Gas chromatography–mass spectrometry with chemometric analysis for determining 12C and 13C labeled contributions in metabolomics and 13C flux analysis
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 Î¼M known metabolite concentration (for the 11 metabolites), with an average predicted concentration of 5.07 Î¼M, 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. âº GC–MS with chemometrics for analyzing coeluting 12C and 13C labeled contributions. âº The stacked samples in GC–MS create a three-dimensional data cube. âº PARAFAC is utilized for mathematical peak deconvolution and isotopic isolation. âº The accuracy and precision of metabolomics and 13C flux analysis are enhanced.