PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements
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Published:2023-04-28
Issue:1
Volume:14
Page:
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ISSN:2041-1723
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Container-title:Nature Communications
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language:en
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Short-container-title:Nat Commun
Author:
Bilbao AivettORCID, Munoz NathalieORCID, Kim Joonhoon, Orton Daniel J.ORCID, Gao YuqianORCID, Poorey Kunal, Pomraning Kyle R.ORCID, Weitz Karl, Burnet Meagan, Nicora Carrie D.ORCID, Wilton Rosemarie, Deng Shuang, Dai Ziyu, Oksen EthanORCID, Gee Aaron, Fasani Rick A., Tsalenko Anya, Tanjore Deepti, Gardner James, Smith Richard D.ORCID, Michener Joshua K.ORCID, Gladden John M., Baker Erin S.ORCID, Petzold Christopher J.ORCID, Kim Young-MoORCID, Apffel Alex, Magnuson Jon K.ORCID, Burnum-Johnson Kristin E.ORCID
Abstract
AbstractMultidimensional 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 ofAspergillus pseudoterreus, Aspergillus niger, Pseudomonas putidaandRhodosporidium 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.
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary
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