Original Article


, Volume 7, Issue 1, pp 1-14

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

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

  • Maud M. KoekAffiliated withAnalytical Research Department, TNO Quality of Life Email author 
  • , Frans M. van der KloetAffiliated withLACDR Analytical Biosciences, Leiden University
  • , Robert KleemannAffiliated withDepartment of Vascular and Metabolic Disease, TNO Quality of Life
  • , Teake KooistraAffiliated withDepartment of Vascular and Metabolic Disease, TNO Quality of Life
  • , Elwin R. VerheijAffiliated withAnalytical Research Department, TNO Quality of Life
  • , Thomas HankemeierAffiliated withLACDR Analytical Biosciences, Leiden UniversityNetherlands Metabolomics Centre


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.


Metabolomics Comprehensive two-dimensional gas chromatography mass spectrometry GC × GC–MS Automated data processing Diabetes Insulin resistance