Comprehensive non-targeted LC×LC-HRMS lipidomics workflow with active solvent modulation for the characterization of endocrine disruptor effects in zebrafish eleutheroembryos

Le résumé fourni par la source

Lipidomics is a powerful approach for investigating lipid alterations in complex biological systems. However, conventional liquid chromatography coupled to mass spectrometry (LC-MS) may be limited in non-targeted studies due to the high structural diversity and wide concentration range of lipid species. In this work, an optimized comprehensive two-dimensional liquid chromatography coupled to high-resolution mass spectrometry (LC×LC-HRMS) workflow was developed for non-targeted lipidomics. The method combines reversed-phase (RP) separation in the first chromatographic dimension with hydrophilic interaction liquid chromatography (HILIC) separation in the second dimension (RP×HILIC) and incorporates active solvent modulation (ASM) as an interface between the two chromatographic dimensions to improve analytical sensitivity and solvent compatibility across both dimensions. This approach improves sensitivity, solvent compatibility and lipidome coverage compared to conventional LC-MS workflows. The proposed workflow was applied to investigate lipidomic alterations in zebrafish (Danio rerio) eleutheroembryos exposed to the endocrine-disrupting chemical bisphenol A (BPA), using 17β-estradiol (E2) as an estrogenic control. The LC×LC-HRMS approach enabled enhanced lipidome coverage, resulting in the detection of 567 lipid features, of which 134 showed significant alterations associated with endocrine-disruptor exposure. Comparative analysis revealed lipidomic changes consistent with estrogenic responses for both BPA and E2 treatments, while additional lipid alterations suggested potential obesogenic effects under E2 exposure. In summary, the proposed LC×LC-HRMS workflow with ASM provides enhanced separation performance. The obtained results demonstrate the potential of comprehensive multidimensional chromatography combined with multivariate analysis tools for in-depth lipidomics studies in complex biological samples.

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