Computationally unmasking each fatty acyl C=C position in complex lipids by routine LC-MS/MS lipidomics
Leonida M. Lamp, Gosia M. Murawska, Joseph P. Argus, Aaron M. Armando, Radu A. Talmazan, Marlene Pühringer, Evelyn Rampler, Oswald Quehenberger, Edward A. Dennis, Jürgen Hartler
Abstract
Abstract 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 2400 complex lipid species identified in RAW264.7 macrophages, including 1145 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 A 2 (cPLA 2 ). Accordingly, C=C position information is now readily accessible for large-scale high-throughput studies with any MS/MS instrumentation and ion activation method.