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Single-cell gene expression reveals a landscape of regulatory T cell phenotypes shaped by the TCR

Nature Immunology volume 19, pages 291–301 (2018)Cite this article

A Publisher Correction to this article was published on 03 May 2018

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Abstract

CD4+ T regulatory cells (Treg) are central to immune homeostasis, their phenotypic heterogeneity reflecting the diverse environments and target cells that they regulate. To understand this heterogeneity, we combined single-cell RNA-seq, activation reporter and T cell receptor (TCR) analysis to profile thousands of Treg or conventional CD4+FoxP3– T cells (Tconv) from mouse lymphoid organs and human blood. Treg and Tconv pools showed areas of overlap, as resting ‘furtive’ Tregs with overall similarity to Tconvs or as a convergence of activated states. All Tregs expressed a small core of FoxP3-dependent transcripts, onto which additional programs were added less uniformly. Among suppressive functions, Il2ra and Ctla4 were quasiconstant, inhibitory cytokines being more sparsely distributed. TCR signal intensity did not affect resting/activated Treg proportions but molded activated Treg programs. The main lines of Treg heterogeneity in mice were strikingly conserved in human blood. These results reveal unexpected TCR-shaped states of activation, providing a framework to synthesize previous observations of Treg heterogeneity.

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Fig. 1: Treg and Tconv scRNA-seq datasets.
Fig. 2: Some Tregs and Tconvs show closely related transcriptomes.
Fig. 3: Treg-signature expression in single Treg and Tconv cells.
Fig. 4: Resting and activated Treg states identified by scRNA-seq.
Fig. 5: Treg states associated with different TCR-signaling strength.
Fig. 6: Tregs that express the same TCRα/β sequence have related transcriptional programs.
Fig. 7: Similitudes in human and mouse Treg heterogeneity.

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  • 03 May 2018

    In the version of this article initially published, the Supplementary Note was missing. The Supplementary Note has now been provided online and is cited in the Methods section of the article. The error has been corrected in the HTML and PDF version of the article.

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Acknowledgements

We thank E. Sefik for help with colon profiling and K. Hattori, C. Araneo K. Waraska, M. Thorsen and R. Steen for help with mice, profiling, sorting and sequencing. This work was supported by NIH grants AI051530, AI116834 and AI125603 to C.B./D.M. D.Z. and E.K. were supported by a PhD fellowship from the Boehringer Ingelheim Fonds.

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Authors and Affiliations

  1. Division of Immunology, Department of Microbiology and Immunobiology, Harvard Medical School, and Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA

    David Zemmour, Evgeny Kiner, Diane Mathis & Christophe Benoist

  2. Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    Rapolas Zilionis & Allon M. Klein

  3. Institute of Biotechnology, Vilnius University, Vilnius, Lithuania

    Rapolas Zilionis

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Contributions

D.Z., E.K. and R.Z. performed the experiments; D.Z., A.M.K., C.B. and D.M. designed the study, and analyzed and interpreted the data; D.Z., D.M. and C.B. wrote the manuscript.

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Correspondence to Diane Mathis or Christophe Benoist.

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Integrated supplementary information

Supplementary Figure 1 Quality-control plots and algorithm to identify contaminant cells in scRNA-seq.

a. Scatterplot matrix comparing the collapsed gene expression profiles of the Treg scRNAseq replicates, with Pearson correlation and P value (t test). b. Scatterplot matrix comparing the variability in gene expression (Fano Factor = variance/mean) within each of the different Treg scRNAseq replicates, with Pearson correlation and P value (t test). c. Illustration of the naïve Bayes algorithm used to find contaminant cell in the datasets. We calculated the likelihood for each single cell transcriptome to be drawn from any of the 249 immune cell types profiled by microarrays by the Immgen consortium (Heng, T. S. & Painter, M. W. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008)). Venn diagrams: Distribution of the most likely Immgen immune cell type assigned in each independent Tconv and Treg scRNAseq dataset. d. Treg vs Tconv changes in gene expression in pooled scRNAseq (x-axis) compared with the changes in a classic population RNAseq experiment (y-axis). Highlighted genes are genes are the canonical Treg signature genes12.

Supplementary Figure 2 Furtive Tregs can be found in multiple independent datasets and express FoxP3 by flow cytometry.

a. Distribution of the number of detected genes in Tconvs (in E), Tregs (outside E) and furtive Tregs (in E). E corresponds to the cell cluster delineated in Fig. 2a. Error bars correspond to the mean +- 1 standard deviation. b. Distribution of the number of uniquely identified mRNA reads per cell in Tconvs (in E), Tregs (outside E) and furtive Tregs (in E). E corresponds to the cell cluster delineated in Fig. 2a. Error bars correspond to the mean +- 1 standard deviation. c. Two-dimensional tSNE plot of TCRβ+ CD4+ T cells sequenced using the 10X Genomics platform. Tregs (red) and Tconvs (black) were identified using the naive Bayes algorithm that compares each single-cell transcriptome with each of the 249 immune-cell states profiled by the Immgen Consortium (see Methods). The Tconv area used to identify furtive Tregs is shown along with the proportion of Tregs and Tconvs relative to the total number of Tregs and Tconvs. d. Same tSNE plots as in c, but split to show Treg and Tconv cells separately (top row) and cells in which Foxp3 transcript was detected (bottom row). e. Two-dimensional tSNE plot of index-sorted TCRβ+ CD4+ FoxP3-GFP+ Tregs and FoxP3-GFP- Tconvs sequenced by CEL-Seq. The tSNE is split to show Tregs and Tconvs separately. The Tconv area used to identify furtive Tregs is shown along with the proportion of Tregs and Tconvs relative to the total number of Tregs and Tconvs. f. FoxP3-GFP protein expression (flow cytometry intensity) distribution of indexed-sorted Tconvs, Tregs and furtive Tregs shown in e.

Supplementary Figure 3 FoxP3 binds to the core Treg-signature genes.

a. Core Treg signature genes at the single cell level are the genes that were found to be consistently differentially expressed (>80%) when we compare a sample of closely related Treg and Tconv in the transcriptional space. For each single Treg cell (n = 708), we compared the expression profiles of its 50 closest Tregs and 50 closest Tconvs (by Pearson correlation distance) and deemed significant genes with a P value <0.05. We report in this plot the average of the gene expression fold change across the 708 comparisons (x axis) and the fraction of tests where this difference was significant (y axis). Canonical Treg signature genes are highlighted in red and blue12. b. Chromatin profiles for core Treg signature genes . Top to bottom: ATAC-seq in Tconvs, ATAC-seq in Tregs, FoxP3 Chip-Seq in Tregs (data from 29).

Supplementary Figure 4 Robustness of the splenic Treg clusters.

a. Scatterplot of the average gene expression in single Treg cells and variability of expression (Fano factor = variance/mean). Top 100 most variable genes expressed in >1% cells are in red and were used in Fig. 4 for clustering. b. Jaccard index of the Treg clusters after kmeans bootstrapping (measures how many times cells consistently clustered together). c. Silhouette plot of the Treg clusters after kmeans bootstrapping (measure of how close is each single cell to the cells in the same clusters vs to the cells in the neighbor cluster). d. Biclustering expression heatmap, as Fig. 4a, for 2 independent splenic Treg scRNAseq datasets (n = 2035 and n = 1,294 cells). Columns: Single Treg cells assigned to the same 6 clusters as Fig. 4 using a likelihood model (see Methods). Rows: most characteristic genes found in the 6 Treg clusters (per Fig. 4a). The proportion of Tregs in each cluster in indicated. e. Circular projection plot of the likelihood for each single Treg cell in the 2 independent datasets to belong to any of the 6 mouse clusters described in Fig. 4.

Supplementary Figure 5 Signature enrichment in single Treg cell clusters.

Single-cell Treg expression of various genesets computed or obtained from previous Treg studies (reference included). Differently expressed signatures between clusters are represented here, ordered by hierarchal clustering. Single Treg cells were ordered by clusters (columns). P values by ANOVA.

Supplementary Figure 6 Nr4a1-GFP expression correlates with an enrichment in cluster 3 and an impoverishment in cluster 1 and 2 cells.

Circular projection plot of the likelihood for each single Treg cells in the Nr4a1-GFP high, medium and low independent datasets to belong to any of the 6 mouse clusters described in Fig. 4 (n = 1000 cells in each plot). The proportion of cells in each cluster is indicated.

Supplementary Figure 7 Human Treg and Tconv single-cell datasets: Quality-control plots and definition of the core human Treg transcripts.

a. Scatterplot matrix comparing the collapsed gene expression profiles of the Treg and Tconv scRNAseq replicates, with Pearson correlation and P value (t test). b. Scatterplot matrix comparing the variability in gene expression (Fano Factor = variance/mean) within each of the different Treg and Tconv scRNAseq replicates, with Pearson correlation and P value (t test). c. Treg vs Tconv changes in the gene expression in pooled scRNAseq (x-axis) compared with the changes in an independent population microarrray experiment (y-axis)53. Highlighted genes are genes are the canonical Treg signature genes. d. Same tSNE plots of human Treg and Tconv as in Fig. 7b, but split to show the contribution of each donor. e. Core human Treg signature. The same analysis as described in Fig. S2 was performed. This scatterplot compares the average fold change in gene expression (x axis) with the fraction of tests (y axis) that were significantly differently expressed between human Tregs and Tconvs (similar to Supplementary Fig. 3a). f. Nr4a1-GFP expression distribution in Tregs isolated from the blood and spleen of three mice.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7

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Supplementary Table 1

Single-cell datasets created in this study

Supplementary Table 2

Top 100 most variable genes amongst splenic Tregs

Supplementary Table 3

Average gene expression in each splenic Treg cluster

Supplementary Table 4

Differentially expressed genes between splenic Treg clusters

Supplementary Table 5

DNA primer for TCR sequencing

Supplementary Data

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Zemmour, D., Zilionis, R., Kiner, E. et al. Single-cell gene expression reveals a landscape of regulatory T cell phenotypes shaped by the TCR. Nat Immunol 19, 291–301 (2018). https://doi.org/10.1038/s41590-018-0051-0

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