title: Retention Time Prediction Improves Identification in Nontargeted Lipidomics Approaches.
abstract: 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 (tR) 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.
authors.affiliation: †Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, and Department of Computer Science, University of Tuebingen, Sand 14, 72076 Tuebingen, Baden-Württemberg, Germany.
authors.forename: Fabian
authors.initials: F
authors.lastname: Aicheler
authors.affiliation: ‡Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China.
authors.forename: Jia
authors.initials: J
authors.lastname: Li
authors.affiliation: §Division of Clinical Chemistry and Pathobiochemistry, Department of Internal Medicine IV, University Hospital Tuebingen, 72076 Tuebingen, Baden-Württemberg, Germany.
authors.forename: Miriam
authors.initials: M
authors.lastname: Hoene
authors.affiliation: §Division of Clinical Chemistry and Pathobiochemistry, Department of Internal Medicine IV, University Hospital Tuebingen, 72076 Tuebingen, Baden-Württemberg, Germany.|∥Department of Molecular Diabetology, Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tuebingen, 72076 Tuebingen, Baden-Württemberg, Germany.|⊥German Center for Diabetes Research (DZD), 72076 Tuebingen, Baden-Württemberg, Germany.
authors.forename: Rainer
authors.initials: R
authors.lastname: Lehmann
authors.affiliation: ‡Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China.
authors.forename: Guowang
authors.initials: G
authors.lastname: Xu
authors.affiliation: †Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, and Department of Computer Science, University of Tuebingen, Sand 14, 72076 Tuebingen, Baden-Württemberg, Germany.|∥Department of Molecular Diabetology, Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tuebingen, 72076 Tuebingen, Baden-Württemberg, Germany.|⊥German Center for Diabetes Research (DZD), 72076 Tuebingen, Baden-Württemberg, Germany.
authors.forename: Oliver
authors.initials: O
authors.lastname: Kohlbacher
mesh_terms: D002623:Chemistry Techniques, Analytical / Q000379:methods*; D002853:Chromatography, Liquid; D008055:Lipids / Q000032:analysis* / Q000737:chemistry; D013058:Mass Spectrometry
chemical_list: D008055:Lipids