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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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 https://doi.org/10.5281/zenodo.3951613.
All custom R scripts associated with this manuscript will be made available upon reasonable request.
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).
Olenchock, B. A., Rathmell, J. C. & Vander Heiden, M. G. Biochemical underpinnings of immune cell metabolic phenotypes. Immunity 46, 703–713 (2017).
Buck, M. D., Sowell, R. T., Kaech, S. M. & Pearce, E. L. Metabolic instruction of immunity. Cell 169, 570–586 (2017).
Xu, T. et al. Metabolic control of TH17 and induced Treg cell balance by an epigenetic mechanism. Nature 548, 228–233 (2017).
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).
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).
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).
Leone, R. D. et al. Glutamine blockade induces divergent metabolic programs to overcome tumor immune evasion. Science 366, 1013–1021 (2019).
Angiari, S. et al. Pharmacological activation of pyruvate kinase M2 Inhibits CD4+ T cell pathogenicity and suppresses autoimmunity. Cell Metab. 31, 391–405 (2020).
Kornberg, M. D. et al. Dimethyl fumarate targets GAPDH and aerobic glycolysis to modulate immunity. Science 360, 449–453 (2018).
Lee, C.-F. et al. Preventing allograft rejection by targeting immune metabolism. Cell Rep. 13, 760–770 (2015).
Dettmer, K., Aronov, P. A. & Hammock, B. D. Mass spectrometry-based metabolomics. Mass Spectrom. Rev. 26, 51–78 (2007).
Buescher, J. M. et al. A roadmap for interpreting 13C metabolite labeling patterns from cells. Curr. Opin. Biotechnol. 34, 189–201 (2015).
Verbist, K. C. et al. Metabolic maintenance of cell asymmetry following division in activated T lymphocytes. Nature 532, 389–393 (2016).
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).
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).
Li, X. et al. Navigating metabolic pathways to enhance antitumour immunity and immunotherapy. Nat. Rev. Clin. Oncol. 16, 425–441 (2019).
Muir, A. & Vander Heiden, M. G. The nutrient environment affects therapy. Science 360, 962–963 (2018).
Le Bourgeois, T. et al. Targeting T cell metabolism for improvement of cancer immunotherapy. Front. Oncol. 8, 237 (2018).
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).
Keren, L. et al. MIBI-TOF: a multiplexed imaging platform relates cellular phenotypes and tissue structure. Sci. Adv. 5, eaax5851 (2019).
Hartmann, F. J. & Bendall, S. C. Immune monitoring using mass cytometry and related high-dimensional imaging approaches. Nat. Rev. Rheumatol. 16, 87–99 (2020).
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).
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).
Geiger, R. et al. L-arginine modulates T cell metabolism and enhances survival and anti-tumor activity. Cell 167, 829–842 (2016).
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).
Tanner, L. B. et al. Four key steps control glycolytic flux in mammalian cells. Cell Syst. 7, 49–62 (2018).
Wang, R. et al. The transcription factor Myc controls metabolic reprogramming upon T lymphocyte activation. Immunity 35, 871–882 (2011).
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).
Frauwirth, K. A. et al. The CD28 signaling pathway regulates glucose metabolism. Immunity 16, 769–777 (2002).
Slack, M., Wang, T. & Wang, R. T cell metabolic reprogramming and plasticity. Mol. Immunol. 68, 507–512 (2015).
Wang, R. & Green, D. R. Metabolic checkpoints in activated T cells. Nat. Immunol. 13, 907–915 (2012).
Zaslaver, A. et al. Just-in-time transcription program in metabolic pathways. Nat. Genet. 36, 486–491 (2004).
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).
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).
Fischer, M. et al. Early effector maturation of naïve human CD8+ T cells requires mitochondrial biogenesis. Eur. J. Immunol. 48, 1632–1643 (2018).
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).
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).
Blank, C. U. et al. Defining ‘T cell exhaustion’. Nat. Rev. Immunol. 19, 665–674 (2019).
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).
Son, N.-H. et al. Endothelial cell CD36 optimizes tissue fatty acid uptake. J. Clin. Invest. 128, 4329–4342 (2018).
Chang, C.-H. et al. Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell 162, 1229–1241 (2015).
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).
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).
Canale, F. P. et al. CD39 expression defines cell exhaustion in tumor-infiltrating CD8+ T cells. Cancer Res. 78, 115–128 (2018).
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).
Simon, S. & Labarriere, N. PD-1 expression on tumor-specific T cells: friend or foe for immunotherapy? Oncoimmunology 7, e1364828 (2018).
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).
Linnarsson, S. & Teichmann, S. A. Single-cell genomics: coming of age. Genome Biol. 17, 97 (2016).
Papalexi, E. & Satija, R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18, 35–45 (2018).
Schwartzman, O. & Tanay, A. Single-cell epigenomics: techniques and emerging applications. Nat. Rev. Genet. 16, 716–726 (2015).
Xiao, Z., Dai, Z. & Locasale, J. W. Metabolic landscape of the tumor microenvironment at single cell resolution. Nat. Commun. 10, 3763 (2019).
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
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).
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).
Sadelain, M. Chimeric antigen receptors: a paradigm shift in immunotherapy. Annu. Rev. Cancer Biol. 1, 447–466 (2017).
Rosenberg, S. A. & Restifo, N. P. Adoptive cell transfer as personalized immunotherapy for human cancer. Science 348, 62–68 (2015).
Van den Bossche, J. et al. Mitochondrial dysfunction prevents repolarization of inflammatory macrophages. Cell Rep. 17, 684–696 (2016).
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).
Mei, H. E., Leipold, M. D. & Maecker, H. T. Platinum-conjugated antibodies for application in mass cytometry. Cytometry A 89, 292–300 (2016).
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).
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).
Hartmann, F. J. et al. Comprehensive immune monitoring of clinical trials to advance human immunotherapy. Cell Rep. 28, 819–831 (2019).
Uhlen, M. et al. Tissue-based map of the human proteome. Science 347, 1260419–1260419 (2015).
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).
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).
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).
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).
Van Gassen, S. et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87, 636–645 (2015).
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).
Cannoodt, R. et al. SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development. Preprint at https://doi.org/10.1101/079509 (2016).
Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).
Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019).
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).
Chevrier, S. et al. Compensation of signal spillover in suspension and imaging mass cytometry. Cell Syst. 6, 612–620 (2018).
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).
Bannon, D. et al. Dynamic allocation of computational resources for deep learning-enabled cellular image analysis with Kubernetes. Preprint at https://doi.org/10.1101/505032 (2019).
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.).
The authors declare no competing interests.
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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). https://doi.org/10.1038/s41587-020-0651-8
Nature Methods (2021)
Cellular & Molecular Immunology (2021)
Nature Reviews Immunology (2021)
Nature Methods (2021)
Nature Metabolism (2021)