Untargeted extraction of metabolites 13C labeling profiles from time course labeling switch experiment

Dynamic isotope labeling data provide crucial information about the operation of metabolic pathways, and are commonly generated via liquid chromatography mass spectrometry (LC-MS). Metabolome-wide analysis is challenging as it requires grouping of metabolite features over different samples. We developed DynaMet for fully automated investigations of isotope labeling experiments from LC-high resolution MS raw data. DynaMet enables untargeted extraction of metabolite labeling profiles and provides integrated tools for expressive data visualization. For this study we generated labeling data of the model strain Escherichia coli from 13C glucose labeling switch experiments. Analysis of two biological replicates revealed high robustness and reproducibility of the pipeline. DynaMet analysis of two data sets each comprising 19 samples resulted in 286 and 293 features, respectively, with detection in at least 80% of all samples analyzed. Of these, 222 and 230, respectively, could be fitted with the implemented model. After removing false positives and merging both data sets DynaMet revealed a total number of 291 features with labeling profiles whereof 285 could be generated with implemented fitting function. Comparison with KEGG database resulted in 125 matches corresponding to 98 metabolites covering multiple pathways of core metabolism and major biosynthetic routes. The study results reported here are only a summary. To reproduce the complete results i.e. labeling profiles and corresponding kinetic fits including plots, DynaMet software is required.