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Distinct and temporary-restricted epigenetic mechanisms regulate human αβ and γδ T cell development

Nature Immunology volume 21, pages 1280–1292 (2020)Cite this article

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Abstract

The development of TCRαβ and TCRγδ T cells comprises a step-wise process in which regulatory events control differentiation and lineage outcome. To clarify these mechanisms, we employed RNA-sequencing, ATAC–sequencing and ChIPmentation on well-defined thymocyte subsets that represent the continuum of human T cell development. The chromatin accessibility dynamics show clear stage specificity and reveal that human T cell-lineage commitment is marked by GATA3- and BCL11B-dependent closing of PU.1 sites. A temporary increase in H3K27me3 without open chromatin modifications is unique for β-selection, whereas emerging γδ T cells, which originate from common precursors of β-selected cells, show large chromatin accessibility changes due to strong T cell receptor (TCR) signaling. Furthermore, we unravel distinct chromatin landscapes between CD4+ and CD8+ αβ-lineage cells that support their effector functions and reveal gene-specific mechanisms that define mature T cells. This resource provides a framework for studying gene regulatory mechanisms that drive normal and malignant human T cell development.

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Fig. 1: The transcriptome and accessible chromatin landscape of human T cell development.
Fig. 2: Discontinuous gene expression and chromatin accessibility dynamics during human T cell development.
Fig. 3: Transcriptional regulation of differentiating thymocytes by activating and repressing histone modifications.
Fig. 4: Decreased accessibility of PU.1 binding sites marks T cell commitment.
Fig. 5: GATA3 and BCL11B close PU.1 binding sites in early thymocytes.
Fig. 6: Differential regulation of the αβ–γδ bifurcation at the transcriptome and chromatin level.
Fig. 7: Early αβ T cell development is marked by a temporary increase in H3K27me3.
Fig. 8: Transcriptional and epigenetic characterization of mature naive αβ and γδ T cells.

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Data availability

The sequencing data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) and are available through the GSE151081 accession ID. Publicly available data were retrieved from the ChIP Atlas or the GEO with the following GEO accession codes: GSM2893605, GSM3686939, GSM1557623, GSM2592810, GSM1816090, GSM1013125, GSM1059446 and GSM956030. Any other data that support the findings of the present study are available from the corresponding author upon request.

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Acknowledgements

We thank C. de Bock for help on the ATAC–seq protocol; S. Vermaut and K. Reynvoet for flow cytometry help; K. Weening for assistance in molecular cloning; K. Francois and G. Van Nooten (Department of Cardiac Surgery, Ghent University Hospital) for thymus tissue; the Red Cross Flanders for CB; and the Ghent University Hospital Hematopoietic Biobank. This work was supported by the Fund for Scientific Research Flanders (FWO, grant nos. G037514N, G053816N and G053916N to T.T., fellowship grant to S.S.); the Concerted Research Action from the Ghent University Research Fund (GOA, grant no. BOF18-GOA-024 to T.T. and P.V.V.); the European Research Council (ERC, grant no. StG 639784 to P.V.V.); the Foundation against Cancer (Stichting Tegen Kanker, grant no. FAF-F/2016/824 to T.T.); and the Cancer Research Institute Ghent (CRIG, YIPOC to A.K.). The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation—Flanders (FWO) and the Flemish Government, Department EWI.

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  1. These authors contributed equally: Juliette Roels, Anna Kuchmiy.

Authors and Affiliations

  1. Department of Biomolecular Medicine, Ghent University, Ghent, Belgium

    Juliette Roels & Pieter Van Vlierberghe

  2. Department of Diagnostic Sciences, Ghent University, Ghent, Belgium

    Juliette Roels, Anna Kuchmiy, Matthias De Decker, Steven Strubbe, Marieke Lavaert, Kai Ling Liang, Georges Leclercq, Bart Vandekerckhove & Tom Taghon

  3. Cancer Research Institute Ghent, Ghent, Belgium

    Juliette Roels, Anna Kuchmiy, Kai Ling Liang, Georges Leclercq, Bart Vandekerckhove, Filip Van Nieuwerburgh, Pieter Van Vlierberghe & Tom Taghon

  4. Laboratory of Pharmaceutical Biotechnology, Ghent University, Ghent, Belgium

    Filip Van Nieuwerburgh

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Contributions

J.R., A.K., M.D.D., S.S., K.L.L. and F.V.N. performed the experiments. J.R., A.K. and M.L. performed the analyses. G.L., B.V. and P.V.V. provided critical reagents and J.R., A.K. and T.T. designed the research and wrote the paper. All authors have seen, reviewed and approved the final version.

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Correspondence to Tom Taghon.

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Extended data

Extended Data Fig. 1 Schematic overview of human T cell development.

Schematic overview of the sorted populations that represent all major stages of human T cell development. During T cell development, a CD34+ hematopoietic precursor cell (HPC) enters the thymus. Immature CD34+ thymocytes undergo a T cell specification (CD34+CD1–) and T cell commitment (CD34+CD1+) phase. Further differentiation leads to the upregulation of CD4. These early CD34+CD4+ immature single positive thymocytes (ISP) are the precursors of both αβ- and γδ-lineage cells. The combination of an in-frame rearranged TCRβ chain with the invariant preTCRα induces β-selection and the generation of ISP CD28+ T cells that further mature towards double positive and eventually single positive αβ-lineage T cells. Alternatively, when a TCRγδ is successfully rearranged and expressed in CD34+CD4+ precursor cells, differentiation towards the γδ T-cell lineage is predominantly initiated. Immature γδ CD1+ T cells then further differentiate towards mature naïve γδ CD1– T cells.

Extended Data Fig. 2 Feature distribution of ATAC peaks.

a, Mean read count frequency of ATAC peaks at the transcription start site. b, Mean frequency (two donors) of genomic feature distribution of all significant ATAC peaks in all stages of T cell development.

Extended Data Fig. 3 Integration of bulk RNAseq with single cell RNAseq of human postnatal thymus.

a, Uniform manifold approximation and projection (UMAP) of the single cell RNAseq dataset available from30 with integration of our 11 bulk RNAseq subsets (red diamonds). DN: CD4 CD8 double negative; DP: CD4+ CD8+; P: proliferative; Q: quiescent; T(agonist): agonist selected T cells; DP (late): Positively selected DP T cells. b, Classification of single cells to each of the 11 bulk populations by logistic regression. c, Probability that single cells are classified according to any of the bulk RNAseq samples by logistic regression.

Extended Data Fig. 4 Branching of αβ and γδ T cells at the single cell level.

a, STEMNET analysis to predict the branching of αβ and γδ T cell lineages based on similarities in the transcriptome. For this analysis, the single cell annotation corresponding to the bulk RNAseq populations from panel Extended Data Fig. 3b was used (denoted as ‘scthymic_population’, for example scCD34+CD1– are all single cells classified as CD34+CD1– by using our sorted population as a reference). Similarity of the transcriptome of scγδ CD1+ or scDP CD3+ T cells to more immature cell populations is shown (n = 5 donors, individual datapoints and standard deviation are shown). Cells were classified to either scγδ CD1+ or scDP CD3+ by probability of similarity. This can be interpreted as the relative potential that a certain subset would directly give rise to the corresponding reference subset. This revealed the branching point of αβ and γδ T cells; at this point the potential to directly give rise to γδ CD1+ dropped below the potential to directly form DP CD3+ T cells, thus marking the bifurcation point. In this analysis, CD34+CD4+ cells have mixed lineage potential, but also ISP CD28+ could still give rise to γδ CD1+ cells whereas the probability for DP CD3– cells to form γδ CD1+ cells is very low. b, Expression of RAG1 and RAG2 in pseudo-bulk counts of single cell populations from Extended Data Fig. 3b. The expression of RAG genes indicates the capacity to rearrange the TCR chain; only from the CD34+CD4+ subsets RAG expression appears and can thus support the formation of functional αβ and γδ T cells.

Extended Data Fig. 5 K-means clustering of significantly variably expressed genes.

Scaled, log transformed gene expression of genes inside each of the 25 k-means clusters of the significantly variably expressed genes as determined by Chi-squared test (p adj.< 0.05). Indication of a selection of genes inside each cluster.

Extended Data Fig. 6 Changes in RNAseq and ATACseq during early T cell development.

Volcano plots of a, gene expression changes and b, chromatin accessibility changes during early T cell development (CD34+CD1− compared to HPC).

Extended Data Fig. 7 H3K27ac and H3K27me3 peaks that overlap with open chromatin sites closed upon T cell commitment.

H3K27ac and H3K27me3 peaks that overlap with open chromatin sites that are more accessible in CD34+CD1– compared to CD34+CD1+ thymocytes (Fig. 4a). Numbers of overlapping regions are indicated on the left of each heatmap.

Extended Data Fig. 8 Genome browser view of ATACseq and histone marks at the SOX13, ZBTB16, RORC and RAG gene loci.

Tracks show open chromatin regions and ChIPmentation data for indicated histone marks for genomic loci at selected stages of thymocyte development as indicated. Overlay for two donors is shown.

Extended Data Fig. 9 TCR stimulation of ISP CD28+ T cells diverts development from the DP αβ T cell fate.

a, Flow cytometric analysis of CD73 expression in β-selected ISP CD28+, total DP, total γδ, SP CD4+ and SP CD8+ T cells. b, NT5E (coding for CD73) gene expression in αβ and γδ T cells. CD34+CD4+ are shown as reference values from which γδ and αβ T cells develop and are representative for 2 donors. c,d, Flow cytometric analysis of CD4 and CD73 (c) and CD4 and CD8 (d) staining in unstimulated β-selected ISP CD28+ thymocytes and ISP CD28+ thymocytes stimulated 24 hours with 2.5 μL (‘Low stimulated’) or 10 μL (‘High stimulated’) CD3/CD28 ImmunoCult per 100 000 cells after 6 days of culture, representative for 3 donors. (a,c,d) Numbers indicate the frequencies for each corresponding quadrant.

Extended Data Fig. 10 TCR stimulation has a minimal impact on the transcriptome.

a, Number of differentially expressed genes in pairwise comparisons between different conditions. The upper right part of the diagonal indicates upregulated genes in the conditions indicated on the x-axis compared to conditions on the y-axis. The lower left part of the diagonal indicates genes downregulated in conditions on the x-axis as compared to the y-axis. The comparison with the largest number of differentially expressed genes is marked (for example 4645 genes are significantly upregulated in unstimulated (‘No stim’) compared to ISP CD28+ thymocytes). b, Volcano plot of gene expression in stimulated (“Low”+”High”) compared to unstimulated cells. Triangles indicate datapoints outside of axis range.

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Roels, J., Kuchmiy, A., De Decker, M. et al. Distinct and temporary-restricted epigenetic mechanisms regulate human αβ and γδ T cell development. Nat Immunol 21, 1280–1292 (2020). https://doi.org/10.1038/s41590-020-0747-9

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