Bioanalytical insights into mediator lipidomics

• Lipidomics is one of the most popular “omics” in metabolomics.
• Analytical method validation in mediator lipidomics assures assay quality.
• A critical review of challenges of matrix effect evaluation in mediator lipidomics.
• Mediator lipidomics will contribute to decode pathology and encode physiology.

The importance of lipids in health and disease has been widely acknowledged. Lipids are well known to undergo enzymatic and/or non-enzymatic conversions to lipid mediators (LMs), which demonstrate potent actions in various biological events, such as the regulation of cellular signaling pathways and the promotion and resolution of inflammation. LMs activate G-protein-coupled receptors (GPCRs) to exert various functions. Monitoring these mediators in disease is essential to uncover the mechanisms of pathogenesis for many diseases, such as asthma, rheumatoid arthritis, Alzheimer's disease, and cancer. Along with technical developments in mass spectrometry, highly sensitive and multiplexed analyses of LMs in the human periphery and other tissues have become available. These advancements enable the temporal and spatial profiling of LMs; therefore, the findings obtained from LM profiling are expected to decode pathology. As trace amounts of LMs can exert functions, the development of a highly sensitive, accurate, and robust analytical method is necessary. Although not mandatory, mediator lipidomics validation is becoming popular and remains challenging. Because LMs already exist in biological matrices, evaluations of the matrix effect and extraction efficiencies are important issues. Thus, more careful analyses are required. In this review, we focus on mediator lipidomics, including polyunsaturated fatty acids (PUFAs), such as omega-3 and omega-6 fatty acids, and LMs derived from PUFAs, such as eicosanoids, lipoxins and resolvins. In addition to the recent progress in human mediator lipidomics, bioanalytical insights derived from this field (i.e., effective sample preparation from biological matrices and evaluation of the matrix effect) are described herein.

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