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Structure Annotation of All Mass Spectra in Untargeted Metabolomics

  • Ivana Blaženović
    Ivana Blaženović
    West Coast Metabolomics Center, University of California, Davis, Davis, California 95616, United States
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  • , 
  • Tobias Kind
    Tobias Kind
    West Coast Metabolomics Center, University of California, Davis, Davis, California 95616, United States
    More by Tobias Kind
    Orcidhttp://orcid.org/0000-0002-1908-4916
  • , 
  • Michael R. Sa
    Michael R. Sa
    West Coast Metabolomics Center, University of California, Davis, Davis, California 95616, United States
    More by Michael R. Sa
  • , 
  • Jian Ji
    Jian Ji
    School of Food Science, State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 330047, China
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  • , 
  • Arpana Vaniya
    Arpana Vaniya
    West Coast Metabolomics Center, University of California, Davis, Davis, California 95616, United States
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  • , 
  • Benjamin Wancewicz
    Benjamin Wancewicz
    West Coast Metabolomics Center, University of California, Davis, Davis, California 95616, United States
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  • , 
  • Bryan S. Roberts
    Bryan S. Roberts
    West Coast Metabolomics Center, University of California, Davis, Davis, California 95616, United States
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  • , 
  • Hrvoje Torbašinović
    Hrvoje Torbašinović
    Inovatus Ltd., Zagreb 10000, Croatia
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  • , 
  • Tack Lee
    Tack Lee
    Department of Urology, Inha University College of Medicine, Incheon 22212, South Korea
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  • , 
  • Sajjan S. Mehta
    Sajjan S. Mehta
    West Coast Metabolomics Center, University of California, Davis, Davis, California 95616, United States
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  • , 
  • Megan R. Showalter
    Megan R. Showalter
    West Coast Metabolomics Center, University of California, Davis, Davis, California 95616, United States
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  • , 
  • Hosook Song
    Hosook Song
    Department of Urology, Inha University College of Medicine, Incheon 22212, South Korea
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  • , 
  • Jessica Kwok
    Jessica Kwok
    West Coast Metabolomics Center, University of California, Davis, Davis, California 95616, United States
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  • , 
  • Dieter Jahn
    Dieter Jahn
    Institute of Microbiology, Technische Universität Braunschweig, Braunschweig 38106, Germany
    Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig 38106, Germany
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  • , 
  • Jayoung Kim
    Jayoung Kim
    Departments of Surgery and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
    Department of Medicine, University of California Los Angeles, Los Angeles, California 90095, United States
    Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
    Department of Urology, Ga Cheon University College of Medicine, Incheon 22212, South Korea
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  • , and 
  • Oliver Fiehn*
    Oliver Fiehn
    West Coast Metabolomics Center, University of California, Davis, Davis, California 95616, United States
    *Phone +1-530-754-8258 (office), +1-530-752-9922 (lab), +1-530-723-4450 (cell). Corresponding author address: NIH West Coast Metabolomics Center, UC Davis Genome Center, Room 1313, 451 Health Sci Drive, Davis, CA 95616.
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    Orcidhttp://orcid.org/0000-0002-6261-8928
Cite this: Anal. Chem. 2019, 91, 3, 2155–2162
Publication Date (Web):January 4, 2019
https://doi.org/10.1021/acs.analchem.8b04698
Copyright © 2019 American Chemical Society
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    Abstract

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    Urine metabolites are used in many clinical and biomedical studies but usually only for a few classic compounds. Metabolomics detects vastly more metabolic signals that may be used to precisely define the health status of individuals. However, many compounds remain unidentified, hampering biochemical conclusions. Here, we annotate all metabolites detected by two untargeted metabolomic assays, hydrophilic interaction chromatography (HILIC)-Q Exactive HF mass spectrometry and charged surface hybrid (CSH)-Q Exactive HF mass spectrometry. Over 9,000 unique metabolite signals were detected, of which 42% triggered MS/MS fragmentations in data-dependent mode. On the highest Metabolomics Standards Initiative (MSI) confidence level 1, we identified 175 compounds using authentic standards with precursor mass, retention time, and MS/MS matching. An additional 578 compounds were annotated by precursor accurate mass and MS/MS matching alone, MSI level 2, including a novel library specifically geared at acylcarnitines (CarniBlast). The rest of the metabolome is usually left unannotated. To fill this gap, we used the in silico fragmentation tool CSI:FingerID and the new NIST hybrid search to annotate all further compounds (MSI level 3). Testing the top-ranked metabolites in CSI:Finger ID annotations yielded 40% accuracy when applied to the MSI level 1 identified compounds. We classified all MSI level 3 annotations by the NIST hybrid search using the ClassyFire ontology into 21 superclasses that were further distinguished into 184 chemical classes. ClassyFire annotations showed that the previously unannotated urine metabolome consists of 28% derivatives of organic acids, 16% heterocyclics, and 16% lipids as major classes.

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.8b04698.

    • Table S1, settings used for LC-MS/MS data processing of polar metabolites and biogenic amines (XLSX)

    • Table S2, multilevel compound annotations (XLSX)

    • Table S3, HILIC library annotations of all 43 IC patients (XLSX)

    • Table S4, structural classification of all spectra from 43 IC patients (XLSX)

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