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

Nature Immunology volume 23, pages 1600–1613 (2022)Cite this article

Subjects

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.

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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.

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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

Author notes

  1. Ryan Staupe

    Present address: Infectious Disease & Vaccines, MRL, Merck & Co. Inc., West Point, PA, USA

  2. Mohamed S. Abdel-Hakeem

    Present address: Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, USA

  3. Patrick K. Yan

    Present address: Immunology Graduate Program, Stanford University, Stanford, CA, USA

Authors and Affiliations

  1. Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA

    Josephine R. Giles, Shin Foong Ngiow, Sasikanth Manne, Amy E. Baxter, Omar Khan, Ping Wang, Ryan Staupe, Mohamed S. Abdel-Hakeem, Hua Huang, Divij Mathew, Mark M. Painter, Jennifer E. Wu, Yinghui Jane Huang, Rishi R. Goel, Patrick K. Yan & E. John Wherry

  2. Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Josephine R. Giles, Shin Foong Ngiow, Sasikanth Manne, Amy E. Baxter, Omar Khan, Ping Wang, Mohamed S. Abdel-Hakeem, Hua Huang, Divij Mathew, Mark M. Painter, Jennifer E. Wu, Yinghui Jane Huang, Rishi R. Goel, Alexander C. Huang & E. John Wherry

  3. Parker Institute for Cancer Immunotherapy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Josephine R. Giles, Shin Foong Ngiow, Jennifer E. Wu, Alexander C. Huang & E. John Wherry

  4. Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA, USA

    Ping Wang & Alexander C. Huang

  5. Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA, USA

    Hua Huang

  6. Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA

    Giorgos C. Karakousis & Xiaowei Xu

  7. Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Tara C. Mitchell

  8. Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Alexander C. Huang

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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.

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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.

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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.

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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.

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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

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