Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

SpaceM reveals metabolic states of single cells


A growing appreciation of the importance of cellular metabolism and revelations concerning the extent of cell–cell heterogeneity demand 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 per hour, together with a fluorescence-based readout and retention of morpho-spatial features. We validated SpaceM by predicting the cell types of cocultured human epithelial cells and mouse fibroblasts. We used SpaceM to show that stimulating human hepatocytes with fatty acids leads to the emergence of two coexisting subpopulations outlined by distinct cellular metabolic states. Inducing inflammation with the cytokine interleukin-17A perturbs the balance of these states in a process dependent on NF-κB signaling. The metabolic state markers were reproduced in a murine model of nonalcoholic steatohepatitis. We anticipate SpaceM to be broadly applicable for investigations of diverse cellular models and to democratize single-cell metabolomics.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: The SpaceM workflow and validation.
Fig. 2: SpaceM identifies a steatotic subpopulation in lipid stimulated human hepatocytes.
Fig. 3: SpaceM discovers and characterizes metabolic states of stimulated hepatocytes, cross-validated by preclinical NASH models.

Similar content being viewed by others

Data availability

All MALDI-imaging MS data as well as metabolite and lipid annotations and images are publicly available through METASPACE ( The MALDI-imaging MS data and LC–MS/MS datasets are available in the MetaboLights repository under accession number MTBLS78. The microscopy data are available at the European Bioinformatics Institute BioStudies repository under accession number S-BSST369. Source data are provided with this paper.

Code availability

The SpaceM codebase is accessible as Supplementary Software and on GitHub ( The spatio-molecular matrices and the code for their downstream processing, including generation of the main figures, are available on Google Collaboratory (


  1. Wellen, K. E. & Thompson, C. B. A two-way street: reciprocal regulation of metabolism and signalling. Nat. Rev. Mol. Cell Biol. 13, 270–276 (2012).

    Article  CAS  PubMed  Google Scholar 

  2. Kim, J. & DeBerardinis, R. J. Mechanisms and implications of metabolic heterogeneity in cancer. Cell Metab. 30, 434–446 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  3. Johnson, C. H., Ivanisevic, J. & Siuzdak, G. Metabolomics: beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17, 451–459 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  4. Li, H. et al. The landscape of cancer cell line metabolism. Nat. Med. 25, 850–860 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  5. Marioni, J. C. & Arendt, D. How single-cell genomics is changing evolutionary and developmental biology. Annu. Rev. Cell Dev. Biol. 33, 537–553 (2017).

    Article  CAS  PubMed  Google Scholar 

  6. Pelkmans, L. Cell biology. Using cell-to-cell variability–a new era in molecular biology. Science 336, 425–426 (2012).

    Article  CAS  PubMed  Google Scholar 

  7. Lee, M.-C. W. et al. Single-cell analyses of transcriptional heterogeneity during drug tolerance transition in cancer cells by RNA sequencing. Proc. Natl Acad. Sci. USA 111, E4726–E4735 (2014).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  8. Russell, A. B., Trapnell, C. & Bloom, J. D. Extreme heterogeneity of influenza virus infection in single cells. eLife 7, e3230 (2018).

    Article  Google Scholar 

  9. Duncan, K. D., Fyrestam, J. & Lanekoff, I. Advances in mass spectrometry based single-cell metabolomics. Analyst 144, 782–793 (2019).

    Article  CAS  PubMed  Google Scholar 

  10. Rubakhin, S. S., Romanova, E. V., Nemes, P. & Sweedler, J. V. Profiling metabolites and peptides in single cells. Nat. Methods 8, S20–S29 (2011).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  11. Ibáñez, A. J. et al. Mass spectrometry-based metabolomics of single yeast cells. Proc. Natl Acad. Sci. USA 110, 8790–8794 (2013).

    Article  PubMed Central  PubMed  Google Scholar 

  12. Qi, M., Philip, M. C., Yang, N. & Sweedler, J. V. Single cell neurometabolomics. ACS Chem. Neurosci. 9, 40–50 (2018).

    Article  CAS  PubMed  Google Scholar 

  13. Ali, A. et al. Single-cell metabolomics by mass spectrometry: advances, challenges, and future applications. Trends Anal. Chem. 120, 115436 (2019).

    Article  CAS  Google Scholar 

  14. Gilmore, I. S., Heiles, S. & Pieterse, C. L. Metabolic imaging at the single-cell scale: recent advances in mass spectrometry imaging. Annu. Rev. Anal. Chem. 12, 201–224 (2019).

    Article  CAS  Google Scholar 

  15. Lombard-Banek, C. et al. In vivo subcellular mass spectrometry enables proteo-metabolomic single-cell systems biology in a chordate embryo developing to a normally behaving tadpole (X. laevis). Angew. Chem. Int. Ed Engl. (2021).

  16. Belloni, L. et al. Targeting a phospho-STAT3-miRNAs pathway improves vesicular hepatic steatosis in an in vitro and in vivo model. Sci. Rep. 8, 13638 (2018).

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  17. Tanner, N. et al. Regulation of drug metabolism by the interplay of inflammatory signaling, steatosis, and xeno-sensing receptors in HepaRG cells. Drug Metab. Dispos. 46, 326–335 (2018).

    Article  CAS  PubMed  Google Scholar 

  18. Herms, A. et al. Cell-to-cell heterogeneity in lipid droplets suggests a mechanism to reduce lipotoxicity. Curr. Biol. 23, 1489–1496 (2013).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Anstee, Q. M., Reeves, H. L., Kotsiliti, E., Govaere, O. & Heikenwalder, M. From NASH to HCC: current concepts and future challenges. Nat. Rev. Gastroenterol. Hepatol. 16, 411–428 (2019).

    Article  PubMed  Google Scholar 

  20. Malehmir, M. et al. Platelet GPIbα is a mediator and potential interventional target for NASH and subsequent liver cancer. Nat. Med. 25, 641–655 (2019).

    Article  CAS  PubMed  Google Scholar 

  21. Alexandrov, T. Spatial metabolomics and imaging mass spectrometry in the age of artificial intelligence. Annu. Rev. Biomed. Data Sci. 3, 61–87 (2020).

    Article  PubMed Central  PubMed  Google Scholar 

  22. Patterson, N. H., Tuck, M., Van de Plas, R. & Caprioli, R. M. Advanced registration and analysis of MALDI imaging mass spectrometry measurements through autofluorescence microscopy. Anal. Chem. 90, 12395–12403 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Wolf, M. J. et al. Metabolic activation of intrahepatic CD8+ T cells and NKT cells causes nonalcoholic steatohepatitis and liver cancer via cross-talk with hepatocytes. Cancer Cell 26, 549–564 (2014).

    Article  CAS  PubMed  Google Scholar 

  24. Spandl, J., White, D. J., Peychl, J. & Thiele, C. Live cell multicolor imaging of lipid droplets with a new dye, LD540. Traffic 10, 1579–1584 (2009).

    Article  CAS  PubMed  Google Scholar 

  25. Molenaar, M. R. et al. LION/web: a web-based ontology enrichment tool for lipidomic data analysis. Gigascience 8, giz061 (2019).

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  26. Ress, C. & Kaser, S. Mechanisms of intrahepatic triglyceride accumulation. World J. Gastroenterol. 22, 1664–1673 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  27. Gluchowski, N. L., Becuwe, M., Walther, T. C. & Farese, R. V. Jr. Lipid droplets and liver disease: from basic biology to clinical implications. Nat. Rev. Gastroenterol. Hepatol. 14, 343–355 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Baiceanu, A., Mesdom, P., Lagouge, M. & Foufelle, F. Endoplasmic reticulum proteostasis in hepatic steatosis. Nat. Rev. Endocrinol. 12, 710–722 (2016).

    Article  CAS  PubMed  Google Scholar 

  29. Lau, A. N. et al. Dissecting cell-type-specific metabolism in pancreatic ductal adenocarcinoma. eLife (2020).

  30. Rodríguez-Colman, M. J. et al. Interplay between metabolic identities in the intestinal crypt supports stem cell function. Nature 543, 424–427 (2017).

    Article  CAS  PubMed  Google Scholar 

  31. Robinson, J. L. et al. An atlas of human metabolism. Sci. Signal. 13, eaaz1482 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  32. Daemen, A. et al. Metabolite profiling stratifies pancreatic ductal adenocarcinomas into subtypes with distinct sensitivities to metabolic inhibitors. Proc. Natl Acad. Sci. USA 112, E4410–E4417 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  33. Guillaume-Gentil, O. et al. Tunable single-cell extraction for molecular analyses. Cell 166, 506–516 (2016).

    Article  CAS  PubMed  Google Scholar 

  34. Liu, R., Pan, N., Zhu, Y. & Yang, Z. T-Probe: an integrated microscale device for online in situ single cell analysis and metabolic profiling using mass spectrometry. Anal. Chem. 90, 11078–11085 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  35. Cahill, J. F., Kertesz, V. & Van Berkel, G. J. Laser dissection sampling modes for direct mass spectral analysis. Rapid Commun. Mass Spectrom. 30, 611–619 (2016).

    Article  CAS  PubMed  Google Scholar 

  36. Cahill, J. F., Riba, J. & Kertesz, V. Rapid, untargeted chemical profiling of single cells in their native environment. Anal. Chem. 91, 6118–6126 (2019).

    Article  CAS  PubMed  Google Scholar 

  37. Rubakhin, S. S., Lanni, E. J. & Sweedler, J. V. Progress toward single cell metabolomics. Curr. Opin. Biotechnol. 24, 95–104 (2013).

    Article  CAS  PubMed  Google Scholar 

  38. Zenobi, R. Single-cell metabolomics: analytical and biological perspectives. Science 342, 1243259 (2013).

    Article  CAS  PubMed  Google Scholar 

  39. Zhang, L. & Vertes, A. Single-cell mass spectrometry approaches to explore cellular heterogeneity. Angew. Chem. Int. Ed. Engl. 57, 4466–4477 (2018).

    Article  CAS  PubMed  Google Scholar 

  40. Comi, T. J., Neumann, E. K., Do, T. D. & Sweedler, J. V. microMS: a Python platform for image-guided mass spectrometry profiling. J. Am. Soc. Mass. Spectrom. 28, 1919–1928 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  41. Neumann, E. K., Comi, T. J., Rubakhin, S. S. & Sweedler, J. V. Lipid heterogeneity between astrocytes and neurons revealed by single-cell MALDI-MS combined with immunocytochemical classification. Angew. Chem. Int. Ed. Engl. 58, 5910–5914 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  42. Mereu, E. et al. Benchmarking single-cell RNA-sequencing protocols for cell atlas projects. Nat. Biotechnol. 38, 747–755 (2020).

    Article  CAS  PubMed  Google Scholar 

  43. Wang, B., Zhao, L., Fish, M., Logan, C. Y. & Nusse, R. Self-renewing diploid Axin2(+) cells fuel homeostatic renewal of the liver. Nature 524, 180–185 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  44. Aizarani, N. et al. A human liver cell atlas reveals heterogeneity and epithelial progenitors. Nature 572, (2019).

  45. Hall, Z. et al. Lipid zonation and phospholipid remodeling in nonalcoholic fatty liver disease. Hepatology 65, 1165–1180 (2017).

    Article  CAS  PubMed  Google Scholar 

  46. Thiam, A. R. & Beller, M. The why, when and how of lipid droplet diversity. J. Cell Sci. 130, 315–324 (2017).

    CAS  PubMed  Google Scholar 

  47. Araya, J. et al. Increase in long-chain polyunsaturated fatty acid n − 6/n − 3 ratio in relation to hepatic steatosis in patients with non-alcoholic fatty liver disease. Clin. Sci. 106, 635–643 (2004).

    Article  CAS  Google Scholar 

  48. Sanders, F. W. B. et al. Hepatic steatosis risk is partly driven by increased de novo lipogenesis following carbohydrate consumption. Genome Biol. 19, 79 (2018).

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  49. Saito, K. et al. Characterization of hepatic lipid profiles in a mouse model with nonalcoholic steatohepatitis and subsequent fibrosis. Sci. Rep. 5, 12466 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  50. Apostolopoulou, M. et al. Specific hepatic sphingolipids relate to insulin resistance, oxidative stress, and inflammation in nonalcoholic steatohepatitis. Diabetes Care 41, 1235–1243 (2018).

    Article  CAS  PubMed  Google Scholar 

  51. Gripon, P. et al. Infection of a human hepatoma cell line by hepatitis B virus. Proc. Natl Acad. Sci. USA 99, 15655–15660 (2002).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  52. Preibisch, S., Saalfeld, S. & Tomancak, P. Globally optimal stitching of tiled 3D microscopic image acquisitions. Bioinformatics 25, 1463–1465 (2009).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  53. Palmer, A. et al. FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry. Nat. Methods 14, 57–60 (2017).

    Article  CAS  PubMed  Google Scholar 

  54. Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  55. Ovchinnikova, K., Kovalev, V., Stuart, L. & Alexandrov, T. OffsampleAI: artificial intelligence approach to recognize off-sample mass spectrometry images. BMC Bioinf. 21, 129 (2020).

    Article  CAS  Google Scholar 

  56. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

Download references


We thank C.B. Vibe for her support and feedback on the manuscript, M. Shahraz, M. Ekelhof and A. Palmer for help with MALDI-imaging, A. Eisenbarth for software development, N. Typas for advising on biology and providing access to a microscope, B. El Debs and J. Selkrig for training on microscopy and cell culturing, METASPACE software development team (all EMBL), M. Stanifer and S. Boulant (DKFZ) for training on cell culturing, A. Andersen (Life Science Editors) and T. O’Connor (Helmholtz Center Munich) for scientific editing, S. Seah and C. Merten (EMBL) for providing NIH3T3-GFP, F. Merkel and C. Häring (EMBL) for providing HeLa Kyoto H2B-mCherry. We thank other members of the Thesis Advisory Committee of L.R., A.-C. Gavin (EMBL) and B. Brügger (Heidelberg University). This work was funded in part by the European Union’s Horizon 2020 program under agreement numbers 634402, 777222 (T.A.) and 667273 (M.H.), the DKFZ-MOST cooperation program (M.H., M.S.), Darwin Trust of Edinburgh (S.T.), SFB Transregio grant nos. 179, 209, 1335 and I&I Helmholtz Zukunftsthema (all M.H.), the ERC Consolidator grants HepatoMetaboPath (M.H.) and METACELL (T.A.) and ERC Proof of Concept Faith (M.H.). We thank all the reviewers and the editor for detailed feedback that helped improve the paper.

Author information

Authors and Affiliations



L.R. and T.A. conceived the method. L.R. developed the method. S.T. conceived and performed the coculture experiments. K.O. performed the on-sample analysis for hepatocytes. R.M.G. and P.P. performed LC–MS/MS validation. M.S. and M.H conceived the hepatocytes study. M.S. cultured and prepared hepatocytes. L.R. and T.A. conceived and performed data analyses. L.R., M.S., M.H. and T.A. interpreted data. L.R., M.S., M.H. and T.A. wrote the paper. T.A. supervised and coordinated the work.

Corresponding authors

Correspondence to Mathias Heikenwalder or Theodore Alexandrov.

Ethics declarations

Competing interests

L.R. and T.A. are the inventors on a patent application describing a spatial single-cell metabolomics method (application in the EU EP3610267A1, US US20200057049A1, Canada CA3059818A1, Australia AU2018252185A1, World Intellectual Property Organization (Patent Cooperation Treaty) WO2018189365A1).

Additional information

Peer review information Nature Methods thanks Young Jin Lee, Peter Nemes and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Procedure of detection of laser ablation marks in post-MALDI microscopy images.

The post-MALDI microscopy image is manually cropped around the baltion marks. The Fourier Transform is computed, followed by a low pass filter. The inverse Fourier Transform generates a denoised image with features having spatial frequencies associated with the ablation marks becoming more pronounced. This enables a robust histogram-based thresholding to segment individual ablation marks and determine their centroid coordinates. Every centroid is used as a seed for a region growing algorithm to determine the edges of each ablation mark.

Extended Data Fig. 2 Registration workflow of pre- and post-MALDI microscopy images.

Pen marks are drawn with a black sharpie on the glass slide at the edges of the culturing area. These black pen marks are visible in both the pre- and post-MALDI microscopy images. Histogram-based thresholding is applied to both microscopy images to extract the penmarks areas followed by an edge detection that detects pixels on the edge of the pen marks. This generates more than 400.000 individual features for each microscopy image. A random subset of 5000 features from both pre- (in blue) and post-MALDI (in red) images are used as fiducials to estimate the coordinate transformation for image registration. The overlap of both pre- and post-MALDI fiducials is illustrated. The estimated coordinate transformation from post- to pre-MALDI is applied to the ablation mark coordinates (shown in green) in order to estimate their position in the pre-MALDI microscopy image.

Extended Data Fig. 3 Procedure of the indexation of the segmented ablation marks.

The indexation of the segmented ablation marks involved fitting a theoretical rectangular grid to the ablation marks. In A, the grid angle is estimated by minimizing the number of non-overlapping ablation mark coordinates after projection of the X axis for different rotation angles. In B, the center of the grid is estimated from the extrema of the ablation mark coordinates. The spacing of the grid nodes is estimated in C by minimizing the mean distance for each grid note to the nearest ablation mark. The re-indexing in D is done by choosing the closest ablation mark coordinates from the grid nodes constructed using the parameters defined before (the grid nodes are shown in red, their nearest ablation mark coordinates are shown in black). In E, the ablation mark coordinates are color-coded by their index. An illustration of the different steps for fitting a grid onto the ablation mark coordinates as well as the re-indexing is shown in F. In G, the re-indexed ablation marks are shown.

Extended Data Fig. 4 Details of the SpaceM processing.

The SpaceM processing is composed of two parts: filtering ablation marks and normalizing metabolite intensities across single cells. First, for each cell, its touching ablation marks are filtered based on their area, their sampling proportions (proportion of the ablation mark area sampling any cell) and their sampling specificity (the proportion of their sampling area shared with the cell of interest). Ablation marks sampling predominantly extracellular areas or too many cells at the same time are filtered out (as illustrated here for the ablation mark II and III). Second, for a given metabolite, its intensity in a cell is calculated as a weighted mean of the metabolite intensities from the filtered ablation marks sampling that cell. The intensities are divided by the sampling proportion to account for differences in amount of sampled cellular material between ablation marks. To increase the contribution of ablation marks which sample the cell of interest more than other ablation marks, their intensities are weighted by the sampling specificity. ‘area(a)’ stands for the area of an ablation mark a; ‘sampling area(a)’ stands for the intracellular area of ablation mark ‘a’; ‘area(c)’ stands for the area of a cell ‘c’; all areas are computed in microscopy pixels.

Supplementary information

Source data

Source Data Fig. 1

Statistical source data for Fig. 1.

Source Data Fig. 2

Statistical source data for Fig. 2.

Source Data Fig. 3

Statistical source data for Fig. 3.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rappez, L., Stadler, M., Triana, S. et al. SpaceM reveals metabolic states of single cells. Nat Methods 18, 799–805 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research