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Mass spectrometry-based metabolomics in microbiome investigations

Nature Reviews Microbiology volume 20, pages 143–160 (2022)Cite this article

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Abstract

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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Fig. 1: Mass spectrometry-based metabolomics for studying the microbiota.
Fig. 2: Computational tools for metabolite annotation, substructure assessment and chemical classification.
Fig. 3: Data analysis tools to uncover microbiome-derived molecules.

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Acknowledgements

A.B. acknowledges FAPESP (2018/24865-4), H.M.-R. acknowledges the Brazilian Fulbright Commission and CNPq (142014/2018-4), L.V.C.-L. acknowledges FAPESP (2015/17177-6 and 2018/07098-0) and CNPq (443281/2019-0 and 306913/2017-8) and P.C.D. acknowledges the NIH (1 U19 AG063744), the Gordon and Betty Moore Foundation and the Crohn’s & Colitis Foundation for financial support.

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Authors and Affiliations

  1. Institute of Biomedical Science, Universidade de São Paulo, São Paulo, Brazil

    Anelize Bauermeister & Letícia V. Costa-Lotufo

  2. Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA

    Anelize Bauermeister, Helena Mannochio-Russo, Alan K. Jarmusch & Pieter C. Dorrestein

  3. Department of Biochemistry and Organic Chemistry, Institute of Chemistry, São Paulo State University, Araraquara, Brazil

    Helena Mannochio-Russo

  4. Department of Pediatrics, University of California, San Diego, San Diego, CA, USA

    Pieter C. Dorrestein

  5. Center for Microbiome Innovation, University of California, San Diego, San Diego, CA, USA

    Pieter C. Dorrestein

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  1. Anelize Bauermeister
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Contributions

P.C.D, A.B., H.M-R. and A.K.J. researched data for article. P.C.D, A.B., H.M-R., L.V.C.-L and A.K.J. substantially contributed to the discussion of content, wrote the article and reviewed and edited the manuscript before submission.

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Correspondence to Alan K. Jarmusch or Pieter C. Dorrestein.

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Nature Reviews Microbiology thanks M. Fischbach, B. B. Misra, O. Pedersen, who co-reviewed with Y. Fan, and P. Turnbaugh for their contribution to the peer review of this work.

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Related links

GNPS: https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp

SCiLS Lab: https://scils.de

Supplementary information

Glossary

Alpha diversity

A metric that summarizes the number of taxonomic groups or unique molecular features (species richness) and the evenness or balance of those microorganisms (species diversity) or molecular features that can be detected in the sample.

Beta diversity

The ratio between the diversity of molecules or organisms in the entire dataset and the diversity of the specific sample. This metric represents the diversity of microbial communities across different environments, also referred to ‘compositional heterogeneity’.

Rarefaction

A strategy whereby the summed number of unique data points (for example, operational taxonomic units in microbiome data or fragmentation spectra in mass spectrometry) are inventoried with each sample that is added to the sample set. It is often used to standardize samples of different sizes and determine whether a sample has been sequenced enough to represent its true diversity.

Procrustes analysis

A statistical model based on canonical correlation to shape the distribution of two or more groups of features from different omics datasets.

mmvec

One-layer neural networking method using bi-log–linear multinominal regression to predict the probability for co-occurrence relative to metadata.

Principal coordinate analysis

Unsupervised multivariate analysis used to calculate the interrelationships of a dataset, often used to reduce the dimensionality of large datasets.

MS1

Precursor mass of the intact molecular ion.

Liquid chromatography

(LC). A technique to separate two or more compounds present in a sample by exploiting the affinity balance between a stationary phase placed inside the chromatographic column and a mobile phase that flows through it.

Gas chromatography

(GC). A technique to separate compounds in a mixture by injecting a gaseous or liquid sample into a mobile phase, usually an inert gas such as helium.

Metabolomics Standards Initiative

(MSI). A formal initiative to define metabolite annotation and identification. It comprises four levels: level 1 represents the identified metabolites; level 2 represents the putatively annotated compounds; level 3 represents the putatively characterized chemical classes or molecular family; level 4 represents an unknown but detected mass spectrometry signal.

Schymanski system

A system for metabolite annotation. Level 1 represents the confirmed structure. Level 2 represents the probable structure. Level 3 represents the tentative candidate of the compound class. Levels 4 and 5 correspond to an unequivocal molecular formula assignment, and an exact mass of interest that still lacks molecular formula assignment.

False discovery rate

(FDR). The proportion of events that falsely seem to be significant.

Molecular networking

A computational algorithm that organizes fragmentation spectra by spectral similarities from which structural similarity is inferred. It is obtained via spectral alignment.

In silico annotation tools

Computational methods to improve compound annotation by exploring other approaches besides spectral libraries, such as machine learning.

Substructures

Small parts or functional groups in a chemical entity.

Machine learning

Application of artificial intelligence that is able to learn, adapt and improve its accuracy over time without being explicitly programmed.

Biosynthetic gene cluster

(BGC). A group of two or more genes in a particular genome that together encode a biosynthetic pathway.

ClassyFire

A hierarchical chemical classification of chemical entities.

Neural networks

A set of algorithms designed to recognize patterns and used to classify entities or make predictions. Modelled around the concept of neurons.

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Bauermeister, A., Mannochio-Russo, H., Costa-Lotufo, L.V. et al. Mass spectrometry-based metabolomics in microbiome investigations. Nat Rev Microbiol 20, 143–160 (2022). https://doi.org/10.1038/s41579-021-00621-9

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