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

Nature Immunology volume 18, pages 422–432 (2017)Cite this article

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Abstract

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.

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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.

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Acknowledgements

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

Author notes

  1. Boyko Kakaradov, Janilyn Arsenio and Christella E Widjaja: These authors contributed equally to this work.

  2. John T Chang and Gene W Yeo: These authors jointly directed this work.

Authors and Affiliations

  1. Department of Cellular and Molecular Medicine, University of California, San Diego, California, USA

    Boyko Kakaradov, Zhaoren He, Stefan Aigner & Gene W Yeo

  2. Department of Medicine, University of California, San Diego, California, USA

    Janilyn Arsenio, Christella E Widjaja, Patrick J Metz, Justine Lopez, Stephanie H Kim & John T Chang

  3. Division of Biological Sciences, University of California, San Diego, California, USA

    Bingfei Yu, Ellen J Wehrens, Elina I Zuniga & Ananda W Goldrath

  4. Institute for Genomic Medicine, University of California, San Diego, California, USA

    Gene W Yeo

  5. Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore

    Gene W Yeo

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  1. Boyko Kakaradov
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Contributions

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.

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Correspondence to John T Chang or Gene W Yeo.

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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)

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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). https://doi.org/10.1038/ni.3688

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