Peak Annotation and Verification Engine for Untargeted LC–MS Metabolomics

Wang, Lin; Xing, Xi; Chen, Li; Yang, Lifeng; Su, Xiaoyang; Rabitz, Herschel; Lu, Wenyun; Rabinowitz, Joshua D.

doi:10.1021/acs.analchem.8b03132

Title: Peak Annotation and Verification Engine for Untargeted LC–MS Metabolomics

Journal Article · Wed Dec 26 00:00:00 EST 2018 · Analytical Chemistry

DOI:https://doi.org/10.1021/acs.analchem.8b03132· OSTI ID:1491815

Wang, Lin ^[1]; Xing, Xi ^[1]; Chen, Li ^[1]; Yang, Lifeng ^[1];

^[2];

^[1]; Lu, Wenyun ^[1];

^[1]

Princeton Univ., NJ (United States)
Princeton Univ., NJ (United States); Rutgers Univ., New Brunswick, NJ (United States)

Untargeted metabolomics can detect more than 10 000 peaks in a single LC–MS run. The correspondence between these peaks and metabolites, however, remains unclear. Here in this study, we introduce a Peak Annotation and Verification Engine (PAVE) for annotating untargeted microbial metabolomics data. The workflow involves growing cells in ¹³C and ¹⁵N isotope-labeled media to identify peaks from biological compounds and their carbon and nitrogen atom counts. Improved deisotoping and deadducting are enabled by algorithms that integrate positive mode, negative mode, and labeling data. To distinguish metabolites and their fragments, PAVE experimentally measures the response of each peak to weak in-source collision induced dissociation, which increases the peak intensity for fragments while decreasing it for their parent ions. The molecular formulas of the putative metabolites are then assigned based on database searching using both m/z and C/N atom counts. Application of this procedure to Saccharomyces cerevisiae and Escherichia coli revealed that more than 80% of peaks do not label, i.e., are environmental contaminants. More than 70% of the biological peaks are isotopic variants, adducts, fragments, or mass spectrometry artifacts yielding ~2000 apparent metabolites across the two organisms. About 650 match to a known metabolite formula based on m/z and C/N atom counts, with 220 assigned structures based on MS/MS and/or retention time to match to authenticated standards. Lastly, PAVE enables systematic annotation of LC–MS metabolomics data with only ~4% of peaks annotated as apparent metabolites.

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Research Organization:: Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0018420; SC0018260; FG02-02ER15344

OSTI ID:: 1491815

Journal Information:: Analytical Chemistry, Vol. 91, Issue 3; ISSN 0003-2700

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 44 works

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