A novel method for the analysis of nearly co-eluting ¹²C and ¹³C isotopically labeled metabolites has been developed and evaluated for gas chromatography coupled to mass spectrometry (GC-MS) data. The method utilizes parallel factor analysis (PARAFAC) with two-dimensional GC-MS data when sample replicates are aligned and stacked in series to create a three-dimensional data cube for mathematical peak deconvolution and ¹²C and ¹³C contribution isolation, with the intent of increasing the accuracy and precision of quantitative metabolomics and ¹³C flux analysis. The platform is demonstrated with ¹³C-labeled metabolite extracts, generated via biosynthesis, added as an internal standard to unlabeled ¹²C metabolites extracted from the methanol-utilizing bacterium Methylobacterium extorquens AM1. Eleven representative metabolites that are common targets for flux analysis were chosen for validation. Good quantitative accuracy and precision were acquired for a 5.00 μM known metabolite concentration (for the 11 metabolites), with an average predicted concentration of 5.07 μM, and a RSD range of 1.2-13.0%. This study demonstrates the ability to reliably deconvolute ¹²C-unlabeled and ¹³C-labeled contributions for a given metabolite. Additionally, using this chemical analysis platform, a dynamic flux experiment is presented in which the incorporation of ¹³C-labeled cell extract can be detected in the methane-utilizing bacterium Methylosinus trichosporium OB3b and measured temporally.
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