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Aicheler, F.* ; Li, J.* ; Hoene, M.* ; Lehmann, R. ; Xu, G.* ; Kohlbacher, O.

Retention time prediction improves identification in nontargeted lipidomics approaches.

Anal. Chem. 87, 7698-7704 (2015)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
Identification of lipids in nontargeted lipidomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) is still a major issue. While both accurate mass and fragment spectra contain valuable information, retention time (t(R)) information can be used to augment this data. We present a retention time model based on machine learning approaches which enables an improved assignment of lipid structures and automated annotation of lipidomics data. In contrast to common approaches we used a complex mixture of 201 lipids originating from fat tissue instead of a standard mixture to train a support vector regression (SVR) model including molecular structural features. The cross-validated model achieves a correlation coefficient between predicted and experimental test sample retention times of r = 0.989. Combining our retention time model with identification via accurate mass search (AMS) of lipids against the comprehensive LIPID MAPS database, retention time filtering can significantly reduce the rate of false positives in complex data sets like adipose tissue extracts. In our case, filtering with retention time information removed more than half of the potential identifications, while retaining 95% of the correct identifications. Combination of high-precision retention time prediction and accurate mass can thus significantly narrow down the number of hypotheses to be assessed for lipid identification in complex lipid pattern like tissue profiles.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Support Vector Regression; 2-dimensional Gas-chromatography; Flight Mass-spectrometry; Biological Samples; Plasma Lipidomics; Fatty-acids; Gc-ms; Database; Proteomics; System
ISSN (print) / ISBN 0003-2700
e-ISSN 1520-6882
Journal Analytical Chemistry
Quellenangaben Volume: 87, Issue: 15, Pages: 7698-7704 Article Number: , Supplement: ,
Publisher American Chemical Society (ACS)
Publishing Place Washington
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Diabetes Research and Metabolic Diseases (IDM)