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Semi-automated non-target processing in GC × GC-MS metabolomics analysis: Applicability for biomedical studies

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Author: Koek, M.M. · Kloet, F.M. van der · Kleemann, R. · Kooistra, T. · Verheij, E.R. · Hankemeier, T.
Type:article
Date:2011
Source:Metabolomics, 1, 7, 1-4
Identifier: 427715
doi: doi:10.1007/s11306-010-0219-6
Keywords: Nutrition · Automated data processing · Comprehensive two-dimensional gas chromatography mass spectrometry · Diabetes · GC × GC-MS · Insulin resistance · Metabolomics · Triskelion BV Life · TAP - Toxicology and Applied Pharmacology QS - Quality & Safety · EELS - Earth, Environmental and Life Sciences

Abstract

Due to the complexity of typical metabolomics samples and the many steps required to obtain quantitative data in GC × GC-MS consisting of deconvolution, peak picking, peak merging, and integration, the unbiased non-target quantification of GC × GC-MS data still poses a major challenge in metabolomics analysis. The feasibility of using commercially available software for non-target processing of GC × GC-MS data was assessed. For this purpose a set of mouse liver samples (24 study samples and five quality control (QC) samples prepared from the study samples) were measured with GC × GC-MS and GC-MS to study the development and progression of insulin resistance, a primary characteristic of diabetes type 2. A total of 170 and 691 peaks were quantified in, respectively, the GC-MS and GC × GC-MS data for all study and QC samples. The quantitative results for the QC samples were compared to assess the quality of semi-automated GC × GC-MS processing compared to targeted GC-MS processing which involved time-consuming manual correction of all wrongly integrated metabolites and was considered as golden standard. The relative standard deviations (RSDs) obtained with GC × GC-MS were somewhat higher than with GC-MS, due to less accurate processing. Still, the biological information in the study samples was preserved and the added value of GC × GC-MS was demonstrated; many additional candidate biomarkers were found with GC × GC-MS compared to GC-MS. © 2010 The Author(s).