ScholarWorks@중앙대학교

초록

Conventional non-targeted lipidomic workflows relying on automated data-dependent acquisition (DDA) often encounter challenges in reliably detecting and identifying minor lipid species because of their low signal intensities and co-elution with more abundant components. To address this limitation, a cross-species alignment strategy was developed to predict the retention times (RTs) of low-abundance lipids. This approach leverages a predictive model aligned with the systematic RT patterns of major lipid classes from readily available mammalian sources, for which retention behavior is governed by structural attributes such as the length of the acyl chain and degree of unsaturation. Various common biological matrices were evaluated as calibration samples by comparing lipidome coverage across subclasses, confirming that both tissue and in vitro mammalian cells are effective sources. Based on comprehensive RTs and the respective molecular weight of predicted lipids, targeted MS/MS elucidated low-abundance lipids within extracellular vesicles from the gut microbiota. Using this strategy, 29 minor, odd-chain lipid species that are often overlooked by automated DDA methods, were identified. Multivariate statistical analysis indicated that these odd chain lipids could serve as distinctive biomarkers for differentiating between the microbial genera (Bifidobacterium vs. Lactobacillus). This cross-species lipidomics strategy enhances the confidence and depth of lipidome characterization, enabling the discovery of novel biomarkers in complex biological systems.

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