In metabolomics, the objective is to identify differences in metabolite profiles between samples. A widely used tool in metabolomics investigations is gas chromatography-mass spectrometry (GC/MS). More than 400 compounds can be detected in a single analysis, if overlapping GC/MS peaks are deconvoluted. However, the deconvolution process is time-consuming and difficult to automate, and additional processing is needed in order to compare samples. Therefore, there is a need to improve and automate the data processing strategy for data generated in GC/MS-based metabolomics; if not, the processing step will be a major bottleneck for high-throughput analyses. Here we describe a new semiautomated strategy using a hierarchical multivariate curve resolution approach that processes all samples simultaneously. The presented strategy generates (after appropriate treatment, e.g., multivariate analysis) tables of all the detected metabolites that differ in relative concentrations between samples. The processing of 70 samples took similar time to that of the GC/TOFMS analyses of the samples. The strategy has been validated using two different sets of samples: a complex mixture of standard compounds and Arabidopsis samples.

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Analysis of the entire set of low molecular weight compounds (LMC), the metabolome, could provide deeper insights into mechanisms of disease and novel markers for diagnosis. In the investigation, we developed an extraction and derivatization protocol, using experimental design theory (design of experiment), for analyzing the human blood plasma metabolome by GC/MS. The protocol was optimized by evaluating the data for more than 500 resolved peaks using multivariate statistical tools including principal component analysis and partial least-squares projections to latent structures (PLS). The performance of five organic solvents (methanol, ethanol, acetonitrile, acetone, chloroform), singly and in combination, was investigated to optimize the LMC extraction. PLS analysis demonstrated that methanol extraction was particularly efficient and highly reproducible. The extraction and derivatization conditions were also optimized. Quantitative data for 32 endogenous compounds showed good precision and linearity. In addition, the determined amounts of eight selected compounds agreed well with analyses by independent methods in accredited laboratories, and most of the compounds could be detected at absolute levels of approximately 0.1 pmol injected, corresponding to plasma concentrations between 0.1 and 1 microM. The results suggest that the method could be usefully integrated into metabolomic studies for various purposes, e.g., for identifying biological markers related to diseases.