Metabolite identification remains a major challenge in untargeted metabolomics, which can detect more than 10 000 peaks in a single LC–MS run. In this presentation I will demonstrate some approaches that can facilitate untargeted analysis and metabolite identification, using stable isotope labeling as a primary tool.<br/>I will first introduce Peak Annotation and Verification Engine (PAVE), a workflow for annotating untargeted microbial metabolomics data. The PAVE strategy combines isotopic labeling and computational data analysis to pick out biological peaks, assign C and N atom counts, and annotate adducts and natural isotopic variants. Remaining peaks are then discriminated from fragments based on a simple experimental strategy involving selective enhancement of fragment intensity by applying a weak in-source CID energy. The molecular formulas of the putative metabolites are then assigned based on database searching using both m/z and C/N atom counts.<br/>In some cases, molecular formula does not directly lead to compound identification due to existence of multiple isomers, and lack of authentic standards. I will then discuss some additional approaches that can solve these issues, using yeast gene YNL010W as an example. Untargeted metabolomics shows the building up of three metabolites in the knockout strain, and we successfully identified their identity even though authentic isomer standards are not available: time-course labeling shows that the unknown metabolites are related to pentose phosphate pathways, thus narrowing down candidate structures; in addition, feeding unlabeled substrates to cells growing in 13C-glucose shows the building up of the corresponding metabolites at correct retention time only for one of the isomers. These experiments demonstrate the power of stable isotope labeling in untargeted analysis and metabolite identification.

Using stable isotope labeling to facilitate unknown metabolite identification

2019 Fall National Meeting

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