The last decade has seen rapid progress in instrumentation and data analysis techniques for untargeted metabolomics. High resolution mass spectrometry (HRMS) has significantly enhanced our ability not only to distinguish different metabolites by their exact m/z, but also to resolve distinct isotopologues of a given metabolite. For example, using HRMS one can successfully differentiate a 2H/15N/13C isotopologues that are indistinguishable at unit resolution. Stable isotope labeling experiments have contributed vastly to our knowledge of biological pathways in human, plant and bacterial species. However, the available software tools do not leverage the full potential of multiplexed experiments (experiments that involve multiple stable isotopes). Taking advantage of our current HRMS capabilities to resolve multiple labels and detect compounds with high mass accuracy, we can now perform isotope-assisted metabolomics profiling, which adds another dimension of information to address questions about metabolic pathway dynamics and regulation. Identifying the isotopically labeled species in such high-dimensional datasets and then extracting, quantifying, and visualizing isotope enrichment information in an automated way remains a major bottleneck. Our focus is to develop computational tools that adress the targeted and untargeted pipelines of the data analysis. We have developed a tool to extract and analyze the information in a targeted manner to process several files simultaneously, and a tool that can i) detect relevant compounds in the experiment (i.e., compounds that show a labeling pattern) and their corresponding isotopologues, ii) quantify their enrichment across multiple timepoints , and iii) suggest their identity based on their labeling properties and location within known metabolic networks in the context of untargeted labeling experiments. In this presentation we will show the tools and their applications in multiple metabolomic datasets..

Computational tools for leveraging high-resolution MS detection of stable isotopes for metabolomics

ACS Spring 2022

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