TRACE: An Integrated Isotope-Tracing Framework for Metabolite Validation and Nutrient Fate Mapping

Abstract

Untargeted LC-MS metabolomics offers a broad view of the microbial metabolism. However, its application is hindered by two intertwined challenges: distinguishing true biological signals from chemical artifacts and quantifying nutrient partitioning under nutrient-competitive conditions. Here, we present TRACE, an integrated experimental and computational framework that dynamically calibrates mass and retention time tolerances from the data itself to construct isotope-informed peak networks, enabling rigorous discrimination of biological metabolites from artifacts. Across four LC-MS platforms, TRACE reveals that the proportion of high-confidence annotations fell from 2.94 to 1.48%, while the total features increased by 331% from lower- to higher-sensitivity instruments. TRACE also maps nutrient fates into metabolic pathways by detecting isotopic dilution in Saccharomyces cerevisiae cultured with 13C-glucose, 15N-ammonium, and other unlabeled nutrients. Specifically, labeling of glutathione, a linear assembly of three amino acids, accurately reflect direct incorporation from its constituent amino acids; NAD+, whose biosynthesis proceeds through concurrent salvage and de novo pathways, revealed how adenine, tryptophan, and glutamine shaped its final isotopologue pattern. By converting untargeted LC-MS data into functional maps of nutrient flow, TRACE establishes a system-level approach to interrogate microbial metabolism under physiologically relevant competitive conditions.