A comprehensive UHPLC-MS/MS method for metabolomics profiling of signaling lipids: Markers of oxidative stress, immunity and inflammation

Abstract

Signaling lipids (SLs) play a crucial role in various signaling pathways, featuring diverse lipid backbone structures. Emerging evidence showing the biological significance and biomedical values of SLs has strongly spurred the advancement of analytical approaches aimed at profiling SLs. Nevertheless, the dramatic differences in endogenous abundances across lipid classes as well as multiple isomers within the same lipid class makes the development of a generic analytical method challenging. A better analytical method that combines comprehensive coverage and high sensitivity is needed to enable us to gain a deeper understanding of the biochemistry of these molecules in health and disease. In this study, we developed a fast and comprehensive targeted ultra-high performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) method for profiling SLs. The platform enables analyses of 260 metabolites covering oxylipins (isoprostanes, prostaglandins and other oxidized lipids), free fatty acids, lysophospholipids, sphingoid bases (C16, C18), platelet activating factors (C16, C18), endocannabinoids and bile acids. Various validation parameters including linearity, limit of detection, limit of quantification, extraction recovery, matrix effect, intra-day and inter-day precision were used to characterize this method. Metabolite quantitation was successfully achieved in both NIST Standard Reference Material for human plasma (NIST SRM 1950) and pooled human plasma, with 109 and 144 metabolites quantitated. The quantitation results in NIST SRM 1950 plasma demonstrated good correlations with certified or previously reported values in published literature. This study introduced quantitative data for 37 SLs for the first time. Metabolite concentrations measured in NIST SRM 1950 will serve as essential reference data for facilitating interlaboratory comparisons. The methodology established here will be the cornerstone for in-depth profiling of signaling lipids across diverse biological samples and contexts.