Abstract
In atherosclerosis, some regulatory T (Treg) cells become exTreg cells. We crossed inducible Treg and exTreg cell lineage-tracker mice (FoxP3eGFP−Cre-ERT2ROSA26CAG-fl-stop-fl-tdTomato) to atherosclerosis-prone Apoe−/− mice, sorted Treg cells and exTreg cells and determined their transcriptomes by bulk RNA sequencing (RNA-seq). Genes that were differentially expressed between mouse Treg cells and exTreg cells and filtered for their presence in a human single-cell RNA-sequencing (scRNA-seq) panel identified exTreg cell signature genes as CST7, NKG7, GZMA, PRF1, TBX21 and CCL4. Projecting these genes onto the human scRNA-seq with CITE-seq data identified human exTreg cells as CD3+CD4+CD16+CD56+, which was validated by flow cytometry. Bulk RNA-seq of sorted human exTreg cells identified them as inflammatory and cytotoxic CD4+T cells that were significantly distinct from both natural killer and Treg cells. DNA sequencing for T cell receptor-β showed clonal expansion of Treg cell CDR3 sequences in exTreg cells. Cytotoxicity was functionally demonstrated in cell killing and CD107a degranulation assays, which identifies human exTreg cells as cytotoxic CD4+T cells.
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Data availability
RNA-seq data have been uploaded to the NCBI GEO and are accessible under accession numbers GSE217010 (mouse and human bulk RNA-seq) and GSE190570 (human scRNA-seq data). Human TCR-seq data were generated and processed by Adaptive Biotechnologies. Details of productive TCR sequences, accessed through their immunoSEQ Analyzer portal, are provided in Supplementary Table 14. Source data are provided with this paper.
Code availability
No new algorithms were generated for this study.
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Acknowledgements
We thank A. Rudensky at Memorial Sloan Kettering Cancer Center for providing lineage-tracker mice. We also thank members of the clinical core and flow cytometry core at LJI. We thank Z. Mikulski and S. McArdle, microscopy core, LJI, for capturing images of exTreg cells and Treg cells in mouse arteries. We thank H. Cheroutre and N. Thiault who kindly provided the P815 cell line.
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Contributions
K.L., A.F., P.R. and S.S.A. designed the experimental and analytical workflow. A.F. and P.R. conducted most experiments, analyzed and assembled data and drafted parts of the paper. S.S.A., M.O., A.K., J.M., Y.G. and R.G. conducted bioinformatic data analysis. S.K. conducted flow cytometry. M. Orecchioni conducted RT–qPCR and mouse proliferation experiments. A.J.A. and H.W. generated the mouse strain by breeding. A.J.A. generated most of the mouse data. Q.L. assisted in data analysis. E.W. prepared libraries for human bulk RNA-seq experiments. C.D. prepared human single-cell and mouse bulk RNA-seq libraries. F.N. assisted in mouse strain maintenance and provided expertise in proliferation experiments. K.L. conceived and supervised the study, provided funding and wrote the paper. We acknowledge the support from funding agencies. Our work is supported by the National Institutes of Health awards P01 HL136275 and R35 HL145241 to K.L., the American Heart Association’s Career Development Award (941152) to M. Orecchioni, a fellowship from Neven-DuMont Foundation to H.W. and a Deutsche Forschungsgemeinschaft fellowship (NE 2574/1-1) to F.N.
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Extended data
Extended Data Fig. 1 Experimental controls for lineage-tracker atherosclerotic mouse model and differentially expressed genes in mouse Treg cells vs exTregs.
(a,b) Eight week-old female Foxp3eGFP-Cre-ERT2ROSA26fl-STOP-fl-tdTomato Apoe−/− mice were injected with Tamoxifen twice for 5 days each, at week 1 and 6, then fed Western diet (WD) for 12 weeks. a) gating strategy, b) representative plots and quantification of exTreg and Treg cells among CD4+T cells (black circles) in lymph nodes (LNs) and spleen, harvested after 12 weeks of WD from 3 independent mice. Non-T cells (orange open squares) and CD4−T cells (green open circles) are negative controls. Frequencies of exTregs and Treg cells among the parent subsets were plotted as mean ± SEM. Statistical comparisons were done using 2-way ANOVA with Dunnett’s multiple comparison test. **** p < 0.0001. c) Volcano plot representing significantly differentially expressed genes between mouse Treg cells and exTregs from 20-week old FoxP3eGFP-Cre-ERT2ROSA26CAG-fl-stop-fl-tdTomato Apoe−/− mice (lymph nodes and spleen pooled). Left, up in Treg cells (blue). Right, up in exTregs (red). Horizontal dotted line is at -log10 (p adjusted) = 1.3 (padj = 0.05). The top 60 exTreg and 60 Treg classifying genes from the SVM model are annotated. Canonical Treg genes Il2ra and Foxp3 are shown in black boxes. Statistical analyses of DE genes using two-tailed Wald test with Benjamini-Hochberg correction for multiple comparisons.
Extended Data Fig. 2 Expression of exTreg candidate genes in scRNAseq data and validation by qRT-PCR from sorted human cells.
(a) Feature maps showing the gene expression of the single gene markers CST7, NKG7, GZMA, PRF1, TBX21 and CCL4 in the human single-cell dataset for all CD4 T cell clusters. (b) Combinations 1-4 and 6 of exTreg candidate genes are highlighted in red on UMAP embeddings of CD4 T clusters from the scRNA-Seq. (c) UMAP embeddings of CD4 T clusters. Black outline marks cluster CD4T_7; cells that express either CD56 (left) or CD16 (right) are shown as red dots. (d) Gating strategy to identify exTreg and Treg cells in human PBMCs. Dump channel: CD14, CD19. (e) Gene expression analysis of CST7, NKG7, GZMA, PRF1, TBX21 and CCL4 in sorted human Treg cells (blue circles) and exTregs (red circles) by qRT-PCR. Gene-specific Ct values were normalized (ΔCt) based on actin (ACTB). Relative expression was calculated by the 1/ΔCt method. n = 7. 33.33% male, 66.67% female donors; age: 21-54 yrs. Data shown as mean ± SEM. Each dot represents a biological replicate from an independent donor. Statistical comparisons by two-tailed Mann Whitney U test. **p = 0.0012,***p = 0.0006.
Extended Data Fig. 3 Human bulk RNAseq.
(a) Gating strategy used to sort human exTregs and Treg cells to perform bulk RNA-seq. (b) gene set enrichment analysis (GSEA) of bulk RNA-seq transcriptomes of sorted human exTreg cells against CD4T_7 (left) and all other clusters (right). Normalized enrichment score (NES) and FDR q values are indicated. (c) Significantly (adjusted p < 0.05) enriched pathways in human exTreg cells, based on genes expressed at significantly higher levels in human exTreg than in Treg cells. Analysis by Bioplanet2019 from the EnrichR suite. Dotted line indicates adjusted p = 0.05 (-log10 padj=1.3). Statistical comparisons with two-tailed Fisher’s exact test and Benjamini- Hochberg adjustment of p-values. (d) Gating strategy to identify exTregs and NK cells in human PBMCs. Dump channel: CD14, CD19.
Extended Data Fig. 4 Mouse bulk RNAseq.
(a) Comparative gene signature analysis between mouse exTregs and Treg cells. Genes were filtered for significant differential expression in mouse and human dataset. Gene expression shown here is from FoxP3eGFP-Cre-ERT2ROSA26CAG-fl-stop-fl-tdTomato Apoe−/− mice. Low-expressed genes (<7 raw reads in all samples) in our dataset were filtered out. Technical replicates were averaged, biological replicates shown as columns. Analysis of differentially expressed (DE) genes was done using DESeq2. Curated list of significant DE (log2FC ± 1, adjusted p < 0.05) genes are shown on normalized heatmaps, scaled by row (z scores). (b) Gene set enrichment analysis (GSEA) of mouse exTreg genes from bulk RNA-seq transcriptomes against human exTreg (left) and Treg cells (right) from the human bulk RNA-seq data set. Mouse orthologs of human genes, filtered for those present in the human scRNA-Seq targeted gene panel, were used to calculate enrichment for mouse bulk RNA-seq dataset. (c) Comparative gene signature analysis between mouse exTreg and NK cells. An external dataset was used for mouse NK cells (3 samples): GSE122597, GSE116177, and GSE52043. EdgeR was used to normalize the counts by applying the trimmed mean of M-values (TMM) method and counts per million (CPM) conversion. All other data processing and filtering steps were same as in a. Curated list of significant DE (log2FC ± 1, adjusted p < 0.05) genes are shown on normalized heatmaps, scaled by row (z scores). Statistical analyses of DE genes (a,c) using two-tailed Wald test with Benjamini-Hochberg correction for p-value adjustment. All data from independent biological replicates.
Extended Data Fig. 5 Assessment of proliferation in mouse Treg cells and exTregs.
(a) Eight week-old female Foxp3eGFP-Cre-ERT2ROSA26fl-STOP-fl-tdTomato Apoe−/− mice were injected with Tamoxifen twice for 5 days each, at week 1 and 6, then fed Western diet (WD) for 12 weeks. BrDU (0.8 mg/mL) was incorporated in the drinking water for the last 9 days of WD (n = 6). (b) Gating scheme for CD4+T cells. (c) Ki67 FMO control. (d) Representative plots and quantification of proliferating Treg cells (blue circles, %Ki67+BrDU+CD4+Foxp3+RFP+) and exTregs (red circles, %Ki67+BrDU+CD4+Foxp3−RFP+) in the spleen (n = 6), as identified by anti-BrDU and anti-Ki-67 Abs. Data shown as mean ± SEM. Each animal is an independent biological replicate. Gates were set by FMO for Ki67 and by no BrdU controls for BrdU. Background from “No BrDU” control was subtracted for normalization. The percentage of proliferating cells was compared by two-tailed Mann-Whitney U test, **p = 0.0087.
Extended Data Fig. 6 Treg marker expression on human exTregs.
Representative contour FACS plots showing the expressions of PD-1, GITR, LAG3 and TIGIT in exTreg cells (left). Corresponding FMO controls were used to set the gates. Right, contour plots showing the expression of these markers in all CD4+T cells and in Treg cells from the same donor.
Extended Data Fig. 7 Cytotoxic and T cell activation marker expression on stimulated human exTreg vs NK cells.
Contour plots show intracellular expression of CD40L (X-axis) and Perforin (Y-axis) in exTregs and NK cells from unstimulated and PMA+ionomycin stimulated PBMCs. Data from three independent donors. 33.33% male, 66.67% female donors, age: 25-43 yrs.
Extended Data Fig. 8 Gating strategy and representative plots.
(a) Gating strategy used to analyze granzyme B, perforin and TNF in Treg cells and exTregs. (b) Contour plots show surface expression of chemokine receptors CCR5, CXCR2, CXCR3, CXCR4 and CX3CR1 on exTreg cells (left) and their corresponding expression in all CD4+T cells (right). Individual FMO controls were used to set the gate for expression of each receptor.
Supplementary information
Supplementary Tables 1–14
Supplementary Table 1: Mouse and human ortholog genes. Supplementary Table 2: SVM classifier: Top 60 genes classifying Treg cells and exTreg cells. Supplementary Table 3: DEGs of mouse exTreg cells versus Treg cells. Supplementary Table 4: Number and frequency of CD4+ T cells expressing each candidate gene combination. Supplementary Table 5: Genes driving PC1 in human bulk RNA-seq data. Supplementary Table 6: DEGs of human exTreg cells versus Treg cells. Supplementary Table 7: Results obtained from the TCRβ-seq of human exTreg cells, Treg cells and Tn (naïve) cells. Supplementary Table 8: 345 significantly enriched and expanded GLIPH patterns with human exTreg TCRs. Supplementary Table 9: Differential frequency analysis of exTreg cell and Treg TCRs in the 178 shared GLIPH groups. Supplementary Table 10: Human Treg DEGs (significantly up in Treg cells compared to helper T cells) that are still expressed in exTreg cells. Supplementary Table 11: Human exTreg DEGs present in both bulk RNA-seq and scRNA-seq datasets. Supplementary Table 12: Human (h) and mouse (m) antibodies used in the study. Supplementary Table 13: Details of donors and PBMCs used for the Bulk RNA-seq and TCRβ-seq experiments. Supplementary Table 14: Details of CDR3β sequences identified in immunoSEQ assay (Adaptive).
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Freuchet, A., Roy, P., Armstrong, S.S. et al. Identification of human exTreg cells as CD16+CD56+ cytotoxic CD4+ T cells. Nat Immunol 24, 1748–1761 (2023). https://doi.org/10.1038/s41590-023-01589-9
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DOI: https://doi.org/10.1038/s41590-023-01589-9
