Lamp Leonida M, Murawska Gosia M, Argus Joseph P, Armando Aaron M, Talmazan Radu A, Pühringer Marlene, Rampler Evelyn, Quehenberger Oswald, Dennis Edward A, Hartler Jürgen
Institute of Pharmaceutical Sciences, University of Graz, Graz, Austria.
Department of Pharmacology, University of California San Diego, La Jolla, CA, USA.
Nat Commun. 2025 Aug 11;16(1):7277. doi: 10.1038/s41467-025-61911-x.
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 (cPLA). Accordingly, C=C position information is now readily accessible for large-scale high-throughput studies with any MS/MS instrumentation and ion activation method.
确定复杂脂质中碳 - 碳双键（C=C）的位置对于阐明生理和病理过程至关重要。目前，在没有会降低离子产率的专门仪器的情况下，对天然脂质进行高通量分析时无法做到这一点。在此，我们展示了基于常规反相色谱串联质谱（RPLC-MS/MS）得出的保留时间，对复杂脂质中C=C位置进行自动化、链特异性鉴定。我们引入了LC=CL，这是一种计算解决方案，它利用了一个综合数据库，该数据库记录了在RAW264.7巨噬细胞中鉴定出的2400多种复杂脂质物种的洗脱图谱，其中包括1145种新报道的化合物。利用机器学习，LC=CL提供精确且自动化的C=C位置分配，适用于任何合适的色谱条件。为了说明LC=CL的强大功能，我们重新评估了先前发表的数据，并发现了胞质磷脂酶A（cPLA）新的C=C位置依赖性特异性。因此，现在使用任何MS/MS仪器和离子活化方法，都可以轻松获取C=C位置信息用于大规模高通量研究。
