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Four-dimensional trapped ion mobility spectrometry lipidomics for high throughput clinical profiling of human blood samples.

Raissa LernerDhanwin BakerClaudia SchwitterSarah NeuhausTony HauptmannJulia M PostStefan KramerLaura Bindila
Published in: Nature communications (2023)
Lipidomics encompassing automated lipid extraction, a four-dimensional (4D) feature selection strategy for confident lipid annotation as well as reproducible and cross-validated quantification can expedite clinical profiling. Here, we determine 4D descriptors (mass to charge, retention time, collision cross section, and fragmentation spectra) of 200 lipid standards and 493 lipids from reference plasma via trapped ion mobility mass spectrometry to enable the implementation of stringent criteria for lipid annotation. We use 4D lipidomics to confidently annotate 370 lipids in reference plasma samples and 364 lipids in serum samples, and reproducibly quantify 359 lipids using level-3 internal standards. We show the utility of our 4D lipidomics workflow for high-throughput applications by reliable profiling of intra-individual lipidome phenotypes in plasma, serum, whole blood, venous and finger-prick dried blood spots.
Keyphrases
  • high throughput
  • fatty acid
  • single cell
  • rna seq
  • mass spectrometry
  • machine learning
  • primary care
  • endothelial cells
  • high resolution
  • deep learning
  • electronic health record