4.7 Review

Mass spectrometry-based metabolomics in microbiome investigations

期刊

NATURE REVIEWS MICROBIOLOGY
卷 20, 期 3, 页码 143-160

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NATURE PORTFOLIO
DOI: 10.1038/s41579-021-00621-9

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资金

  1. FAPESP [2018/24865-4, 2015/17177-6, 2018/07098-0]
  2. Brazilian Fulbright Commission
  3. CNPq [142014/2018-4, 443281/2019-0, 306913/2017-8]
  4. NIH [1 U19 AG063744]
  5. Gordon and Betty Moore Foundation
  6. Crohn's & Colitis Foundation

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Mass spectrometry-based metabolomics is a key technology for detecting and identifying small molecules produced by the human microbiota and understanding their functional role. Microbiotas play a malleable part in ecosystems, affecting not only their specific niche but also distant environments. Data analysis, especially in the context of microbiome analysis, remains a challenge, with emphasis placed on integrative approaches and the integration of microbiome sequencing data.
Mass spectrometry-based metabolomics is one of the key technologies to detect and identify the small molecules produced by the human microbiota and to understand the functional role of these microbial metabolites. In this Review, Dorrestein and colleagues review common forms of untargeted mass spectrometry and the types of data that can be obtained in the context of microbiome analysis. Microbiotas are a malleable part of ecosystems, including the human ecosystem. Microorganisms affect not only the chemistry of their specific niche, such as the human gut, but also the chemistry of distant environments, such as other parts of the body. Mass spectrometry-based metabolomics is one of the key technologies to detect and identify the small molecules produced by the human microbiota, and to understand the functional role of these microbial metabolites. This Review provides a foundational introduction to common forms of untargeted mass spectrometry and the types of data that can be obtained in the context of microbiome analysis. Data analysis remains an obstacle; therefore, the emphasis is placed on data analysis approaches and integrative analysis, including the integration of microbiome sequencing data.

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