GNPSapplication/xmlftp://massive-ftp.ucsd.edu/v09/MSV000097637/primaryOK200MetabolomicsJuergen HartlerEdward A DennisZenoTOF 7600Q ExactiveHomo Sapiens (ncbitaxon:9606)Mus Musculus (ncbitaxon:10090)https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=a69526cc26f04f098cb86d7804c686dbUniversity of California, San DiegoUniversity of Grazjuergen.hartler@uni-graz.atedennis@ucsd.eduGNPS3,055MS:1002864 - No post-translational-modifications are included in the identified peptides of this datasetfalseGNPS - Computationally unmasking each fatty acyl C=C position in complex lipids by routine LC-MS/MS lipidomicsIdentifying 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.Tue Apr 15 22:04:00 BST 2025MSV000097637