In recent years, instrumental improvements have enabled the spread of mass spectrometry-based lipidomics platforms in biomedical research. In mass spectrometry, the reliability of generated data varies for each compound, contingent on, among other factors, the availability of labeled internal standards. It is challenging to evaluate the data for lipids without specific labeled internal standards, especially when dozens to hundreds of lipids are measured simultaneously. Thus, evaluation of the performance of these platforms at the individual lipid level in interlaboratory studies is generally not feasible in a time-effective manner. Herein, using a focused subset of sphingolipids, we present an in-house validation methodology for individual lipid reliability assessment, tailored to the statistical analysis to be applied. Moreover, this approach enables the evaluation of various methodological aspects, including discerning coelutions sharing identical selected reaction monitoring transitions, pinpointing optimal labeled internal standards and their concentrations, and evaluating different extraction techniques. While the full validation according to analytical guidelines for all lipids included in a lipidomics method is currently not possible, this process shows areas to focus on for subsequent method development iterations as well as the robustness of data generated across diverse methodologies.

Open access funding provided by Karolinska Institute. CEW received support from the Swedish Heart and Lung Foundation (HLF 20230463 and HLF 20210519) and the Swedish Research Council (2022-00796). BZ is supported by the H2020 ITN consortium ArthritisHeal (#812890), the Konung Gustaf V:s 80-årsfond (FAI-2020-0732), and the Swedish Heart and Lung Foundation (HLF20230363). AW received support from the Swedish Heart and Lung Foundation (HLF 20190017) and the Swedish Research Council (2018-00520). JJ received support from the Spanish Ministry of Science and Innovation MCIU/AEI/10.13039/501100011033 and Severo Ochoa Excelencia Grant CEX2018-000794-S.

Peer reviewed