The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry techniques are well-suited to high-throughput characterization of natural products, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social molecular networking (GNPS, http://gnps.ucsd.edu), an open-access knowledge base for community wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of ‘living data’ through continuous reanalysis of deposited data.

High-mass-resolution imaging mass spectrometry promises to localize hundreds of metabolites in tissues, cell cultures, and agar plates with cellular resolution, but it is hampered by the lack of bioinformatics tools for automated metabolite identification. We report pySM, a framework for false discovery rate (FDR)-controlled metabolite annotation at the level of the molecular sum formula, for high-mass-resolution imaging mass spectrometry (https://github.com/alexandrovteam/pySM). We introduce a metabolite-signal match score and a target-decoy FDR estimate for spatial metabolomics.

A growing appreciation of the importance of cellular metabolism together with recent revelations concerning the extent of cell-cell heterogeneity demand performing metabolic characterization of individual cells. We present SpaceM, an open-source method for in situ single-cell metabolomics that detects >100 metabolites from >1,000 individual cells/hour together with a fluorescence based read-out and morpho-spatial features. We validated SpaceM by predicting the cell types of co-cultured human epithelial cells and mouse fibroblasts. We used SpaceM to show that stimulating human hepatocytes with fatty acids led to the emergence of two co-existing subpopulations outlined by distinct cellular metabolic states. Inducing inflammation with the cytokine IL-17A perturbs the balance of these states in a process dependent on NF-κB signalling. The metabolic-state markers were reproduced in a pre-clinical in vivo murine model of non-alcoholic steatohepatitis. We anticipate SpaceM to be broadly applicable for investigations of diverse cellular models and to democratize single-cell metabolomics.

Bacteria in the gut can modulate the availability and efficacy of therapeutic drugs. Yet, the systematic mapping of the respective interactions has only started recently 1 and the main underlying mechanism proposed is chemical transformation of drugs by microbes (biotransformation). Here, we investigated the depletion of 15 structurally diverse drugs by 25 representative gut bacterial strains. This revealed 70 bacteria-drug interactions, 29 of which had not been reported before. Over half of the new interactions can be ascribed to bioaccumulation, that is bacteria storing the drug intracellularly without chemically modifying it, and in most cases without their growth being affected. As a case in point, we studied the molecular basis of bioaccumulation of the widely used antidepressant duloxetine by using clickchemistry, thermal proteome profiling and metabolomics. We find that duloxetine binds to several metabolic enzymes and changes metabolite secretion of the respective bacteria. When tested in a defined microbial community of accumulators and non-accumulators, duloxetine markedly altered the community composition through metabolic cross-feeding. We further validated our findings in an animal model, showing that bioaccumulating bacteria attenuate the behavioral response of Caenorhabditis elegans to duloxetine. Taken together, bioaccumulation by gut bacteria may be a common mechanism that alters drug availability and bacterial metabolism, with implications for microbiota composition, pharmacokinetics, side effects and drug responses, likely in an individual manner.Therapeutic drugs can have a strong impact on the gut microbiome and vice versa 2-5 . The underlying drug-bacteria interactions can reduce microbial fitness 6 or alter the drug availability through biotransformation 7-14 . The latter can have either a positive or a negative impact on drug activity and efficacy. While drugs like lovastatin and sulfasalazine are chemically transformed by gut bacteria into their active forms, bacterial metabolism can inactivate drugs such as digoxin 15,16 , or cause toxic effects as in the case of irinotecan 17 .Furthering the diversity of susceptible drugs, over one hundred molecules were recently reported to be chemically modified by gut bacteria 1 . Yet, the mechanistic view on these interactions is largely confined to drug biotransformation 12,13 . Drug accumulation without metabolizationTo expand the knowledge of bacterial effect on drug availability, we systematically profiled interactions between 15 human-targeted drugs and 25 representative human gut bacterial strains (21 species; with additional subspecies or conspecific strains of Bifidobacterium longum, Escherichia coli and Bacteroides uniformis) (Supplementary Table 1). The bacterial species were selected to cover a broad phylogenetic and metabolic diversity representative of the healthy microbiota 18 (Extended Data Fig. 1a, Supplementary Table 1). On the drug side, 12 orally administered small molecule drugs (MW<500 Da), amenable to UPLC-UV-based quantificat...

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.