of C=C positions in complex lipids based on the retention time derived from routine reverse-phase chromatography tandem mass spectrometry (RPLC-MS/MS). We introduce LC=CL, a computational solution that utilizes a comprehensive database capturing the elution profile of more than 2,400 complex lipid species identified in RAW264.7 macrophages, including 1,145 newly reported compounds. Using machine learning, LC=CL provides precise and automated C=C position assignments, adaptable to any suitable chromatographic condition. To illustrate the power of LC=CL, we re-evaluated previously published data and discovered new C=C position-dependent specificity of cytosolic phospholipase A2 (cPLA2). Accordingly, C=C position information is now readily accessible for large-scale high-throughput studies with any MS/MS instrumentation and ion activation method. [doi:10.25345/C5CV4C43V] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: RPLC ; lipidomics ; cPLA2 ; phospholipids ; double bond positions ; LC=CL ; EAD ; DatasetType:Metabolomics ; DatasetType:Other (Lipidomics)

Contact

Principal Investigators:
(in alphabetical order) 		Edward A Dennis, University of California, San Diego, USA
Juergen Hartler, University of Graz, Austria
Submitting User: Leonida
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GNPS content goes here (MSV000097637 [task=a69526cc26f04f098cb86d7804c686db])
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