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3D molecular cartography using LC–MS facilitated by Optimus and 'ili software

Nature Protocols volume 13, pages 134–154 (2018)Cite this article

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Abstract

Our skin, our belongings, the world surrounding us, and the environment we live in are covered with molecular traces. Detecting and characterizing these molecular traces is necessary to understand the environmental impact on human health and disease, and to decipher complex molecular interactions between humans and other species, particularly microbiota. We recently introduced 3D molecular cartography for mapping small organic molecules (including metabolites, lipids, and environmental molecules) found on various surfaces, including the human body. Here, we provide a protocol and open-source software for 3D molecular cartography. The protocol includes step-by-step procedures for sample collection and processing, liquid chromatography–mass spectrometry (LC–MS)-based metabolomics, quality control (QC), molecular identification using MS/MS, data processing, and visualization with 3D models of the sampled environment. The LC–MS method was optimized for a broad range of small organic molecules. We enable scientists to reproduce our previously obtained results, and illustrate the broad utility of our approach with molecular maps of a rosemary plant and an ATM keypad after a PIN code was entered. To promote reproducibility, we introduce cartographical snapshots: files that describe a particular map and visualization settings, and that can be shared and loaded to reproduce the visualization. The protocol enables molecular cartography to be performed in any mass spectrometry laboratory and, in principle, for any spatially mapped data. We anticipate applications, in particular, in medicine, ecology, agriculture, biotechnology, and forensics. The protocol takes 78 h for a molecular map of 100 spots, excluding the reagent setup.

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Figure 1
Figure 2: Illustrations of the molecular cartography.

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Acknowledgements

This project received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 634402 (to T.A. and I.P.), from Marie Skłodowska-Curie Action grants MSCA-IF-2016, 3D-Plant2Cells, and ID 704786 (to L.-F.N.), from US National Institutes of Health (NIH) grant 5P41GM103484-07, from National Institute of Justice Award 2015-DN-BX-K047 (to A.B.), and from NIH grant GMS10RR029121. P.C.D. further thanks the Alfred P. Sloan Foundation program on the Microbiology of the Built Environment for its support. We thank the EMBL Metabolomics Core Facility for LC–MS experiments, and Bruker Daltonics for the shared instrumentation infrastructure that enabled this work. We thank M. Rurik (OpenMS development team) and A. Fillbrunn from the KNIME development team for their advice, and V. Kovalev (EMBL) for helping with data analysis. We thank C. Kapono and D. Petras (UCSD) for helpful discussions.

Author information

Author notes

  1. Ivan Protsyuk, Alexey V Melnik and Louis-Felix Nothias: These authors contributed equally to this work.

Authors and Affiliations

  1. Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany

    Ivan Protsyuk, Luca Rappez, Sergey Ryazanov & Theodore Alexandrov

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

    Alexey V Melnik, Louis-Felix Nothias, Alexander A Aksenov, Amina Bouslimani, Pieter C Dorrestein & Theodore Alexandrov

  3. Metabolomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany

    Prasad Phapale & Theodore Alexandrov

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

    Pieter C Dorrestein

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

    Pieter C Dorrestein

  6. Center for Computational Mass Spectrometry, University of California San Diego, La Jolla, California, USA

    Pieter C Dorrestein

Authors
  1. Ivan Protsyuk
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  6. Alexander A Aksenov
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  9. Pieter C Dorrestein
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Contributions

I.P. and S.R. developed the software. T.A. created prototype software and coordinated computational research and software development. I.P., A.V.M., L.-F.N., L.R., and A.B. analyzed the data. A.V.M., L.-F.N., A.A.A., and P.P. performed mass spectrometry experiments. L.-F.N. and A.A.A. contributed to the rosemary study. I.P., L.R., P.P., and T.A. contributed to the ATM keypad study. I.P., A.V.M., L.-F.N., P.C.D., and T.A. wrote the manuscript; all authors contributed to the writing. P.C.D. and T.A. coordinated the research, development, and experiments.

Corresponding authors

Correspondence to Pieter C Dorrestein or Theodore Alexandrov.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Results for the lifestyle chemistries from phones study.

Visualization of nystatin and unannotated metabolites across samples from the published study on lifestyle chemistries from phones3. The set of samples represent one 96-well plate from the study, containing blank samples, row A, and samples from 7 volunteers, rows B-H. See cartographical snapshots in Supplementary Data 7.1-7.4.

Supplementary Figure 2 Examples of molecular maps of unannotated molecules in tissues of Rosmarinus officinalis plant with predominant occurrence in the flowers.

These examples were chosen to highlight compounds that change in abundance in different parts of the plant as they mature. These results may lead to discovery of novel natural products. See cartographical snapshots in Supplementary Data 7.5-7.7.

Supplementary Figure 3 Examples of molecular maps of unannotated molecules in tissues of Rosmarinus officinalis plant with predominant occurrence the foliage.

See cartographical snapshots in Supplementary Data 7.8-7.10.

Supplementary Figure 4 Examples of distributions of unannotated metabolites in tissues of Rosmarinus officinalis plant with predominant occurrence the stem.

See cartographical snapshots in Supplementary Data 7.11-7.13.

Supplementary Figure 5 Examples of distribution corresponding to the pressed keys on the keypad for annotated and unannotated metabolites.

See cartographical snapshots in Supplementary Data 7.14-7.22.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Table 1, and Supplementary Methods. (PDF 1434 kb)

Supplementary Data 1

Example Optimus input files. (ZIP 97 kb)

Supplementary Data 2

Human built environment. (ZIP 21036 kb)

Supplementary Data 3

Human skin. (ZIP 4919 kb)

Supplementary Data 4

Lifestyle chemistries from phones. (ZIP 2093 kb)

Supplementary Data 5

Rosemary plant. (ZIP 826 kb)

Supplementary Data 6

ATM keypad. (ZIP 33040 kb)

Supplementary Data 7

URL links to cartographical snapshots for the considered studies. (PDF 118 kb)

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Protsyuk, I., Melnik, A., Nothias, LF. et al. 3D molecular cartography using LC–MS facilitated by Optimus and 'ili software. Nat Protoc 13, 134–154 (2018). https://doi.org/10.1038/nprot.2017.122

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