Skip to main content

Thank you for visiting nature.com. 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.

Shared and distinct biological circuits in effector, memory and exhausted CD8+ T cells revealed by temporal single-cell transcriptomics and epigenetics

Abstract

Naïve CD8+ T cells can differentiate into effector (Teff), memory (Tmem) or exhausted (Tex) T cells. These developmental pathways are associated with distinct transcriptional and epigenetic changes that endow cells with different functional capacities and therefore therapeutic potential. The molecular circuitry underlying these developmental trajectories and the extent of heterogeneity within Teff, Tmem and Tex populations remain poorly understood. Here, we used the lymphocytic choriomeningitis virus model of acute-resolving and chronic infection to address these gaps by applying longitudinal single-cell RNA-sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) analyses. These analyses uncovered new subsets, including a subpopulation of Tex cells expressing natural killer cell-associated genes that is dependent on the transcription factor Zeb2, as well as multiple distinct TCF-1+ stem/progenitor-like subsets in acute and chronic infection. These data also revealed insights into the reshaping of Tex subsets following programmed death 1 (PD-1) pathway blockade and identified a key role for the cell stress regulator, Btg1, in establishing the Tex population. Finally, these results highlighted how the same biological circuits such as cytotoxicity or stem/progenitor pathways can be used by CD8+ T cell subsets with highly divergent underlying chromatin landscapes generated during different infections.

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

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Single-cell transcriptional and accessible chromatin landscape of memory and exhausted CD8+ T development.
Fig. 2: Acute-resolving infection generates two branches of effector and memory CD8+ T cells distinguished by epigenetic cytolytic potential.
Fig. 3: Exhausted CD8+ T cells are transcriptionally heterogeneous and include a distinct subset characterized by expression of natural killer cell receptors.
Fig. 4: The accessible chromatin landscape distinguishes fewer exhausted T cell epigenetic cell fates under wider transcriptional diversity.
Fig. 5: Zeb2 promotes differentiation of epigenetically distinct cytotoxic CD8+ T cell subsets in chronic and acute-resolving viral infection.
Fig. 6: PD-1 pathway blockade alters exhausted T cell subset dynamics within the preexisting population structure.
Fig. 7: Acute-resolving and chronic infections generate Tcf7-expressing progenitors with divergent accessible chromatin profiles.
Fig. 8: Transition from Exh-Pre to Exh-Prog uncovers Btg1 as a new regulator of exhausted T cell differentiation.

Data availability

scRNA-seq and scATAC-seq data generated in this study are deposited in the National Center for Biotechnology Information Gene Expression Omnibus under accession GSE199565. Processed Seurat R objects are available here. Source data are provided with this paper.

Code availability

All analyses were done with custom R scripts and are available upon request using standard R packages. No new algorithms were developed during this study.

References

  1. Kaech, S. M. et al. Selective expression of the interleukin-7 receptor identifies effector CD8+ T cells that give rise to long-lived memory cells. Nat. Immunol. 4, 1191–1198 (2003).

    Article  CAS  Google Scholar 

  2. Joshi, N. S. et al. Inflammation directs memory precursor and short-lived effector CD8+ T cell fates via the graded expression of t-bet transcription factor. Immunity 27, 281–295 (2007).

    Article  CAS  Google Scholar 

  3. Martin, M. D. & Badovinac, V. P. Defining memory CD8+ T cell. Front. Immunol. 9, 2692 (2018).

    Article  Google Scholar 

  4. Chung, H. K., McDonald, B. & Kaech, S. M. The architectural design of CD8+ T cell responses in acute and chronic infection: Parallel structures with divergent fates. J. Exp. Med. 218, e20201730 (2021).

    Article  CAS  Google Scholar 

  5. McLane, L. M., Abdel-Hakeem, M. S. & Wherry, E. J. CD8+ T cell exhaustion during chronic viral infection and cancer. Annu. Rev. Immunol. 37, 457–495 (2019).

    Article  CAS  Google Scholar 

  6. Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).

    Article  CAS  Google Scholar 

  7. Utzschneider, D. T. et al. T cell factor 1-expressing memory like CD8+ T cells sustain the immune response to chronic viral infections. Immunity 45, 415–427 (2016).

    Article  CAS  Google Scholar 

  8. Blackburn, S. D., Shin, H., Freeman, G. J. & Wherry, E. J. Selective expansion of a subset of exhausted CD8+ T cells by αPD-L1 blockade. Proc. Natl Acad. Sci. USA 105, 15016–15021 (2008).

    Article  CAS  Google Scholar 

  9. Krishna, S. et al. Stem-like CD8+ T cells mediate response of adoptive cell immunotherapy against human cancer. Science 370, 1328–1334 (2020).

    Article  CAS  Google Scholar 

  10. Hudson, W. H. et al. Proliferating transitory T cells with an effector-like transcriptional signature emerge from PD-1+ stem-like CD8+ T cells during chronic infection. Immunity 51, 1043–1058 (2019).

    Article  CAS  Google Scholar 

  11. Beltra, J. C. et al. Developmental relationships of four exhausted CD8+ T cell subsets reveals underlying transcriptional and epigenetic landscape control mechanisms. Immunity 52, 825–841 (2020).

    Article  CAS  Google Scholar 

  12. Zander, R. et al. CD4+ T cell help is required for the formation of a cytolytic CD8+ T cell subset that protects against chronic infection and cancer. Immunity 51, 1028–1042 (2019).

    Article  CAS  Google Scholar 

  13. Paley, M. A. et al. Progenitor and terminal subsets of CD8+ T cells cooperate to contain chronic viral infection. Science 338, 1220–1225 (2012).

    Article  CAS  Google Scholar 

  14. Pauken, K. E. et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 354, 1160–1165 (2016).

    Article  CAS  Google Scholar 

  15. Abdel-Hakeem, M. S. et al. Epigenetic scarring of exhausted T cells hinders memory differentiation upon eliminating chronic antigenic stimulation. Nat. Immunol. 22, 1008–1019 (2021).

    Article  CAS  Google Scholar 

  16. Yates, K. B. et al. Epigenetic scars of CD8+ T cell exhaustion persist after cure of chronic infection in humans. Nat. Immunol. 22, 1020–1029 (2021).

    Article  CAS  Google Scholar 

  17. Sen, D. R. et al. The epigenetic landscape of T cell exhaustion. Science 354, 1165–1169 (2016).

    Article  CAS  Google Scholar 

  18. Khan, O. et al. TOX transcriptionally and epigenetically programs CD8+ T cell exhaustion. Nature 571, 211–218 (2019).

    Article  CAS  Google Scholar 

  19. Alfei, F. et al. TOX reinforces the phenotype and longevity of exhausted T cells in chronic viral infection. Nature 571, 265–269 (2019).

    Article  CAS  Google Scholar 

  20. Scott, A. C. et al. TOX is a critical regulator of tumour-specific T cell differentiation. Nature 571, 270–274 (2019).

    Article  CAS  Google Scholar 

  21. Yao, C. et al. Single-cell RNA-seq reveals TOX as a key regulator of CD8+ T cell persistence in chronic infection. Nat. Immunol. 20, 890–901 (2019).

    Article  CAS  Google Scholar 

  22. Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet 48, 1193–1203 (2016).

    Article  CAS  Google Scholar 

  23. Yoshida, H. et al. The cis-regulatory atlas of the mouse immune system. Cell 176, 897–912 (2019).

    Article  CAS  Google Scholar 

  24. Giles, J. R. et al. Human epigenetic and transcriptional T cell differentiation atlas for identifying functional T cell-specific enhancers. Immunity 55, 557–574.e557 (2022).

    Article  CAS  Google Scholar 

  25. Wherry, E. J. et al. Lineage relationship and protective immunity of memory CD8+ T cell subsets. Nat. Immunol. 4, 225–234 (2003).

    Article  CAS  Google Scholar 

  26. Omilusik, K. D. et al. Transcriptional repressor ZEB2 promotes terminal differentiation of CD8+ effector and memory T cell populations during infection. J. Exp. Med. 212, 2027–2039 (2015).

    Article  Google Scholar 

  27. Dominguez, C. X. et al. The transcription factors ZEB2 and T-bet cooperate to program cytotoxic T cell terminal differentiation in response to LCMV viral infection. J. Exp. Med 212, 2041–2056 (2015).

    Article  CAS  Google Scholar 

  28. Guan, T. et al. ZEB1, ZEB2, and the miR-200 family form a counterregulatory network to regulate CD8+ T cell fates. J. Exp. Med 215, 1153–1168 (2018).

    Article  CAS  Google Scholar 

  29. Masopust, D. & Soerens, A. G. Tissue-resident T cells and other resident leukocytes. Annu. Rev. Immunol. 37, 521–546 (2019).

    Article  CAS  Google Scholar 

  30. Chen, Z. et al. TCF-1-centered transcriptional network drives an effector versus exhausted CD8+ T cell-fate decision. Immunity 51, 840–855 (2019).

    Article  CAS  Google Scholar 

  31. Good, C. R. et al. An NK-like CAR T cell transition in CAR T cell dysfunction. Cell 184, 6081–6100 (2021).

    Article  CAS  Google Scholar 

  32. Zheng, L. et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 374, abe6474 (2021).

    Article  Google Scholar 

  33. Mathewson, N. D. et al. Inhibitory CD161 receptor identified in glioma-infiltrating T cells by single-cell analysis. Cell 184, 1281–1298 (2021).

    Article  CAS  Google Scholar 

  34. van Montfoort, N. et al. NKG2A blockade potentiates CD8+ T cell immunity induced by cancer vaccines. Cell 175, 1744–1755 (2018).

    Article  Google Scholar 

  35. Raulet, D. H., Marcus, A. & Coscoy, L. Dysregulated cellular functions and cell stress pathways provide critical cues for activating and targeting natural killer cells to transformed and infected cells. Immunol. Rev. 280, 93–101 (2017).

    Article  CAS  Google Scholar 

  36. Huang, A. C. et al. A single dose of neoadjuvant PD-1 blockade predicts clinical outcomes in resectable melanoma. Nat. Med 25, 454–461 (2019).

    Article  CAS  Google Scholar 

  37. McMahon, C. W. et al. Viral and bacterial infections induce expression of multiple NK cell receptors in responding CD8+ T cells. J. Immunol. 169, 1444–1452 (2002).

    Article  CAS  Google Scholar 

  38. McMahon, C. W. & Raulet, D. H. Expression and function of NK cell receptors in CD8+ T cells. Curr. Opin. Immunol. 13, 465–470 (2001).

    Article  CAS  Google Scholar 

  39. Philip, M. et al. Chromatin states define tumour-specific T cell dysfunction and reprogramming. Nature 545, 452–456 (2017).

    Article  CAS  Google Scholar 

  40. Nüssing, S. et al. Efficient CRISPR–Cas9 gene editing in uncultured naive mouse T cells for in vivo studies. J. Immunol. 204, 2308–2315 (2020).

    Article  Google Scholar 

  41. Will, B. et al. Satb1 regulates the self-renewal of hematopoietic stem cells by promoting quiescence and repressing differentiation commitment. Nat. Immunol. 14, 437–445 (2013).

    Article  CAS  Google Scholar 

  42. Huang, C. & Qin, D. Role of Lef1 in sustaining self-renewal in mouse embryonic stem cells. J. Genet. Genomics 37, 441–449 (2010).

    Article  CAS  Google Scholar 

  43. Chen, Z. et al. In vivo CD8+ T cell CRISPR screening reveals control by Fli1 in infection and cancer. Cell 184, 1262–1280 (2021).

    Article  CAS  Google Scholar 

  44. Angelosanto, J. M., Blackburn, S. D., Crawford, A. & Wherry, E. J. Progressive loss of memory T cell potential and commitment to exhaustion during chronic viral infection. J. Virol. 86, 8161–8170 (2012).

    Article  CAS  Google Scholar 

  45. Lane, N. & Martin, W. The energetics of genome complexity. Nature 467, 929–934 (2010).

    Article  CAS  Google Scholar 

  46. Yuniati, L., Scheijen, B., van der Meer, L. T. & van Leeuwen, F. N. Tumor suppressors BTG1 and BTG2: beyond growth control. J. Cell. Physiol. 234, 5379–5389 (2019).

    Article  CAS  Google Scholar 

  47. Venezia, T. A. et al. Molecular signatures of proliferation and quiescence in hematopoietic stem cells. PLoS Biol. 2, e301 (2004).

    Article  Google Scholar 

  48. Milner, J. J. et al. Runx3 programs CD8+ T cell residency in non-lymphoid tissues and tumours. Nature 552, 253 (2017).

    Article  CAS  Google Scholar 

  49. Odorizzi, P. M., Pauken, K. E., Paley, M. A., Sharpe, A. & Wherry, E. J. Genetic absence of PD-1 promotes accumulation of terminally differentiated exhausted CD8+ T cells. J. Exp. Med 212, 1125–1137 (2015).

    Article  CAS  Google Scholar 

  50. Araki, K. et al. Translation is actively regulated during the differentiation of CD8+ effector T cells. Nat. Immunol. 18, 1046–1057 (2017).

    Article  CAS  Google Scholar 

  51. Fellmann, C. et al. An optimized microRNA backbone for effective single-copy RNAi. Cell Rep. 5, 1704–1713 (2013).

    Article  CAS  Google Scholar 

  52. Kurachi, M. et al. Optimized retroviral transduction of mouse T cells for in vivo assessment of gene function. Nat. Protoc. 12, 1980–1998 (2017).

    Article  CAS  Google Scholar 

  53. Shin, H., Blackburn, S. D., Blattman, J. N. & Wherry, E. J. Viral antigen and extensive division maintain virus-specific CD8+ T cells during chronic infection. J. Exp. Med. 204, 941–949 (2007).

    Article  CAS  Google Scholar 

  54. Wherry, E. J., Barber, D. L., Kaech, S. M., Blattman, J. N. & Ahmed, R. Antigen-independent memory CD8+ T cells do not develop during chronic viral infection. Proc. Natl Acad. Sci. USA 101, 16004-9 (2004).

    Article  Google Scholar 

  55. Yao, C. BACH2 enforces the transcriptional and epigenetic programs of stem-like CD8+ T cells. Nat Immunol. 22, 370–380 (2021).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank members of the laboratory of E.J.W. This work was supported by T32 CA009140 and a Cancer Research Institute-Mark Foundation Fellowship (to J.G.), by the Parker Institute for Cancer Immunotherapy and Stand Up to Cancer and National Institutes of Health (NIH) grants AI155577, AI149680, AI108545, AI082630, DK127768 and CA210944 (to E.J.W.). Work in the Wherry laboratory is supported by the Parker Institute for Cancer Immunotherapy. S.F.N. was supported by an Australia NHMRC C.J. Martin Fellowship (GNT1111469) and the Mark Foundation Momentum Fellowship. O.K. was supported by an NIAID F30 fellowship (F30AI129263). D.M. was supported through The American Association of Immunologists Intersect Fellowship Program for Computational Scientists and Immunologists. J.E.W. was supported by a PICI Scholar award. Y.J.H. was supported by a National Science Foundation graduate research fellowship. A.C.H. was supported by NIH grant K08-CA230157, the Damon Runyon Clinical Investigator Award, Doris Duke Clinical Scientist Development Award, W. W. Smith Charitable Trust Award, the Tara Miller Foundation and P50 CA174523. The melanoma clinical trial was supported by SPORE grant P50CA261608.

Author information

Authors and Affiliations

Authors

Contributions

J.G., O.K. and E.J.W. conceived and designed the experiments. J.G., O.K. and R.S. performed FACS and prepared sequencing libraries. J.G. analyzed data with help from S.F.N., S.M. and H.H. P.W. prepared retroviruses. M.S.A. provided long-term Arm-infected mice. A.E.B., S.F.N., D.M., M.M.P., R.R.G., J.E.W. and Y.J.H. helped with experiments. For the melanoma TIL samples, A.C.H. and T.C.M. designed the trial; A.C.H., T.C.M., X.X. and G.C.K. implemented the clinical trial at Penn; T.C.M. was principal investigator of the clinical trial; and P.K.Y. performed flow cytometry on TIL samples. J.G. and E.J.W. wrote the manuscript.

Corresponding author

Correspondence to E. John Wherry.

Ethics declarations

Competing interests

E.J.W. is a member of the Parker Institute for Cancer Immunotherapy, which supported the study. E.J.W. is an advisor for Danger Bio, Marengo, Janssen, Pluto Immunotherapeutics, Related Sciences, Rubius Therapeutics, Synthekine and Surface Oncology. E.J.W. is a founder of Surface Oncology, Danger Bio and Arsenal Biosciences. E.J.W. has a patent on the PD-1 pathway. O.K. holds equity in Arsenal Biosciences and is an employee of Orange Grove Bio. A.C.H. is a consultant for Immunai and receives funding from BMS. X.X. is scientific cofounder of CureBiotech and Exio Biosciences. T.M. is on the scientific advisory board for Merck, BMS, OncoSec, GigaGen and Instil Bio. G.C.K. is on the scientific advisory board for Merck and was the principal investigator of an investigator-initiated trial sponsored by Merck.

Peer review

Peer review information

Nature Immunology thanks Fotini Gounari and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: L. A. Dempsey, in collaboration with the Nature Immunology team.

Additional information

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

Extended data

Extended Data Fig. 1 Flow cytometry gating schemes.

a) Sort strategy of scRNA-seq/scATAC-seq depicted in Fig. 1a,b. b) Sort strategy of scATAC-seq depicted in Fig. 2i. c) Gating strategy for Fig. 3j. d) Gating strategy for Extended Data Fig. 5b. e) Gating strategy for Fig. 3m. f) Gating strategy for Fig. 5b-f. g) Gating strategy for Fig 8 g-m.

Extended Data Fig. 2 UMAP analysis of scRNA-seq and scATAC-seq by infection and timepoint.

UMAP from (a) scRNA-seq and (b) scATAC-seq colored by infection and timepoint as indicated.

Extended Data Fig. 3 Effector and memory clusters defined by scRNA-seq and scATAC-seq identify shared and non-overlapping cell subsets.

Percentage of cells from Arm infection by timepoint as indicated in (a) scRNA-seq clusters and (b) scATAC-seq clusters. c) scATAC-seq UMAP (left) and scRNA-seq UMAP (right) colored with d30 Arm cells.

Extended Data Fig. 4 ZEB1 motif is enriched in non-CTL clusters.

scATAC-seq UMAP of cells from Arm infection colored by ZEB1 motif enrichment. The location of CTL and non-CTL clusters is indicated.

Extended Data Fig. 5 CD8+ TIL from human melanoma post-PD1 blockade express NK receptors.

a) Sample schematic. b) Representative flow cytometry plots of four patients. Cells are first gated as live single non-naïve (not CD45RA+CD27+) CD8+ T cells. (Extended Data Fig.1d) c) Enumeration of subsets gated in (b). Two-sided paired Student’s t-test. n = 11 patients.

Source data

Extended Data Fig. 6 scATAC-seq defined clusters Eff-like I and Eff-like II are distinguished by DACRs at gene loci related to migration.

a) Barplot representing the number of DACRs between scATAC-seq clusters Eff-like I and Eff-like II. b) Number of Eff-like II DACRs per gene loci. Genes of interest annotated.

Extended Data Fig. 7 Zeb1 is critical for persistence of exhausted CD8+ T cells.

a) Experimental schematic for testing the role of Zeb1 in Cl13 infection. b) Frequency of Zeb1 KD versus control (Ctrl) over time in the spleen in Cl13 infection. Data are presented as mean values +/- standard deviation. Enumeration of Tex subsets gated as in Fig. 3j as percent of parent (c) and total number (d). (b-d) P values calculated with two-sided paired Student’s t-test with Benjamini–Hochberg correction. n = 5 d8 Cl13, 5 d15 Cl13, 5 d30 Cl13, 5 d8 Arm, 5 d15 Arm, 5 d30 Arm mice. Data representative of 2 independent experiments.

Source data

Extended Data Fig. 8 Identification of Tcf7-expressing progenitor/stem-like CD8+ T cell subsets.

a) Gene expression from scRNA-seq of all scRNA-seq defined clusters. b) Motif enrichment from scATAC-seq of all scATAC-seq defined clusters.

Extended Data Fig. 9 Btg1 expression is associated with return to quiescence after proliferation.

a) Gene expression of Btg1 compared to cell cycle phase scores in Cl13. b) Correlation of Btg1 with all other expressed genes in within G2M+ cells as indicated. c) Gene ontology of genes positively or negatively correlated Btg1 performed with performed with metascape.org which uses hypergeometric test and Benjamini-Hochberg p-value correction algorithm.

Extended Data Fig. 10 Retroviral-mediated knock down of Btg1.

a) Experimental schematic. b) qPCR results of shRNA-mediated knockdown of Btg1. Bar represents mean, points represent independent experiments.

Supplementary information

Reporting Summary

Supplementary Table 1

DEGs by cluster. DEGs were calculated with using Seurat FindAllMarkers two-sided Wilcoxon test using Bonferroni correction.

Supplementary Table 2

DACRs by cluster. DACRs were calculated with Signac FindAllMarkers two-sided LR test using Bonferroni correction.

Supplementary Table 3

Demographic information for melanoma patients.

Supplementary Table 4

DACRs with and without αPD-L1 treatment by cluster. DACRs were calculated with with Signac FindAllMarkers two-sided LR test using Bonferroni correction.

Supplementary Table 5

Flow cytometry antibodies.

Supplementary Table 6

shRNA and sgRNA sequences.

Supplementary Table 7

Sample and cluster statistics.

Source data

Source Data Fig. 3

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Giles, J.R., Ngiow, S.F., Manne, S. et al. Shared and distinct biological circuits in effector, memory and exhausted CD8+ T cells revealed by temporal single-cell transcriptomics and epigenetics. Nat Immunol 23, 1600–1613 (2022). https://doi.org/10.1038/s41590-022-01338-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-022-01338-4

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing