Database-dependent metabolite profiling focused on steroid and fatty acid derivatives using high-temperature gas chromatography-mass spectrometry

Background: A database-dependent gas chromatography-mass spectrometry (GC-MS) based approach was developed for non-targeted metabolite profiling, focusing on 232 steroids, 24 fatty acids, 10 eicosanoids, 10 cannabinoids and 22 steroid-fatty acid esters in biological specimens. Methods: This method, used to search for potent biomarkers in lipid metabolism, included MS based analysis combined with high-temperature gas chromatographic (HTGC) separation of biological metabolites, statistical clustering and an in-house database (DB) searching. Results: The HTGC technique showed better detectability of high lipophilic compounds, particularly steroid-fatty acid esters, which generally have poor chromatographic properties on a conventional GC column. The in-house DB search consisted of the retention index and mass spectrum corresponding to each compound selected. The method was applied to tissue samples obtained from cardiac hypertrophy-induced mice. Increased levels of palmitic, linoleic, oleic, and stearic acids and cholesterol were detected and identified. Conclusions: This data-dependent non-targeted metabolite profiling technique could be more effective in biomarker studies associated with the steroid and lipid metabolism than commercially available DBs.