Peak Annotation and Verification Engine for Untargeted LC–MS Metabolomics

doi:10.1021/acs.analchem.8b03132

This content will become publicly available on December 26, 2019

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

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Abstract

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/Nmore »« less

Authors:

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)

Publication Date:: 2018-12-26

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

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)

OSTI Identifier:: 1491815

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

Resource Type:: Accepted Manuscript

Journal Name:: Analytical Chemistry

Additional Journal Information:: Journal Volume: 91; Journal Issue: 3; Journal ID: ISSN 0003-2700

Publisher:: American Chemical Society (ACS)

Country of Publication:: United States

Language:: English

Subject:: 59 BASIC BIOLOGICAL SCIENCES; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats


                    Wang, Lin, Xing, Xi, Chen, Li, Yang, Lifeng, Su, Xiaoyang, Rabitz, Herschel, Lu, Wenyun, and Rabinowitz, Joshua D. Peak Annotation and Verification Engine for Untargeted LC–MS Metabolomics.  United States: N. p., 2018. 
        Web.  doi:10.1021/acs.analchem.8b03132.

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                    Wang, Lin, Xing, Xi, Chen, Li, Yang, Lifeng, Su, Xiaoyang, Rabitz, Herschel, Lu, Wenyun, & Rabinowitz, Joshua D. Peak Annotation and Verification Engine for Untargeted LC–MS Metabolomics.  United States.  doi:10.1021/acs.analchem.8b03132.

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                    Wang, Lin, Xing, Xi, Chen, Li, Yang, Lifeng, Su, Xiaoyang, Rabitz, Herschel, Lu, Wenyun, and Rabinowitz, Joshua D. Wed .  
        "Peak Annotation and Verification Engine for Untargeted LC–MS Metabolomics".  United States.  doi:10.1021/acs.analchem.8b03132.

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@article{osti_1491815,

  title        = {Peak Annotation and Verification Engine for Untargeted LC–MS Metabolomics},

  author       = {Wang, Lin and Xing, Xi and Chen, Li and Yang, Lifeng and Su, Xiaoyang and Rabitz, Herschel and Lu, Wenyun and Rabinowitz, Joshua D.},

  abstractNote = {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.},

  doi          = {10.1021/acs.analchem.8b03132},

  journal      = {Analytical Chemistry},

  number       = 3,

  volume       = 91,

  place        = {United States},

  year         = {2018},

  month        = {12}

}

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