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Chemically informed analyses of metabolomics mass spectrometry data with Qemistree

Nature Chemical Biology volume 17, pages 146–151 (2021)Cite this article

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Abstract

Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow.

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Fig. 1: Qemistree mitigates aspects of technical artifacts by coclustering structurally similar molecules across MS runs.
Fig. 2: The pitfalls of assuming equal relatedness of molecules and the advantages of a chemical tree for sample comparison.
Fig. 3: A chemical hierarchy of food-derived compounds based on predicted molecular fingerprints.
Fig. 4: A hierarchy of the compounds observed in simple foods and seven complex samples.

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Data availability

The MS data, metadata and methods for the evaluation dataset have been deposited on the GNPS/MassIVE public repository2,32 under the accession number MSV000083306. Source data for the figures are available as Supplementary Datasets 2–5. The parameters used for molecular networking are available on GNPS at https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=efda476c72724b29a91693a108fa5a9d. The chemical hierarchy generated by Qemistree (v.2020.1.2) is available on iTOL24 at https://itol.embl.de/tree/709513416494381587432576. The MS data, metadata and methods for Global FoodOmics dataset have been deposited on the GNPS/MassIVE public repository2,32 under the accession number MSV000085226. The parameters used for molecular networking are available on GNPS at https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=ceb28a199d6b4f4fbf08490d9c96d631. The chemical hierarchy generated by Qemistree (v.2020.1.2) is available on iTOL24 at https://itol.embl.de/tree/13711034118313741584046018. The MS data, metadata and methods for Cheetah fecal dataset have been deposited on the GNPS/MassIVE public repository2,32 under the accession number MSV000082969. The parameters used for molecular networking are available on GNPS at https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=093798dffe2448239410c3d465ef9fea.

Code availability

All source code is publicly available under BSD-2-Clause on GitHub at https://github.com/biocore/q2-qemistree. Qemistree is also available as an advanced analysis workflow on GNPS at https://ccms-ucsd.github.io/GNPSDocumentation/qemistree/. All analyses are documented in Jupyter Notebooks available at https://github.com/knightlab-analyses/qemistree-analyses.

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Acknowledgements

P.C.D. was supported by the Gordon and Betty Moore Foundation (grant no. GBMF7622), CCF foundation no. 675191, the US National Institutes of Health (grant nos. U19 AG063744 01, P41 GM103484, R03 CA211211, R01 GM107550, 1 DP1 AT010885, P30 DK120515) and the University of Wisconsin-Madison OVCRGE; L.F.N. was supported by the US National Institutes of Health (grant no. R01 GM107550), and the European Union’s Horizon 2020 program (MSCA-GF, 704786). J.J.J.v.d.H. was supported by an ASDI eScience grant no. ASDI.2017.030, from the Netherlands eScience Center—NLeSC. K.D., M.F., M.L. and S.B. were supported by Deutsche Forschungsgemeinschaft (BO 1910/20). Y.V.B. was funded by the Janssen Human Microbiome Initiative through the Center for Microbiome Innovation at UC San Diego.

Author information

Author notes

  1. These authors contributed equally: Anupriya Tripathi, Yoshiki Vázquez-Baeza.

Authors and Affiliations

  1. Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA

    Anupriya Tripathi

  2. Department of Pediatrics, University of California San Diego, La Jolla, CA, USA

    Anupriya Tripathi, Qiyun Zhu, Daniel McDonald, Antonio Gonzalez & Rob Knight

  3. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA

    Anupriya Tripathi, Julia M. Gauglitz, Mingxun Wang, Mélissa Nothias-Esposito, Deepa D. Acharya, Madeleine Ernst, Asker D. Brejnrod, Louis-Félix Nothias & Pieter C. Dorrestein

  4. Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

    Yoshiki Vázquez-Baeza & Rob Knight

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

    Yoshiki Vázquez-Baeza, Rob Knight & Pieter C. Dorrestein

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

    Julia M. Gauglitz, Madeleine Ernst & Pieter C. Dorrestein

  7. Chair for Bioinformatics, Friedrich-Schiller-University, Jena, Germany

    Kai Dührkop, Markus Fleischauer, Marcus Ludwig & Sebastian Böcker

  8. Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA

    Deepa D. Acharya & Jo Handelsman

  9. Section for Clinical Mass Spectrometry, Department of Congenital Disorders, Danish Center for Neonatal Screening, Statens Serum Institut, Copenhagen, Denmark

    Madeleine Ernst

  10. Bioinformatics Group, Wageningen University, Wageningen, The Netherlands

    Justin J. J. van der Hooft

  11. Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA

    Rob Knight

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Contributions

A.T. and P.C.D. conceived the concept and managed the project. A.T. and Y.V.B. developed the algorithm and wrote the code for Qemistree. A.T. and Y.V.B. contributed equally to the work. L.F.N., R.K. and P.C.D. supervised method implementation. K.D., M.W., J.J.J.v.d.H., M.E., D.M. and A.G. tested and provided suggestions on how to improve the method. M.W. managed the deployment of Qemistree on GNPS. A.T. and M.W. developed the GNPS-Qemistree Dashboard. D.A. and A.T. wrote the documentation for the GNPS-Qemistree workflow. Y.V.B., Q.Z. and A.T. developed Qemistree-iTOL visualization. L.F.N. and M.N.E. performed the MS for the evaluation dataset. A.T., Y.V.B. and L.F.N. analyzed and interpreted the evaluation data. J.M.G. performed MS of the Global FoodOmics samples. A.T. and J.M.G. analyzed and interpreted the Global FoodOmics data. A.D.B. made the comparisons to CSCS. KD, M.F., M.L. and S.B. supported the integration of SIRIUS, ZODIAC and CSI:FingerID. A.T., Y.V.B., R.K. and P.C.D. wrote the manuscript. L.F.N., J.M.G., M.N.E., J.J.J.v.d.H., M.E., K.D., Q.Z., D.M., A.D.B., A.G., J.H., M.F., M.L. and S.B. improved the manuscript.

Corresponding author

Correspondence to Pieter C. Dorrestein.

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Competing interests

M.W. is a founder of Ometa Laboratories LLC. P.C.D. is a scientific advisor for Sirenas, Cybele and Galileo PCD is also a scientific advisor and founde of Enveda ans Ometa Laboratories LLC with approval by University of California San Diego. LLC. K.D., M.L., M.F. and S.B. are founders of Bright Giant GmbH.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 End-to-end Qemistree analysis using GNPS and QIIME2.

Qemistree analysis can be performed using two required input files: 1) A table of molecule (or chemical feature) abundances per sample and 2) an MGF file with MS1 and MS2 ion information. These inputs can be generated by processing mass spectrometry files (.mzXML) through MZmine for feature detection. In Qemistree, these input files are processed through SIRIUS and CSI:FingerID to generate molecular fingerprints and in silico structural annotations (SMILES) per MS feature. We use the predicted molecular fingerprints to generate a phenetic tree of relationships between MS features based on sub-structural similarity. This tree can be visualized in iTOL for further data exploration. If the user inputs a sample metadata file, they can also visualize the abundances of each MS feature stratified by sample grouping of interest. Additionally, the Qemistree queries ClassyFire to classify the structural annotations into chemical ‘kingdom’, ‘superclass’, ‘class’, ‘subclass’ and ‘direct parent’. We further allow the users to input a file with MS/MS spectral library matches (optional) into the workflow such that these library matches (typically, 2-20% of all MS features), instead of in silico annotation, are used for ClassyFire queries whenever available. All the outputs of the Qemistree workflow can be analyzed further using QIIME 2 tools (such as tree-based alpha and beta diversity, mmvec: https://github.com/biocore/mmvec, songbird: https://github.com/biocore/songbird) or explored in Python, R etc. as needed.

Extended Data Fig. 2

2D map of the LC-MS/MS data of the pooled sample for the C18 chromatographic conditions.

Extended Data Fig. 3

2D map of the LC-MS/MS data of the pooled sample for the C18-RTshift chromatographic conditions.

Extended Data Fig. 4 Technical variation in mass-spectrometry due to chromatographic shifts.

Sample (y-axis) by molecule (x-axis) heatmap of 2 fecal samples, tomato seedling samples, and serum samples in the evaluation dataset grouped by chromatography conditions.

Extended Data Fig. 5 Qemistree reduces the differences between biological replicates across mass-spectrometry runs.

A comparison of distances between sample replicates within and across chromatography gradients when using tree-agnostic (Bray-Curtis) distances and tree-based (Weighted UniFrac) distances.

Extended Data Fig. 6 Qemistree mitigates plate-to-plate variation in fecal metabolomics study to highlight a biologically-relevant effect.

a) Principal coordinate analysis (PCoA) of tree-agnostic distances (Bray-Curtis) colored by plate number (pseudo-F = 32.39, p = 0.001). b) PCoA of tree-informed distances (Weighted UniFrac) colored by plate number (pseudo-F = 15.67, p = 0.001). The same PCoA of (c) Bray-Curtis distances (pseudo-F = 33.50, p = 0.001) and (d) Weighted UniFrac distances (pseudo-F = 48.42, p = 0.001) colored by cheetah location which governed the diet of cheetahs. CBC: Cheetah Breeding Center; WD: Wildlife Discoveries.

Extended Data Fig. 7 Chemical taxonomy of food-derived compounds at chemical superclass level.

Chemical hierarchy of compounds (tree tips) detected in simple food products (single ingredient foods, N = 119). Internal nodes are labeled by pie charts of the superclass level taxonomy of children tips. Outer ring shows the relative abundance of each compound across simple animal products, plant products, and other (fungi and algae). The chemical hierarchy iTOL link: https://itol.embl.de/tree/7095134164128581587333337.

Extended Data Fig. 8 Chemical taxonomy of food-derived compounds at chemical class level.

Chemical hierarchy of compounds (tree tips) detected in simple food products (single ingredient foods, N = 119). Internal nodes are labeled by pie charts of the class level taxonomy of children tips. Outer ring shows the relative abundance of each compound across simple animal products, plant products, and other (fungi and algae). The chemical hierarchy iTOL link: https://itol.embl.de/tree/7095134164128581587333337.

Extended Data Fig. 9 Chemical hierarchy of the compounds observed in simple foods and seven complex samples.

a,b,c) 2 meals of orange chicken, a cooked cucumber and the sauce from a meal (schmorgurken), sour cream, blueberry kefir, and egg scramble with chorizo (N = 126 samples). The inner ring shows the relative abundance of each compound across simple animal products, plant products, fungi and algae (other) and complex foods. The absolute abundances of compounds in blueberry kefir (a), scrambled eggs with chorizo (b), and orange chicken (c) (outer bars) are overlaid on the tree to illustrate the shared and unique chemistry of complex foods. We highlight a classifier subtree annotated as benzodioxoles, compounds found in black pepper (in black) that are almost exclusively detected in complex foods. Note that untargeted mass-spectrometry is blind to stereochemistry and oftentimes regiochemistry (for example double bonds in a fatty acid); the structures shown are based on the spectral annotation of the reference library.

Supplementary information

Supplementary Information

Supplementary Tables 1–4.

Reporting Summary

Supplementary Dataset 1

Table of molecular properties (n = 2,936 properties) predicted by Sirius and CSI:FingerID.

Supplementary Dataset 2

Statistical source data for evaluation dataset (Figs. 1 and 2 and Extended Data Figs. 4 and 5).

Supplementary Dataset 3

Statistical source data for simple food samples (Fig. 3 and Extended Data Figs. 7 and 8).

Supplementary Dataset 4

Statistical source data for complex food samples (Fig. 4 and Extended Data Fig. 9).

Supplementary Dataset 5

Statistical source data for cheetah dataset (Extended Data Fig. 6).

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Tripathi, A., Vázquez-Baeza, Y., Gauglitz, J.M. et al. Chemically informed analyses of metabolomics mass spectrometry data with Qemistree. Nat Chem Biol 17, 146–151 (2021). https://doi.org/10.1038/s41589-020-00677-3

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