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

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

Anal Methods

Centre for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.

Published: June 2023

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 C labelling strategy was employed to explore four species (, , and ) as a source of C-labelled internal standards (C-ISs) for more accurate and quantitative liquid chromatography (LC)-mass spectrometry (MS)-based lipidomics. Results showed that extracts from C-labelled and contain the highest percentage of uniformly labelled lipids (both 83% compared to 67% and 69% in and , respectively) and C-labelled extract was identified as the optimum source of C-ISs for comprehensive data normalisation to correct unwanted variations during sample preparation and LC-MS analysis. Overall, use of a biologically generated C-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 C-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 labelling strategy for reducing technical and analytical variations introduced during sample preparation and analysis in lipidomics studies.

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http://dx.doi.org/10.1039/d3ay00460kDOI Listing

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