Protocol | Published:

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

Nature Protocols volume 13, pages 134154 (2018) | Download Citation

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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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

    • Ivan Protsyuk
    • , Alexey V Melnik
    •  & Louis-Felix Nothias

    These authors contributed equally to this work.

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

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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.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Pieter C Dorrestein or Theodore Alexandrov.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–5, Supplementary Table 1, and Supplementary Methods.

  2. 2.

    Supplementary Data 7

    URL links to cartographical snapshots for the considered studies.

Zip files

  1. 1.

    Supplementary Data 1

    Example Optimus input files.

  2. 2.

    Supplementary Data 2

    Human built environment.

  3. 3.

    Supplementary Data 3

    Human skin.

  4. 4.

    Supplementary Data 4

    Lifestyle chemistries from phones.

  5. 5.

    Supplementary Data 5

    Rosemary plant.

  6. 6.

    Supplementary Data 6

    ATM keypad.

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DOI

https://doi.org/10.1038/nprot.2017.122

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