Differential Kendrick's Plots as an Innovative Tool for Lipidomics in Complex Samples: Comparison of Liquid Chromatography and Infusion-Based Methods to Sample Differential Study.
Justine HustinChristopher KuneJohann FarGauthier EppeDelphine DeboisLoïc QuintonEdwin De PauwPublished in: Journal of the American Society for Mass Spectrometry (2022)
Lipidomics has developed rapidly over the past decade. Nontargeted lipidomics from biological samples remains a challenge due to the high structural diversity, the concentration range of lipids, and the complexity of biological samples. We introduce here the use of differential Kendrick's plots as a rapid visualization tool for a qualitative nontargeted analysis of lipids categories and classes from data generated by either liquid chromatography-mass spectrometry (LC-MS) or direct infusion (nESI-MS). Each lipid class is easily identified by comparison with the theoretical Kendrick plot pattern constructed from exact mass measurements and by using MSKendrickFilter, an in-house Python software. The lipids are identified with the LIPID MAPS database. In addition, in LC-MS, the software based on the Kendrick plots returns the retention time from all the lipids belonging to the same series. Lipid extracts from a yeast ( Saccharomyces cerevisiae ) are used as a model. An on/off case comparing Kendrick plots from two cell lines (prostate cancer cell lines treated or not with a DGAT2 inhibition) clearly shows the effect of the inhibition. Our study demonstrates the good performance of direct infusion as a fast qualitative screening method as well as for the analysis of chromatograms. A fast screening semiquantitative approach is also possible, while the targeted mode remains the golden standard for precise quantitative analysis.
Keyphrases
- mass spectrometry
- liquid chromatography
- high resolution mass spectrometry
- prostate cancer
- saccharomyces cerevisiae
- fatty acid
- low dose
- tandem mass spectrometry
- emergency department
- high performance liquid chromatography
- capillary electrophoresis
- systematic review
- multiple sclerosis
- simultaneous determination
- cancer therapy
- drug delivery
- big data
- machine learning
- data analysis