The capability to chemically identify thousands of metabolites and other chemicals in clinical samples will revolutionize the search for environmental, dietary, and metabolic determinants of 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 propose to overcome 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 from analysis of authentic chemical standards. A paradigm shift in metabolomics, we will use gas-phase molecular properties that can be both accurately predicted computationally and consistently measured experimentally, and which can thus be used for comprehensive identification of the metabolome without the need for authentic chemical standards. The outcomes of this proposal directly advance the mission and goals of the NIH Common Fund by: (i) transforming metabolomics science by enabling consideration of the totality of the human metabolome through optimized identification of currently unidentifiable molecules, eventually reaching hundreds of thousands of molecules, and (ii) developing standardized computational tools and analytical methods to increase the national capacity for biomedical researchers to identify metabolites quickly and accurately. This work is significant because it enables comprehensive and confident chemical measurement of the metabolome. This work is innovative because it utilizes an integrated quantum-chemistry and machine learning computational pipeline to accurately predict physical-chemical properties of metabolites coupled to measurements.
OVERALL NARRATIVE This project will utilize integrated quantum-chemistry and machine learning computational computational approaches coupled with advanced instrumentation to characterize the human metabolome, and identify currently unidentifiable molecules without the use of authentic chemical standards. Results from these studies will contribute to the goal of understanding diseases, and the tools and resources will be made publically available for biomedical researchers.
| Couvillion, Sneha P; Zhu, Ying; Nagy, Gabe et al. (2018) New mass spectrometry technologies contributing towards comprehensive and high throughput omics analyses of single cells. Analyst : |
