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PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements

Author

Listed:
  • Aivett Bilbao

    (Pacific Northwest National Laboratory
    US Department of Energy, Agile BioFoundry)

  • Nathalie Munoz

    (Pacific Northwest National Laboratory
    US Department of Energy, Agile BioFoundry)

  • Joonhoon Kim

    (Pacific Northwest National Laboratory
    US Department of Energy, Agile BioFoundry)

  • Daniel J. Orton

    (Pacific Northwest National Laboratory)

  • Yuqian Gao

    (Pacific Northwest National Laboratory
    US Department of Energy, Agile BioFoundry)

  • Kunal Poorey

    (Sandia National Laboratory)

  • Kyle R. Pomraning

    (Pacific Northwest National Laboratory
    US Department of Energy, Agile BioFoundry)

  • Karl Weitz

    (Pacific Northwest National Laboratory)

  • Meagan Burnet

    (Pacific Northwest National Laboratory)

  • Carrie D. Nicora

    (Pacific Northwest National Laboratory)

  • Rosemarie Wilton

    (US Department of Energy, Agile BioFoundry
    Argonne National Laboratory)

  • Shuang Deng

    (Pacific Northwest National Laboratory
    US Department of Energy, Agile BioFoundry)

  • Ziyu Dai

    (Pacific Northwest National Laboratory
    US Department of Energy, Agile BioFoundry)

  • Ethan Oksen

    (Lawrence Berkeley National Laboratory)

  • Aaron Gee

    (Agilent Research Laboratories, Agilent Technologies)

  • Rick A. Fasani

    (Agilent Research Laboratories, Agilent Technologies)

  • Anya Tsalenko

    (Agilent Research Laboratories, Agilent Technologies)

  • Deepti Tanjore

    (US Department of Energy, Agile BioFoundry
    Lawrence Berkeley National Laboratory)

  • James Gardner

    (US Department of Energy, Agile BioFoundry
    Lawrence Berkeley National Laboratory)

  • Richard D. Smith

    (Pacific Northwest National Laboratory)

  • Joshua K. Michener

    (US Department of Energy, Agile BioFoundry
    Oak Ridge National Laboratory)

  • John M. Gladden

    (US Department of Energy, Agile BioFoundry
    Sandia National Laboratory)

  • Erin S. Baker

    (University of North Carolina)

  • Christopher J. Petzold

    (US Department of Energy, Agile BioFoundry
    Lawrence Berkeley National Laboratory)

  • Young-Mo Kim

    (Pacific Northwest National Laboratory
    US Department of Energy, Agile BioFoundry)

  • Alex Apffel

    (Agilent Research Laboratories, Agilent Technologies)

  • Jon K. Magnuson

    (Pacific Northwest National Laboratory
    US Department of Energy, Agile BioFoundry)

  • Kristin E. Burnum-Johnson

    (Pacific Northwest National Laboratory
    US Department of Energy, Agile BioFoundry)

Abstract

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.

Suggested Citation

  • Aivett Bilbao & Nathalie Munoz & Joonhoon Kim & Daniel J. Orton & Yuqian Gao & Kunal Poorey & Kyle R. Pomraning & Karl Weitz & Meagan Burnet & Carrie D. Nicora & Rosemarie Wilton & Shuang Deng & Ziyu , 2023. "PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37031-9
    DOI: 10.1038/s41467-023-37031-9
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    References listed on IDEAS

    as
    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    2. Oliver Alka & Premy Shanthamoorthy & Michael Witting & Karin Kleigrewe & Oliver Kohlbacher & Hannes L. Röst, 2022. "DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    3. Pamela P. Peralta-Yahya & Mario Ouellet & Rossana Chan & Aindrila Mukhopadhyay & Jay D. Keasling & Taek Soon Lee, 2011. "Identification and microbial production of a terpene-based advanced biofuel," Nature Communications, Nature, vol. 2(1), pages 1-8, September.
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