SpaceM reveals metabolic states of single cells

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-?B 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.