Advantages of using biologically generated 13C-labelled multiple internal standards for stable isotope-assisted LC-MS-based lipidomics.

ABSTRACT

In comprehensive lipidomics studies, accurate quantification is essential but biological and/or clinical relevance is often hindered due to unwanted variations such as lipid degradation during sample preparation, matrix effects and non-linear responses of analytical instruments. In addition, the wide chemical diversity of lipids can complicate the accurate identification of individual lipids. These analytical limitations can potentially be corrected efficiently by the use of lipid-specific isotopically labelled internal standards (IS) but currently such IS mixtures have limited coverage of the mammalian lipidome. In this study, an in vivo13C labelling strategy was employed to explore four species (Escherichia coli, Arthrospira platensis, Saccharomyces cerevisiae and Pichia pastoris) as a source of 13C-labelled internal standards (13C-ISs) for more accurate and quantitative liquid chromatography (LC)-mass spectrometry (MS)-based lipidomics. Results showed that extracts from 13C-labelled P. pastoris and S. cerevisiae contain the highest percentage of uniformly labelled lipids (both 83% compared to 67% and 69% in A. platensis and E. coli, respectively) and 13C-labelled P. pastoris extract was identified as the optimum source of 13C-ISs for comprehensive data normalisation to correct unwanted variations during sample preparation and LC-MS analysis. Overall, use of a biologically generated 13C-IS lipid mixture of 357 identified lipid ions resulted in significant reduction in the lipid CV% of normalisation compared with other normalisation methods using total ion counts or a commercially available deuterated internal standard mixture. This improved normalisation using 13C-IS was confirmed in a typical lipidomics analysis using a large number of samples (>100+) and long analysis time (>70 h). This study highlights the benefit of an in vivo labelling strategy for reducing technical and analytical variations introduced during sample preparation and analysis in lipidomics studies.