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Single-cell metabolic profiling of human cytotoxic T cells


Cellular metabolism regulates immune cell activation, differentiation and effector functions, but current metabolic approaches lack single-cell resolution and simultaneous characterization of cellular phenotype. In this study, we developed an approach to characterize the metabolic regulome of single cells together with their phenotypic identity. The method, termed single-cell metabolic regulome profiling (scMEP), quantifies proteins that regulate metabolic pathway activity using high-dimensional antibody-based technologies. We employed mass cytometry (cytometry by time of flight, CyTOF) to benchmark scMEP against bulk metabolic assays by reconstructing the metabolic remodeling of in vitro-activated naive and memory CD8+ T cells. We applied the approach to clinical samples and identified tissue-restricted, metabolically repressed cytotoxic T cells in human colorectal carcinoma. Combining our method with multiplexed ion beam imaging by time of flight (MIBI-TOF), we uncovered the spatial organization of metabolic programs in human tissues, which indicated exclusion of metabolically repressed immune cells from the tumor–immune boundary. Overall, our approach enables robust approximation of metabolic and functional states in individual cells.

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Fig. 1: Single-cell metabolic regulomes organize the human immune system.
Fig. 2: Single-cell metabolic regulome profiles of T cell activation dynamics.
Fig. 3: Integrative modeling of metabolic rewiring reveals determinants of human T cell activation.
Fig. 4: Cytotoxic T cell metabolic states reflect tissue of residence.
Fig. 5: Imaging-based scMEP analysis reveals spatial influences on the organization of metabolic features.
Fig. 6: Metabolic polarization at the tumor–immune boundary in human colorectal carcinoma.

Data availability

Single-cell mass cytometry data sets for metabolic analysis of human whole blood populations, in vitro T cell activation and analysis of metabolic states in human tissues as well as MIBI-TOF imaging data of colorectal carcinoma and healthy colon are publicly available at

Code availability

All custom R scripts associated with this manuscript will be made available upon reasonable request.


  1. 1.

    Klein Geltink, R. I., Kyle, R. L. & Pearce, E. L. Unraveling the complex interplay between T cell metabolism and function. Annu. Rev. Immunol. 36, 461–488 (2018).

    PubMed Central  Google Scholar 

  2. 2.

    Olenchock, B. A., Rathmell, J. C. & Vander Heiden, M. G. Biochemical underpinnings of immune cell metabolic phenotypes. Immunity 46, 703–713 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Buck, M. D., Sowell, R. T., Kaech, S. M. & Pearce, E. L. Metabolic instruction of immunity. Cell 169, 570–586 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Xu, T. et al. Metabolic control of TH17 and induced Treg cell balance by an epigenetic mechanism. Nature 548, 228–233 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Patel, C. H., Leone, R. D., Horton, M. R. & Powell, J. D. Targeting metabolism to regulate immune responses in autoimmunity and cancer. Nat. Rev. Drug Discov. 18, 669–688 (2019).

    CAS  PubMed  Google Scholar 

  6. 6.

    Scharping, N. E. et al. The tumor microenvironment represses T cell mitochondrial biogenesis to drive intratumoral T cell metabolic insufficiency and dysfunction. Immunity 45, 374–388 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Kishton, R. J., Sukumar, M. & Restifo, N. P. Metabolic regulation of T cell longevity and function in tumor immunotherapy. Cell Metab. 26, 94–109 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Leone, R. D. et al. Glutamine blockade induces divergent metabolic programs to overcome tumor immune evasion. Science 366, 1013–1021 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Angiari, S. et al. Pharmacological activation of pyruvate kinase M2 Inhibits CD4+ T cell pathogenicity and suppresses autoimmunity. Cell Metab. 31, 391–405 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Kornberg, M. D. et al. Dimethyl fumarate targets GAPDH and aerobic glycolysis to modulate immunity. Science 360, 449–453 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Lee, C.-F. et al. Preventing allograft rejection by targeting immune metabolism. Cell Rep. 13, 760–770 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Dettmer, K., Aronov, P. A. & Hammock, B. D. Mass spectrometry-based metabolomics. Mass Spectrom. Rev. 26, 51–78 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Buescher, J. M. et al. A roadmap for interpreting 13C metabolite labeling patterns from cells. Curr. Opin. Biotechnol. 34, 189–201 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Verbist, K. C. et al. Metabolic maintenance of cell asymmetry following division in activated T lymphocytes. Nature 532, 389–393 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Ma, E. H. et al. Metabolic profiling using stable isotope tracing reveals distinct patterns of glucose utilization by physiologically activated CD8+ T cells. Immunity 51, 856–870 (2019).

    CAS  PubMed  Google Scholar 

  16. 16.

    O’Sullivan, D., Sanin, D. E., Pearce, E. J. & Pearce, E. L. Metabolic interventions in the immune response to cancer. Nat. Rev. Immunol. 19, 324–335 (2019).

    PubMed  Google Scholar 

  17. 17.

    Li, X. et al. Navigating metabolic pathways to enhance antitumour immunity and immunotherapy. Nat. Rev. Clin. Oncol. 16, 425–441 (2019).

    CAS  PubMed  Google Scholar 

  18. 18.

    Muir, A. & Vander Heiden, M. G. The nutrient environment affects therapy. Science 360, 962–963 (2018).

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Le Bourgeois, T. et al. Targeting T cell metabolism for improvement of cancer immunotherapy. Front. Oncol. 8, 237 (2018).

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Keren, L. et al. MIBI-TOF: a multiplexed imaging platform relates cellular phenotypes and tissue structure. Sci. Adv. 5, eaax5851 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Hartmann, F. J. & Bendall, S. C. Immune monitoring using mass cytometry and related high-dimensional imaging approaches. Nat. Rev. Rheumatol. 16, 87–99 (2020).

    PubMed  Google Scholar 

  23. 23.

    Saas, P., Varin, A., Perruche, S. & Ceroi, A. Recent insights into the implications of metabolism in plasmacytoid dendritic cell innate functions: potential ways to control these functions. F1000Res. 6, 456 (2017).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Rakus, D., Gizak, A., Deshmukh, A. & Wiśniewski, J. R. Absolute quantitative profiling of the key metabolic pathways in slow and fast skeletal muscle. J. Proteome Res. 14, 1400–1411 (2015).

    CAS  PubMed  Google Scholar 

  25. 25.

    Geiger, R. et al. L-arginine modulates T cell metabolism and enhances survival and anti-tumor activity. Cell 167, 829–842 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Howden, A. J. M. et al. Quantitative analysis of T cell proteomes and environmental sensors during T cell differentiation. Nat. Immunol. 20, 1542–1554 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Tanner, L. B. et al. Four key steps control glycolytic flux in mammalian cells. Cell Syst. 7, 49–62 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Wang, R. et al. The transcription factor Myc controls metabolic reprogramming upon T lymphocyte activation. Immunity 35, 871–882 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Finlay, D. K. et al. PDK1 regulation of mTOR and hypoxia-inducible factor 1 integrate metabolism and migration of CD8+ T cells. J. Exp. Med. 209, 2441–2453 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Frauwirth, K. A. et al. The CD28 signaling pathway regulates glucose metabolism. Immunity 16, 769–777 (2002).

    CAS  PubMed  Google Scholar 

  31. 31.

    Slack, M., Wang, T. & Wang, R. T cell metabolic reprogramming and plasticity. Mol. Immunol. 68, 507–512 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Wang, R. & Green, D. R. Metabolic checkpoints in activated T cells. Nat. Immunol. 13, 907–915 (2012).

    CAS  PubMed  Google Scholar 

  33. 33.

    Zaslaver, A. et al. Just-in-time transcription program in metabolic pathways. Nat. Genet. 36, 486–491 (2004).

    CAS  PubMed  Google Scholar 

  34. 34.

    Good, Z. et al. Proliferation tracing with single-cell mass cytometry optimizes generation of stem cell memory-like T cells. Nat. Biotechnol. 37, 259–266 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Altman, B. J., Stine, Z. E. & Dang, C. V. From Krebs to clinic: glutamine metabolism to cancer therapy. Nat. Rev. Cancer 16, 619–634 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Fischer, M. et al. Early effector maturation of naïve human CD8+ T cells requires mitochondrial biogenesis. Eur. J. Immunol. 48, 1632–1643 (2018).

    CAS  PubMed  Google Scholar 

  37. 37.

    Icard, P., Fournel, L., Wu, Z., Alifano, M. & Lincet, H. Interconnection between metabolism and cell cycle in cancer. Trends Biochem. Sci. 44, 490–501 (2019).

    CAS  PubMed  Google Scholar 

  38. 38.

    Bengsch, B. et al. Bioenergetic insufficiencies due to metabolic alterations regulated by the inhibitory receptor PD-1 are an early driver of CD8+ T cell exhaustion T cell exhaustion. Immunity 45, 358–373 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Blank, C. U. et al. Defining ‘T cell exhaustion’. Nat. Rev. Immunol. 19, 665–674 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Son, N.-H. et al. Endothelial cell CD36 optimizes tissue fatty acid uptake. J. Clin. Invest. 128, 4329–4342 (2018).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Chang, C.-H. et al. Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell 162, 1229–1241 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Kaira, K. et al. Prognostic significance of l-type amino acid transporter 1 (LAT1) and 4F2 heavy chain (CD98) expression in stage I pulmonary adenocarcinoma. Lung Cancer 66, 120–126 (2009).

    PubMed  Google Scholar 

  44. 44.

    Shimizu, K. et al. ASC amino-acid transporter 2 (ASCT2) as a novel prognostic marker in non-small cell lung cancer. Br. J. Cancer 110, 2030–2039 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Canale, F. P. et al. CD39 expression defines cell exhaustion in tumor-infiltrating CD8+ T cells. Cancer Res. 78, 115–128 (2018).

    CAS  PubMed  Google Scholar 

  46. 46.

    Raczkowski, F. et al. CD39 is upregulated during activation of mouse and human T cells and attenuates the immune response to Listeria monocytogenes. PLoS ONE 13, e0197151 (2018).

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Simon, S. & Labarriere, N. PD-1 expression on tumor-specific T cells: friend or foe for immunotherapy? Oncoimmunology 7, e1364828 (2018).

    Google Scholar 

  48. 48.

    Heath, J. R., Ribas, A. & Mischel, P. S. Single-cell analysis tools for drug discovery and development. Nat. Rev. Drug Discov. 15, 204–216 (2016).

    CAS  PubMed  Google Scholar 

  49. 49.

    Linnarsson, S. & Teichmann, S. A. Single-cell genomics: coming of age. Genome Biol. 17, 97 (2016).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Papalexi, E. & Satija, R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18, 35–45 (2018).

    CAS  PubMed  Google Scholar 

  51. 51.

    Schwartzman, O. & Tanay, A. Single-cell epigenomics: techniques and emerging applications. Nat. Rev. Genet. 16, 716–726 (2015).

    CAS  PubMed  Google Scholar 

  52. 52.

    Xiao, Z., Dai, Z. & Locasale, J. W. Metabolic landscape of the tumor microenvironment at single cell resolution. Nat. Commun. 10, 3763 (2019).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Shahi, P., Kim, S. C., Haliburton, J. R., Gartner, Z. J. & Abate, A. R. Abseq: ultrahigh-throughput single cell protein profiling with droplet microfluidic barcoding. Sci. Rep. 7, 44447 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Mair, F. et al. A targeted multi-omic analysis approach measures protein expression and low-abundance transcripts on the single-cell level. Cell Rep. 31, 107499 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Sadelain, M. Chimeric antigen receptors: a paradigm shift in immunotherapy. Annu. Rev. Cancer Biol. 1, 447–466 (2017).

    Google Scholar 

  57. 57.

    Rosenberg, S. A. & Restifo, N. P. Adoptive cell transfer as personalized immunotherapy for human cancer. Science 348, 62–68 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Van den Bossche, J. et al. Mitochondrial dysfunction prevents repolarization of inflammatory macrophages. Cell Rep. 17, 684–696 (2016).

    PubMed  Google Scholar 

  59. 59.

    Hartmann, F. J. et al. Scalable conjugation and characterization of immunoglobulins with stable mass isotope reporters for single-cell mass cytometry analysis. Methods Mol. Biol. 1989, 55–81 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Mei, H. E., Leipold, M. D. & Maecker, H. T. Platinum-conjugated antibodies for application in mass cytometry. Cytometry A 89, 292–300 (2016).

    CAS  PubMed  Google Scholar 

  61. 61.

    Hartmann, F. J., Simonds, E. F. & Bendall, S. C. A universal live cell barcoding-platform for multiplexed human single cell analysis. Sci. Rep. 8, 10770 (2018).

    PubMed  PubMed Central  Google Scholar 

  62. 62.

    Zunder, E. R. et al. Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat. Protoc. 10, 316–333 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Hartmann, F. J. et al. Comprehensive immune monitoring of clinical trials to advance human immunotherapy. Cell Rep. 28, 819–831 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Uhlen, M. et al. Tissue-based map of the human proteome. Science 347, 1260419–1260419 (2015).

    PubMed  Google Scholar 

  65. 65.

    van der Windt, G. J. W., Chang, C.-H. & Pearce, E. L. Measuring bioenergetics in T cells using a Seahorse Extracellular Flux Analyzer. Curr. Protoc. Immunol. 113, 3.16B.1–3.16B.14 (2016).

    Google Scholar 

  66. 66.

    Van den Bossche, J., Baardman, J. & de Winther, M. P. J. Metabolic characterization of polarized M1 and M2 bone marrow-derived macrophages using real-time extracellular flux analysis. J. Vis. Exp. 105, e53424 (2015).

    Google Scholar 

  67. 67.

    Mookerjee, S. A., Gerencser, A. A., Nicholls, D. G. & Brand, M. D. Quantifying intracellular rates of glycolytic and oxidative ATP production and consumption using extracellular flux measurements. J. Biol. Chem. 292, 7189–7207 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Kotecha, N., Krutzik, P. O. & Irish, J. M. Web-based analysis and publication of flow cytometry experiments. Curr. Protoc. Cytom. 53, 10.17.1–10.17.24 (2010).

    Google Scholar 

  69. 69.

    Van Gassen, S. et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87, 636–645 (2015).

    PubMed  Google Scholar 

  70. 70.

    Diggins, K. E., Greenplate, A. R., Leelatian, N., Wogsland, C. E. & Irish, J. M. Characterizing cell subsets using marker enrichment modeling. Nat. Methods 14, 275–278 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Cannoodt, R. et al. SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development. Preprint at (2016).

  72. 72.

    Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

    PubMed  PubMed Central  Google Scholar 

  73. 73.

    Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Behbehani, G. K., Bendall, S. C., Clutter, M. R., Fantl, W. J. & Nolan, G. P. Single-cell mass cytometry adapted to measurements of the cell cycle. Cytometry A 81, 552–566 (2012).

    PubMed  PubMed Central  Google Scholar 

  75. 75.

    Chevrier, S. et al. Compensation of signal spillover in suspension and imaging mass cytometry. Cell Syst. 6, 612–620 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    PubMed  PubMed Central  Google Scholar 

  77. 77.

    Bannon, D. et al. Dynamic allocation of computational resources for deep learning-enabled cellular image analysis with Kubernetes. Preprint at (2019).

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We thank L. Keren for insightful discussions and invaluable feedback as well as the Nakamura lab at the Gladstone Institutes for access to their Seahorse XF analyzer. Further, we thank A. Tsai for advice and help with clinical samples. This study was supported by an EMBO Long-Term Fellowship ALTF 1141–2017 (to F.J.H.), the Novartis Foundation for Medical-Biological Research 16C148 (to F.J.H.) and the Swiss National Science Foundation SNF Early Postdoc Mobility P2ZHP3-171741 (to F.J.H.). In addition, we received support from National Institutes of Health 1DP2OD022550-01 (to S.C.B.), 1R01AG056287-01 (to S.C.B.), 1R01AG057915-01 (to S.C.B.) and 1U24CA224309-01 (to S.C.B.).

Author information




F.J.H. conceived and conceptualized the study, performed the mass cytometry and imaging experiments, validated reagents, analyzed mass cytometry and imaging data, acquired funding and wrote the manuscript. D.M. helped with imaging experiments. E.M. performed the spatial enrichment analysis. D.R.G. and R.B. helped to analyze mass cytometry data. N.F.G. performed cell segmentation. A. Bharadwaj and Z.K. performed IHC stainings and analysis to validate antibodies. S.G.S.V. and J.V.d.B. performed human macrophage polarizations and their extracellular flux analysis. A. Baranski helped to analyze imaging data. W.G. and D.V.V. wrote software for cell segmentation. M.A. provided resources and helped with all aspects of imaging acquisition and analysis. S.C.B. conceptualized and supervised the study, provided resources, acquired funding and wrote the manuscript.

Corresponding author

Correspondence to Sean C. Bendall.

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Supplementary Information

Supplementary Figs. 1–13

Reporting Summary

Supplementary Table 1

Antibody clones evaluated for this study and antibody panels used in each figure

Supplementary Table 2

Basic information on all human samples used in this study

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Hartmann, F.J., Mrdjen, D., McCaffrey, E. et al. Single-cell metabolic profiling of human cytotoxic T cells. Nat Biotechnol 39, 186–197 (2021).

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