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

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 Dai, Ethan Oksen, Aaron Gee, Rick A. Fasani, Anya Tsalenko, Deepti Tanjore, James Gardner, Richard D. Smith, Joshua K. Michener, John M. Gladden, Erin S. Baker, Christopher J. Petzold, Young-Mo Kim, Alex Apffel, Jon K. Magnuson and Kristin E. Burnum-Johnson ()
Additional contact information
Aivett Bilbao: Pacific Northwest National Laboratory
Nathalie Munoz: Pacific Northwest National Laboratory
Joonhoon Kim: Pacific Northwest National Laboratory
Daniel J. Orton: Pacific Northwest National Laboratory
Yuqian Gao: Pacific Northwest National Laboratory
Kunal Poorey: Sandia National Laboratory
Kyle R. Pomraning: Pacific Northwest National Laboratory
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
Shuang Deng: Pacific Northwest National Laboratory
Ziyu Dai: Pacific Northwest National Laboratory
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
Richard D. Smith: Pacific Northwest National Laboratory
Joshua K. Michener: US Department of Energy, Agile BioFoundry
John M. Gladden: US Department of Energy, Agile BioFoundry
Erin S. Baker: University of North Carolina
Christopher J. Petzold: US Department of Energy, Agile BioFoundry
Young-Mo Kim: Pacific Northwest National Laboratory
Alex Apffel: Agilent Research Laboratories, Agilent Technologies
Jon K. Magnuson: Pacific Northwest National Laboratory
Kristin E. Burnum-Johnson: Pacific Northwest National Laboratory

Nature Communications, 2023, vol. 14, issue 1, 1-18

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-37031-9

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