Shifting the Metabolomics Paradigm: Exploiting Computationally Predicted Metabolite Reference Data for Comprehensive Metabolomics

About this Class

The capability to unambiguously and comprehensively identify thousands of metabolites and other chemicals in clinical samples, including the microbiome, will revolutionize the search for environmental, dietary, and metabolic determinants of health and disease. By comparison to near-comprehensive genetic information, comparatively little is understood of the totality of the human metabolome, largely due to insufficiencies in molecular identification methods. Through innovations in computational chemistry and advanced ion mobility separations coupled with mass spectrometry, we are overcoming a significant, long standing obstacle in the field of metabolomics: the absence of methods for accurate and comprehensive identification of metabolites without relying on data derived from analysis of authentic reference compounds. We use gas-phase molecular properties that can be both predicted computationally with high accuracy and experimentally measured with high precision, and which can thus be used for comprehensive identification of the metabolome without the need for reference libraries constructed through experimental analysis of authentic chemical standards. The benefits and remaining limitations of the standards-free metabolomics approach will be demonstrated in a variety of examples, including in analysis of blinded chemical mixtures as a part of the EPA’s Non-Targeted Analysis Collaborative Trial (ENTACT) and in analysis of plasma samples from individuals subjected to simulated shift work.