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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 möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Support Vector Regression; 2-dimensional Gas-chromatography; Flight Mass-spectrometry; Biological Samples; Plasma Lipidomics; Fatty-acids; Gc-ms; Database; Proteomics; System
Sprache englisch
Veröffentlichungsjahr 2015
HGF-Berichtsjahr 2015
ISSN (print) / ISBN 0003-2700
e-ISSN 1520-6882
Zeitschrift Analytical Chemistry
Quellenangaben Band: 87, Heft: 15, Seiten: 7698-7704 Artikelnummer: , Supplement: ,
Verlag American Chemical Society (ACS)
Verlagsort Washington
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Diabetes Research and Metabolic Diseases (IDM)
POF Topic(s) 90000 - German Center for Diabetes Research
Forschungsfeld(er) Helmholtz Diabetes Center
PSP-Element(e) G-502400-001
DOI 10.1021/acs.analchem.5b01139
WOS ID WOS:000359277900031
Erfassungsdatum 2015-09-04
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