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.
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.