Metabolite discovery through global annotation of untargeted metabolomics data

Liquid chromatography-high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified. Here we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims...

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Bibliographic Details
Published in:Nature methods Vol. 18; no. 11; pp. 1377 - 1385
Main Authors: Chen, Li, Lu, Wenyun, Wang, Lin, Xing, Xi, Chen, Ziyang, Teng, Xin, Zeng, Xianfeng, Muscarella, Antonio D, Shen, Yihui, Cowan, Alexis, McReynolds, Melanie R, Kennedy, Brandon J, Lato, Ashley M, Campagna, Shawn R, Singh, Mona, Rabinowitz, Joshua D
Format: Journal Article
Language:English
Published: United States Nature Publishing Group 01.11.2021
Subjects:
Algorithms
Animals
Annotations
Chromatography, Liquid - methods
Data Curation - methods
Data Curation - standards
Fragmentation
Global optimization
Isotopes
Liquid chromatography
Liver - metabolism
Mass spectrometry
Mass spectroscopy
Metabolites
Metabolome
Metabolomics
Metabolomics - methods
Metabolomics - standards
Mice
Network management systems
Optimization
Saccharomyces cerevisiae - metabolism
Scientific imaging
Spectroscopy
Tandem Mass Spectrometry - methods
Taurine
Thiamine
Yeast
ISSN:1548-7091, 1548-7105, 1548-7105
Online Access:Get full text
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Summary:Liquid chromatography-high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified. Here we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times and (when available) tandem mass spectrometry fragmentation patterns. Peaks are connected based on mass differences reflecting adduction, fragmentation, isotopes, or feasible biochemical transformations. Global optimization generates a single network linking most observed ion peaks, enhances peak assignment accuracy, and produces chemically informative peak-peak relationships, including for peaks lacking tandem mass spectrometry spectra. Applying this approach to yeast and mouse data, we identified five previously unrecognized metabolites (thiamine derivatives and N-glucosyl-taurine). Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.
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ISSN:1548-7091
1548-7105
1548-7105
DOI:10.1038/s41592-021-01303-3