Improved Annotation of Untargeted Metabolomics Data Through Buffer Modifications That Shift Adduct Mass and Intensity

Annotation of untargeted high-resolution full-scan LC-MS metabolomics data remains a difficult task. Existing literature suggests that LC-MS peaks can be divided into multiple major categories including “Background”, “Isotope”, “Adduct”, “Fragment” and “Candidate metabolite”. Among these, adduct annotation is a particular challenge, as the same mass difference between peaks can arise from adduct formation, fragmentation, or different biological species. To address this, here we describe a Buffer Modification Workflow (BMW), in which the same sample is run by LC-MS in both liquid chromatography solvent with 14NH3-acetate buffer, and in solvent with the buffer modified with 15NH3-formate. Buffer switching results in characteristic mass and signal intensity changes for adduct peaks, facilitating their annotation. In analyzing the candidate metabolite peaks, we recognized that some paradoxically increased in intensity over time between sample preparation and analysis. We show that such peaks are formed by chemical reactions between known metabolites and the extraction buffer and accordingly categorize these peaks as “Reaction Product”. Comparison using yeast extracts of BMW with a stable isotope labeling-based workflow suggests that BMW captures > 90% of candidate metabolites. This new workflow is well-suited to biological samples that cannot be readily isotope labeled, such as mammalian tissues and tumors. Application to mouse liver identified 26% of ~ 27,000 total peaks across positive and negative mode as candidate metabolites, of which ~ 2600 showed HMDB or KEGG database formula match.