Novel Equivalent Carbon Number Strategy for Large-Scale Lipidomics Data Analysis via Ultrahigh–Performance Liquid Chromatography–Orbitrap Astral Mass Spectrometry

Lipidomics enables studying lipid alterations in physiological or pathological states. The new Orbitrap mass spectrometry (MS), equipped with an Astral analyzer, significantly increases MS/MS acquisition rate. However, simultaneous analysis of lipid retention behavior and structural annotation from this data remains challenging. In this study, we propose a comprehensive strategy using the Equivalent Carbon Number (ECN) model to integrate the advantages of MS-DIAL (providing precise lipid retention behavior) and LipidSearch (offering accurate MS/MS spectral information). By investigating 34 lipid standards spiked into the NIST SRM 1950 plasma sample, the ECN strategy demonstrated high accuracy in retention time prediction with relative standard deviations below ± 5% for 90.0% of lipids in positive ion mode and 100% in negative ion mode. False-positive data from LipidSearch 5.1 were also significantly reduced; for example, in yeast, 68.8% and 80.1% of false positives were removed in the positive and negative ion modes, respectively. A total of 1933, 1539, 1969, 985, and 2786 lipids were annotated with the ECN strategy in HeLa cells, NIST plasma, mouse liver tissues, Saccharomyces cerevisiae yeast, and their pooled sample and were analyzed by Astral-MS, respectively. It was also found that the numbers of annotated lipids from Astral-MS data preprocessed with LipidSearch and MS-DIAL were 3-5 and 2-4 times higher than those from QE-MS data, respectively. This strategy enables efficient and accurate lipid identification with precise retention times and reliable MS/MS annotation, advancing large-scale lipidomics research and offering broad application in diverse biological contexts.