Abstract
A significant hurdle that has limited progress in microbiome science has been identifying and studying the diverse set of metabolites produced by gut microbes. Gut microbial metabolism produces thousands of difficult-to-identify metabolites, which present a challenge to study their roles in host biology. In recent years, mass spectrometry-based metabolomics has become one of the core technologies for identifying small metabolites. However, metabolomics expertise, ranging from sample preparation to instrument use and data analysis, is often lacking in academic labs. Most targeted metabolomics methods provide high levels of sensitivity and quantification, while they are limited to a panel of predefined molecules that may not be informative to microbiome-focused studies. Here we have developed a gut microbe-focused and wide-spectrum metabolomic protocol using liquid chromatography–mass spectrometry and bioinformatic analysis. This protocol enables users to carry out experiments from sample collection to data analysis, only requiring access to a liquid chromatography–mass spectrometry instrument, which is often available at local core facilities. By applying this protocol to samples containing human gut microbial metabolites, spanning from culture supernatant to human biospecimens, our approach enables high-confidence identification of >800 metabolites that can serve as candidate mediators of microbe–host interactions. We expect this protocol will lower the barrier to tracking gut bacterial metabolism in vitro and in mammalian hosts, propelling hypothesis-driven mechanistic studies and accelerating our understanding of the gut microbiome at the chemical level.
Key points
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This protocol describes a method for detecting and measuring metabolites derived from the gut microbiota in diverse biological samples.
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The protocol combines untargeted data collection methods with a targeted data analysis pipeline, allowing characterization of a broad range of microbial metabolites.
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Data availability
The sample metabolomics data required for bioinformatics analysis are available on GitHub (https://github.com/the-han-lab/Han_et_al_Metabolomics_Protocol_2022). We have deposited our MS2 spectra data on the community-accepted MoNA public database, but the MoNA search algorithm is not optimized to return only the relevant data submitted by our lab; to enable quick user access, we have also deposited the full MS2 data in our lab’s GitHub repository (given above). All metabolomics raw data associated with our primary publication15 are publicly accessible on the Metabolomics Workbench under study number ST001683 for all in vivo data and study number ST001688 for in vitro data. New metabolomics data generated for this protocol are also available on the same platform under study number ST002973.
Code availability
The code required for bioinformatics analysis is available on GitHub (https://github.com/the-han-lab/Han_et_al_Metabolomics_Protocol_2022).
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Acknowledgements
We thank Stanford ChEM-H for use of the Agilent 6545 LC/Q-TOF instrument, and W. Van Treuren, D. Dodd, C. R. Fischer, B. C. DeFelice, J. M. Sanchez, M. A. Fischbach and A. Stewart for helpful input on method development and optimization. This work is supported by R01-DK085025, DP1-AT009892, R01-DK101674 and a Stanford Discovery Innovation Fund Award (J.L.S.); Mallinckrodt Foundation Grant, Glenn Foundation for Medical Research and AFAR Grants for Junior Faculty, Duke Science and Technology Initiative and Whitehead Scholar (S.H.); 5T32AI007328-35 (E.R.G.); and Duke University School of Medicine Biomedical PhD Student Research Pilot Grant (Y.L.). J.L.S. is a Chan-Zuckerberg Biohub Investigator.
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Contributions
S.H., E.R.G. and J.L.S. conceived the study and wrote the paper. S.H., E.R.G. and Y.L. generated the figures. S.H. developed methods for building the MS1 and MS2 mass spectrometry reference libraries, metabolite extraction and data analysis via MS-DIAL, conducted bioinformatics analyses and built the bioinformatics pipeline integrating data from diverse sample types. Y.L. optimized and validated the metabolomics analysis workflow using an independent orbitrap instrument, conducted metabolomic experiments and performed data analyses.
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Nature Protocols thanks Thomas Horvath and Martin Giera for their contribution to the peer review of this work.
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Related links
Key references in the development of the protocol
Han, S. et al. Nature 595, 415–420 (2021): https://doi.org/10.1038/s41586-021-03707-9
Mars, R. A. T. et al. Cell 182, 1460–1473.e17 (2020): https://doi.org/10.1016/j.cell.2020.08.007
Wastyk, H. C. et al. Cell 184, 4137–4153.e14 (2021): https://doi.org/10.1016/j.cell.2021.06.019
Extended data
Extended Data Fig. 1 Example images of extracted ion chromatograms generated in the Agilent MassHunter Qualitative Data Analysis software.
a, Example of a single peak identified from extracting a chromatogram from a single m/z value. b, Example of multiple peaks identified from extracting a chromatogram based on a single m/z value. c, Example of no peak identified from extracting a chromatogram based on a single m/z value.
Extended Data Fig. 2 Pre- and post-filtering comparisons in peak areas.
Pairwise molecular feature comparisons before and after the mega media being passed through a 0.2 μm membrane filter. a, b, Two independent batches of mega media have been tested (n = 5 for each of pre- and post-filtered conditions for each batch) using the C18 positive method. 1882 unique molecular features are reported for both batches following the background noise elimination and peak quality checks. Linear regression and r2 values of pairwise molecular feature comparisons, performed on log10 transformed peak area data: pre-filtered versus post-filtered in batch 1 (r2 = 0.977, Post-filtered peak area = 1 × Pre-filtered peak area - 0.0109, a) and batch 2 (r2 = 0.994, Post-filtered peak area = 0.9974 × Pre-filtered peak area + 0.0206, b).
Extended Data Fig. 3 Agilent qTOF mass calibration settings on Agilent MassHunter.
Example calibration shown is in the negative ionization mode (e.g., C18 negative method). For the positive ionization mode (C18 positive, HILIC positive), these calibration settings remain identical with the exception of shifting ion polarity from “negative” to “positive” in both “Ion polarity” and “qTOF” sections. a, Tune and calibration. b, Instrument state. Red boxes highlight each tab in the software that corresponds to each type of settings.
Extended Data Fig. 4 Project settings using the MS-DIAL software.
a, General project information. b, Project file information. Red boxes and associated arrows highlight explanations for specific input.
Extended Data Fig. 5 MS-DIAL analysis parameters and alignment result export settings.
a, Data collection. b, Peak detection. c, MS2 deconvolution (not used in this protocol). d, Identification. e, Adduct (not modified here, but information is provided in the reference library input file in “Identification.”) f, Alignment. g, Isotope tracking (not used in this protocol). h, Alignment result export. Red boxes and associated arrows highlight explanations for specific input.
Extended Data Fig. 6 Residual plots for RT, precursor ion m/z, and peak area of a panel of internal standards detected in individual biological samples from a representative mouse serum metabolomic study.
a, b, Residual plot for linear regression model constructed using RTs (a) or precursor ion m/z values (b). Residuals are calculated based on values from individual biological samples vs. reference library. c, Residual plot for linear regression model constructed on peak areas of extracted ion chromatograms. Residuals are calculated based on detected values from individual biological samples (log10-transformed) vs. QC samples (log10-transformed). A single dot represents individual biological replicate per condition (e.g., germ-free or mono-association). Overlapping dots, which represent identical values, are shown in darker shades than the non-overlapping dots.
Extended Data Fig. 7 MS-DIAL user interface for checking the quality of annotated peaks.
a, Example of visualizing an analyzed dataset on MS-DIAL. b, Example of a low-quality peak to be eliminated from the output result file. Orange boxes highlight specific items to select for visualization. Red arrows highlight specific explanations or instructions.
Supplementary information
Supplementary Information
Supplementary guide for Supplementary Tables 1 and 2.
Supplementary Table 1
Bacterial metabolites.
Supplementary Table 2
Co-eluting metabolites.
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Han, S., Guiberson, E.R., Li, Y. et al. High-throughput identification of gut microbiome-dependent metabolites. Nat Protoc (2024). https://doi.org/10.1038/s41596-024-00980-6
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DOI: https://doi.org/10.1038/s41596-024-00980-6
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