A scalable approach to absolute quantitation in metabolomics

Abstract

Mass spectrometry-based metabolomics allows for the quantitation of metabolite levels in diverse biological samples. The traditional method of converting peak areas to absolute concentrations involves the use of matched heavy isotopologues. However, this approach is laborious and limited to a small number of metabolites. We addressed these limitations by developing PyxisTM, a machine learning-based technology which converts raw mass spectrometry data to absolute concentration measurements without the need for per-analyte standards. Here, we demonstrate Pyxis performance by quantifying metabolome concentration dynamics in murine blood plasma. Pyxis performed equivalently to traditional quantitation workflows used by research institutions, with a fraction of the time needed for analysis. We show that absolute quantitation by Pyxis can be expanded to include concentrations for additional metabolites, without the need to acquire new data. Furthermore, Pyxis allows for absolute quantitation as part of an untargeted metabolomics workflow. By removing the bottleneck of per-analyte standards, Pyxis allows for absolute quantitation in metabolomics that is scalable to large numbers of metabolites. The ability of Pyxis to make concentration-based measurements across the metabolome has the potential to deepen our understanding of diverse metabolic perturbations.