Scheduled data-dependent acquisition MS provides enhanced identification and sensitivity in clinical lipidomics

Lipidomics can provide critical insight into metabolic changes in health and disease, but faces challenges in sensitivity, lipid coverage, and annotation accuracy. To address these limitations, we optimized a liquid chromatography-mass spectrometry (LC-MS) method combining scheduled data-dependent acquisition (SDDA) and C30 column-based separations, aimed at improving global lipidomics for clinical diagnostics.

Compared to conventional DDA and Intelligent Data Acquisition (AcquireX), SDDA demonstrated a 2-fold increase in number of lipids annotated, with a 2-fold higher annotation confidence (Grade A and B) of those lipids compared to DDA. The repeatability and analytical robustness of the method were thoroughly evaluated across different clinical blood matrices, i.e. serum, EDTA-plasma, and dried blood spots (DBS). Serum provided the highest repeatability and lipid coverage, with more than 2000 lipid species annotated. A proof-of-concept study assessing postprandial lipidomic changes in response to intake of a long-chain triglyceride fat emulsion was used to demonstrate the method's applicability in clinical lipidomics. The method detected significant changes in the levels of various lipids, including triacylglycerols, diacylglycerols, bile acids, phosphatidylethanolamines, and lyso-phosphatidylethanolamines, following lipid ingestion.

The optimized lipidomics method (C30-SDDA) enhances lipid coverage and annotation confidence, proving valuable for studying metabolic alterations and biomarker discovery using blood matrices. These findings underscore the clinical potential of this method for advancing diagnostics and personalized medicine.