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.

Early transcriptional and epigenetic regulation of CD8+ T cell differentiation revealed by single-cell RNA sequencing


During microbial infection, responding CD8+ T lymphocytes differentiate into heterogeneous subsets that together provide immediate and durable protection. To elucidate the dynamic transcriptional changes that underlie this process, we applied a single-cell RNA-sequencing approach and analyzed individual CD8+ T lymphocytes sequentially throughout the course of a viral infection in vivo. Our analyses revealed a striking transcriptional divergence among cells that had undergone their first division and identified previously unknown molecular determinants that controlled the fate specification of CD8+ T lymphocytes. Our findings suggest a model for the differentiation of terminal effector cells initiated by an early burst of transcriptional activity and subsequently refined by epigenetic silencing of transcripts associated with memory lymphocytes, which highlights the power and necessity of single-cell approaches.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: scRNA-seq analysis of CD8+ T lymphocytes responding to viral infection.
Figure 2: Cells that have undergone their first division exhibit transcriptional heterogeneity.
Figure 3: Generation and application of 'early-state' and 'fate' classifiers to predict the identity of cells in intermediate states of differentiation.
Figure 4: Identification of putative regulators of the differentiation of CD8+ T lymphocytes.
Figure 5: Ezh2 regulates effector CD8+ T lymphocyte differentiation.
Figure 6: Increased epigenetic repression of genes expressed during the differentiation of terminal effector cells.
Figure 7: Ezh2 mediates the effector differentiation of CD8+ T lymphocytes through epigenetic repression.

Accession codes

Primary accessions

Gene Expression Omnibus


  1. 1

    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).

    CAS  Article  Google Scholar 

  2. 2

    Sallusto, F., Lenig, D., Forster, R., Lipp, M. & Lanzavecchia, A. Two subsets of memory T lymphocytes with distinct homing potentials and effector functions. Nature 401, 708–712 (1999).

    CAS  Article  Google Scholar 

  3. 3

    Mueller, S.N. & Mackay, L.K. Tissue-resident memory T cells: local specialists in immune defence. Nat. Rev. Immunol. 16, 79–89 (2016).

    CAS  Article  Google Scholar 

  4. 4

    Best, J.A. et al. Transcriptional insights into the CD8+ T cell response to infection and memory T cell formation. Nat. Immunol. 14, 404–412 (2013).

    CAS  Article  Google Scholar 

  5. 5

    Kaech, S.M., Hemby, S., Kersh, E. & Ahmed, R. Molecular and functional profiling of memory CD8 T cell differentiation. Cell 111, 837–851 (2002).

    CAS  Article  Google Scholar 

  6. 6

    Chang, J.T., Wherry, E.J. & Goldrath, A.W. Molecular regulation of effector and memory T cell differentiation. Nat. Immunol. 15, 1104–1115 (2014).

    CAS  Article  Google Scholar 

  7. 7

    Arsenio, J. et al. Early specification of CD8+ T lymphocyte fates during adaptive immunity revealed by single-cell gene-expression analyses. Nat. Immunol. 15, 365–372 (2014).

    CAS  Article  Google Scholar 

  8. 8

    Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

    CAS  Article  Google Scholar 

  9. 9

    Gaublomme, J.T. et al. Single-cell genomics unveils critical regulators of Th17 cell pathogenicity. Cell 163, 1400–1412 (2015).

    CAS  Article  Google Scholar 

  10. 10

    Shalek, A.K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).

    CAS  Article  Google Scholar 

  11. 11

    Chang, J.T. et al. Asymmetric T lymphocyte division in the initiation of adaptive immune responses. Science 315, 1687–1691 (2007).

    CAS  Article  Google Scholar 

  12. 12

    Badovinac, V.P., Haring, J.S. & Harty, J.T. Initial T cell receptor transgenic cell precursor frequency dictates critical aspects of the CD8+ T cell response to infection. Immunity 26, 827–841 (2007).

    CAS  Article  Google Scholar 

  13. 13

    Ramskold, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).

    Article  Google Scholar 

  14. 14

    Rouault, J.P. et al. BTG1, a member of a new family of antiproliferative genes. EMBO J. 11, 1663–1670 (1992).

    CAS  Article  Google Scholar 

  15. 15

    Roychoudhuri, R. et al. BACH2 regulates CD8+ T cell differentiation by controlling access of AP-1 factors to enhancers. Nat. Immunol. 17, 851–860 (2016).

    CAS  Article  Google Scholar 

  16. 16

    Blackledge, N.P., Rose, N.R. & Klose, R.J. Targeting Polycomb systems to regulate gene expression: modifications to a complex story. Nat. Rev. Mol. Cell Biol. 16, 643–649 (2015).

    CAS  Article  Google Scholar 

  17. 17

    DuPage, M. et al. The chromatin-modifying enzyme Ezh2 is critical for the maintenance of regulatory T cell identity after activation. Immunity 42, 227–238 (2015).

    CAS  Article  Google Scholar 

  18. 18

    Su, I.H. et al. Polycomb group protein ezh2 controls actin polymerization and cell signaling. Cell 121, 425–436 (2005).

    CAS  Article  Google Scholar 

  19. 19

    Tumes, D.J. et al. The polycomb protein Ezh2 regulates differentiation and plasticity of CD4+ T helper type 1 and type 2 cells. Immunity 39, 819–832 (2013).

    CAS  Article  Google Scholar 

  20. 20

    Manjunath, N. et al. Effector differentiation is not prerequisite for generation of memory cytotoxic T lymphocytes. J. Clin. Invest. 108, 871–878 (2001).

    CAS  Article  Google Scholar 

  21. 21

    van der Windt, G.J. et al. Mitochondrial respiratory capacity is a critical regulator of CD8+T cell memory development. Immunity 36, 68–78 (2012).

    CAS  Article  Google Scholar 

  22. 22

    Yu, B. et al. Epigenetic landscapes reveal transcription factors regulating CD8+ T cell differentiation. Nat. Immunol. (in the press).

  23. 23

    Ma, C. & Zhang, N. Transforming growth factor-β signaling is constantly shaping memory T-cell population. Proc. Natl. Acad. Sci. USA 112, 11013–11017 (2015).

    CAS  Article  Google Scholar 

  24. 24

    Mackay, L.K. et al. T-box transcription factors combine with the cytokines TGF-β and IL-15 to control tissue-resident memory T cell fate. Immunity 43, 1101–1111 (2015).

    CAS  Article  Google Scholar 

  25. 25

    Tinoco, R., Alcalde, V., Yang, Y., Sauer, K. & Zuniga, E.I. Cell-intrinsic transforming growth factor-β signaling mediates virus-specific CD8+ T cell deletion and viral persistence in vivo. Immunity 31, 145–157 (2009).

    CAS  Article  Google Scholar 

  26. 26

    Ananieva, E.A., Patel, C.H., Drake, C.H., Powell, J.D. & Hutson, S.M. Cytosolic branched chain aminotransferase (BCATc) regulates mTORC1 signaling and glycolytic metabolism in CD4+ T cells. J. Biol. Chem. 289, 18793–18804 (2014).

    CAS  Article  Google Scholar 

  27. 27

    Schober, S.L. et al. Expression of the transcription factor lung Kruppel-like factor is regulated by cytokines and correlates with survival of memory T cells in vitro and in vivo. J. Immunol. 163, 3662–3667 (1999).

    CAS  PubMed  Google Scholar 

  28. 28

    Skon, C.N. et al. Transcriptional downregulation of S1pr1 is required for the establishment of resident memory CD8+ T cells. Nat. Immunol. 14, 1285–1293 (2013).

    CAS  Article  Google Scholar 

  29. 29

    Yamada, T., Park, C.S., Mamonkin, M. & Lacorazza, H.D. Transcription factor ELF4 controls the proliferation and homing of CD8+ T cells via the Kruppel-like factors KLF4 and KLF2. Nat. Immunol. 10, 618–626 (2009).

    CAS  Article  Google Scholar 

  30. 30

    Buck, M.D. et al. Mitochondrial dynamics controls T cell fate through metabolic programming. Cell 166, 63–76 (2016).

    CAS  Article  Google Scholar 

  31. 31

    Chtanova, T. et al. Identification of T cell-restricted genes, and signatures for different T cell responses, using a comprehensive collection of microarray datasets. J. Immunol. 175, 7837–7847 (2005).

    CAS  Article  Google Scholar 

  32. 32

    Willinger, T., Freeman, T., Hasegawa, H., McMichael, A.J. & Callan, M.F. Molecular signatures distinguish human central memory from effector memory CD8 T cell subsets. J. Immunol. 175, 5895–5903 (2005).

    CAS  Article  Google Scholar 

  33. 33

    Bouneaud, C., Garcia, Z., Kourilsky, P. & Pannetier, C. Lineage relationships, homeostasis, and recall capacities of central- and effector-memory CD8 T cells in vivo. J. Exp. Med. 201, 579–590 (2005).

    CAS  Article  Google Scholar 

  34. 34

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

    CAS  Article  Google Scholar 

  35. 35

    Gaide, O. et al. Common clonal origin of central and resident memory T cells following skin immunization. Nat. Med. 21, 647–653 (2015).

    CAS  Article  Google Scholar 

  36. 36

    Chang, J.T. et al. Asymmetric proteasome segregation as a mechanism for unequal partitioning of the transcription factor T-bet during T lymphocyte division. Immunity 34, 492–504 (2011).

    CAS  Article  Google Scholar 

  37. 37

    Lin, W.H. et al. Asymmetric PI3K signaling driving developmental and regenerative cell fate bifurcation. Cell Rep. 13, 2203–2218 (2015).

    CAS  Article  Google Scholar 

  38. 38

    Pollizzi, K.N. et al. Asymmetric inheritance of mTORC1 kinase activity during division dictates CD8+ T cell differentiation. Nat. Immunol. 17, 704–711 (2016).

    CAS  Article  Google Scholar 

  39. 39

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

    CAS  Article  Google Scholar 

  40. 40

    Metz, P.J. et al. Regulation of asymmetric division and CD8+ T lymphocyte fate specification by protein kinase Czeta and protein kinase Clambda/iota. J. Immunol. 194, 2249–2259 (2015).

    CAS  Article  Google Scholar 

  41. 41

    Zhao, E. et al. Cancer mediates effector T cell dysfunction by targeting microRNAs and EZH2 via glycolysis restriction. Nat. Immunol. 17, 95–103 (2016).

    CAS  Article  Google Scholar 

  42. 42

    Araki, Y., Fann, M., Wersto, R. & Weng, N.P. Histone acetylation facilitates rapid and robust memory CD8 T cell response through differential expression of effector molecules (eomesodermin and its targets: perforin and granzyme B). J. Immunol. 180, 8102–8108 (2008).

    CAS  Article  Google Scholar 

  43. 43

    Youngblood, B. et al. Chronic virus infection enforces demethylation of the locus that encodes PD-1 in antigen-specific CD8+ T cells. Immunity 35, 400–412 (2011).

    CAS  Article  Google Scholar 

  44. 44

    Chappell, C., Beard, C., Altman, J., Jaenisch, R. & Jacob, J. DNA methylation by DNA methyltransferase 1 is critical for effector CD8 T cell expansion. J. Immunol. 176, 4562–4572 (2006).

    CAS  Article  Google Scholar 

  45. 45

    Araki, Y. et al. Genome-wide analysis of histone methylation reveals chromatin state-based regulation of gene transcription and function of memory CD8+ T cells. Immunity 30, 912–925 (2009).

    CAS  Article  Google Scholar 

  46. 46

    Russ, B.E. et al. Distinct epigenetic signatures delineate transcriptional programs during virus-specific CD8+ T cell differentiation. Immunity 41, 853–865 (2014).

    CAS  Article  Google Scholar 

  47. 47

    Bray, N.L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    CAS  Article  Google Scholar 

  48. 48

    Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).

    CAS  Article  Google Scholar 

  49. 49

    Chen, C., Khaleel, S.S., Huang, H. & Wu, C.H. Software for pre-processing Illumina next-generation sequencing short read sequences. Source Code Biol. Med. 9, 8 (2014).

    Article  Google Scholar 

  50. 50

    Ntranos, V., Kamath, G.M., Zhang, J.M., Pachter, L. & Tse, D.N. Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts. Genome Biol. 17, 112 (2016).

    Article  Google Scholar 

  51. 51

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Article  Google Scholar 

  52. 52

    Mouse Genome Sequencing, C. et al. Initial sequencing and comparative analysis of the mouse genome. Nature 420, 520–562 (2002).

    Article  Google Scholar 

  53. 53

    Maaten, L.H.G.E. Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  54. 54

    Maaten, L. Barnes-Hut-SNE. arXIv (2013).

  55. 55

    Amir el, A.D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013).

    Article  Google Scholar 

  56. 56

    Becher, B. et al. High-dimensional analysis of the murine myeloid cell system. Nat. Immunol. 15, 1181–1189 (2014).

    CAS  Article  Google Scholar 

  57. 57

    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  Article  Google Scholar 

  58. 58

    Cheng, Y., Wong, M.T., van der Maaten, L. & Newell, E.W. Categorical analysis of human T cell heterogeneity with one-dimensional soli-expression by nonlinear stochastic embedding. J. Immunol. 196, 924–932 (2016).

    CAS  Article  Google Scholar 

  59. 59

    Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  Google Scholar 

  60. 60

    Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  Article  Google Scholar 

  61. 61

    Fan, J. et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat. Methods 13, 241–244 (2016).

    CAS  Article  Google Scholar 

  62. 62

    Li, J. & Tibshirani, R. Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data. Stat. Methods Med. Res. 22, 519–536 (2013).

    Article  Google Scholar 

  63. 63

    Stephens, M.A. EDF statistics for goodness of fit and some comparisons. J. Am. Stat. Assoc. 69, 730–737 (1974).

    Article  Google Scholar 

  64. 64

    Geurts, P.E. D.; Wehenkel, L. Extremely randomized trees. Mach. Learn. 62, 3–42 (2006).

    Article  Google Scholar 

  65. 65

    Pedregosa, F.V. G; Gramfor, A. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  66. 66

    Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016).

    CAS  Article  Google Scholar 

  67. 67

    Quinlan, A.R. BEDTools: The Swiss-Army tool for genome feature analysis. Curr. Protoc. Bioinformatics. 47, 11–34 (2014).

    Article  Google Scholar 

  68. 68

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  Article  Google Scholar 

Download references


We thank members of the Chang and Yeo laboratories for discussions and critical reading of the manuscript, and the Sanford Consortium Stem Cell Genomics Core and Institute for Genomic Medicine Genomics Center for single-cell captures and sequencing. Supported by the US National Institutes of Health (DK093507, OD008469, and AI095277 to J.T.C.; NS075449, HG004659 and MH107367 to G.W.Y.; AI072117 and AI096852 to A.W.G; AI081923 and AI113923 to E.I.Z.; DK007202 for C.E.W. and P.J.M.) and the Howard Hughes Medical Institute (J.T.C.).

Author information




B.K., Z.H. and G.W.Y. performed computational analysis; J.A., C.E.W., E.J.W., E.I.Z., B.Y. A.W.G., J.T.C. and G.W.Y. designed experiments and analyzed data; J.A., C.E.W., S.A., P.J.M., B.Y., J.L. and S.H.K. performed experiments; and B.K., J.A., C.E.W., Z.H., J.T.C. and G.W.Y. wrote the manuscript.

Corresponding authors

Correspondence to John T Chang or Gene W Yeo.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Experimental design and quality-control metrics.

(a) Distribution of sequencing output (millions of reads per cell) from single-cell RNA-seq libraries for each cell population. Gaussian kernel smoothing was applied with bandwidth of 10. (b) Distribution of alignment quality (% of reads uniquely mapped) from single-cell RNA-seq libraries for each cell population. Gaussian kernel smoothing was applied with bandwidth of 10. (c) Eigen spectrum of covariance for single-cell expression data matrix (sorted by % of variance explained for each principal component (PC)) justifying use of top 10 PCs for t-distributed Stochastic Neighborhood Embedding (tSNE). (d) Clustergram of pairwise distances between representative triples of decoy plus duplicate samples from each cell population. Duplicate samples from the same single cell library (round braces) are closest to each other (dark red). Next closest (red) to them is a decoy sample of another single cell library from the same cell population (angled brackets). Duplicate and decoy samples form other cell populations are furthest away from each other (light red). (e) tSNE plot of each cell's raw expression counts (TPM) prior to log-transformation, colored by population. (f) Same tSNE plot as (e) but colored by plate (and sequencing batch) of each cell. Low batch effect was due to careful experimental design and random assignment of cells from each population across at least 2 out of 3 plates.

Supplementary Figure 2 Temporal expression patterns of genes encoding members of the PRC2 complex.

Temporal expression patterns of Ezh2, Set, Eed, and Suz12 across inferred paths of differentiation for effector (orange), TCM (purple), and TEM cells (green). Shaded areas around the lines indicate the 95% confidence interval bootstrapped from all possible single-cell expression trajectories.

Supplementary Figure 3 Ezh2-deficient CD8+ T lymphocytes undergo normal activation and proliferation.

(a) Expression of CD44 and CD62L by gated CD8+ cells from Ezh2fl/flCd4+/+ (‘WT’) and Ezh2fl/flCd4Cre (‘KO’) P14 mice. (b) Proportion of gated CD8+CD45.1+ P14 WT and KO cells, analyzed at 2, 3, and 5 d post-infection following adoptive transfer into recipient mice subsequently infected with LCMV. (c) Flow cytometry analysis of CD69 and CD44 by undivided (1st CFSE peak) WT and KO P14 CD8+ T cells labeled with CFSE and adoptively transferred into recipient mice subsequently infected with LCMV and analyzed at 48 h post-infection. (d) Flow cytometry analysis of CFSE dilution by WT and KO CD8+ T cells, as in (c), at 48 and 72 h post-infection with LCMV. (e) Analysis of 7-AAD and Annexin V expression in gated 1st division (2nd CFSE peak) IL-2RαhiCD62Llo and IL-2RαloCD62Lhi WT and KO P14 CD8+ T cells responding to LCMV infection in vivo, presented as flow cytometry analysis (left) and bar graphs (right). Lack of significant differences in apoptosis between Ezh2-deficient Division 1 ‘pre-effector’ IL-2RαhiCD62Llo and ‘pre-memory’ IL-2RαloCD62Lhi cells may be due to the difficulty detecting subtle differences in apoptosis in vivo owing to rapid clearance of dying cells. * p < 0.05, *** p < 0.001 N.S. not significant (Student’s two-tailed t-test). Data are representative of two independent experiments with 4 mice in each group (a-e).

Supplementary Figure 4 Expression of effector-cell- and memory-cell-associated surface markers in wild-type and Ezh2-deficient CD8+ T lymphocytes.

Wild-type and Ezh2-deficient CD8+ T lymphocytes were adoptively transferred into recipient mice subsequently infected with LCMV and analyzed at different times post-infection. Proportion of wild-type (‘WT’) and Ezh2-deficient (‘KO’) CD8+ T cells expressing CD44 and IL-2Rα in (a) Division 1 cells and in (b) cells harvested at 4 d post-infection. (c) Proportion of WT and KO CD8+ T cells expressing CD44, IL-2Rα, KLRG1, and IL-7R in cells harvested at 7 d post-infection. ** p < 0.01, N.S. not significant (Student’s two-tailed t-test). Data are representative of 2 independent experiments with 3 mice in each group (a-c).

Supplementary Figure 5 Distribution of changes in the expression of H3K27me3-marked and unmarked genes during CD8+ T cell differentiation.

Histograms depicting the distribution of changes in expression, depicted as –log2 TPM ratio, in genes where TSS region is marked or unmarked by H3K27me3 during differentiation of (a) effector cells, (b) TCM cells, and (c) TEM cells. Significance was determined by the Kolmogorov-Smirnov (KS) 2-sample test. P < 0.05 was considered significant.

Supplementary Figure 6 Bulk RNA-seq analysis of wild-type and Ezh2-deficient CD8+ T cells.

Wild-type (‘WT’) or Ezh2-deficient (‘KO’) CD8+ P14 T cells isolated 4 d following LCMV infection were analyzed using bulk RNA-seq. (a) Normalized expression of H3K27me3-marked genes in WT and KO CD8+ T cells. (b) Distribution of changes in gene expression (Log2 TPM) gated on whether TSS region is marked or unmarked by H3K27me3 in WT and KO CD8+ T cells. (c, d) Gene Ontology analysis of Ezh2-targeted genes (c) upregulated and (d) downregulated in KO CD8+ T cells compared to WT CD8+ T cells. (e-g) Heatmaps showing expression changes of (e) selected pro-apoptotic genes, (f) Ezh2-targeted memory-associated genes, and (g) Ezh2-untargeted memory-associated genes in WT and KO CD8+ T cells. Two biological replicate samples, made from two individual pools of n = 4 of each genotype, were utilized in the analysis. Significance was determined by the Kolmogorov-Smirnov (KS) 2-sample test (a,b). P < 0.05 was considered significant. *** p < 0.001.

Supplementary Figure 7 H3K27me3-coverage change of wild-type and Ezh2-deficient CD8+ T cells.

Wild-type (‘WT’) or Ezh2-deficient (‘KO’) CD8+ T cells isolated at 4 days following activation in vitro were subjected to H3K27me3 ChIP-seq analysis. Heatmaps show changes in H3K27me3 coverage of (a) Ezh2-targeted and (b) untargeted genes in KO CD8+ T cells compared to WT cells.

Supplementary Figure 8 Ezh2 mediates the effector differentiation of CD8+ T lymphocytes through epigenetic repression.

(a) Normalized changes in H3K27me3 coverage, identified by ChIP-seq, of Ezh2-targeted genes (red) and untargeted genes (blue) in wild-type (‘WT’) and Ezh2-deficient (‘KO’) CD8+ T cells isolated at 4 days following activation in vitro. (b) ChIP-seq analysis of H3K27me3 binding at Eomes, Klf2, Foxo1, and Tcf7 loci in WT (red) and KO (blue) CD8+ T cells. Gray indicates input. Red or blue arrows indicate H3K27me3 binding peaks in WT or KO cells, respectively.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 (PDF 1798 kb)

Supplementary Table 1

Differentially expressed genes between TCM and TEM cells (XLSX 69 kb)

Supplementary Table 2

Differentially expressed genes between Div1TE and Div1MEM cells (XLSX 357 kb)

Supplementary Table 3

Gene Ontology Analysis for genes differentially expressed between Div1TE and Div1MEM cells (XLSX 68 kb)

Supplementary Table 4

Most important genes used by early state classifier (XLSX 70 kb)

Supplementary Table 5

Most important genes used by fate classifier (XLSX 55 kb)

Supplementary Table 6

Differentially expressed genes between effector and memory cells (XLSX 39 kb)

Supplementary Table 7

89 putative regulators of CD8+ T cell differentiation (XLSX 43 kb)

Supplementary Table 8

Changes in H3K27me3 coverage between naïve vs terminal effector cells and naïve vs memory cells (XLSX 1281 kb)

Supplementary Table 9

H3K27me3 intensity in naïve, terminal effector, and memory cells (XLSX 1039 kb)

Supplementary Table 10

Ezh2 gene targets identified in activated CD8+ T cells (XLSX 55 kb)

Supplementary Table 11

Gene Ontology Analysis for Ezh2 target genes (XLSX 19 kb)

Supplementary Table 12

Genes expressed in Ezh2-deficient vs. wild-type CD8+ T cells (XLSX 1059 kb)

Supplementary Table 13

H3K27me3 coverage changes in Ezh2-deficient vs. wild-type CD8+ T cells (XLSX 910 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kakaradov, B., Arsenio, J., Widjaja, C. et al. Early transcriptional and epigenetic regulation of CD8+ T cell differentiation revealed by single-cell RNA sequencing. Nat Immunol 18, 422–432 (2017).

Download citation

Further reading


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