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A double-negative thymocyte-specific enhancer augments Notch1 signaling to direct early T cell progenitor expansion, lineage restriction and β-selection

Nature Immunology volume 23, pages 1628–1643 (2022)Cite this article

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Abstract

T cell differentiation requires Notch1 signaling. In the present study, we show that an enhancer upstream of Notch1 active in double-negative (DN) mouse thymocytes is responsible for raising Notch1 signaling intrathymically. This enhancer is required to expand multipotent progenitors intrathymically while delaying early differentiation until lineage restrictions have been established. Early thymic progenitors lacking the enhancer show accelerated differentiation through the DN stages and increased frequency of B, innate lymphoid (IL) and natural killer (NK) cell differentiation. Transcription regulators for T cell lineage restriction and commitment are expressed normally, but IL and NK cell gene expression persists after T cell lineage commitment and T cell receptor β VDJ recombination, Cd3 expression and β-selection have been impaired. This Notch1 enhancer is inactive in double-positive (DP) thymocytes. Its aberrant reactivation at this stage in Ikaros mutants is required for leukemogenesis. Thus, the DN-specific Notch1 enhancer harnesses the regulatory architecture of DN and DP thymocytes to achieve carefully orchestrated changes in Notch1 signaling required for early lineage restrictions and normal T cell differentiation.

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Fig. 1: A 5′-regulatory region controls Notch1 expression in T cell progenitors.
Fig. 2: The DNS region is required for T cell lineage restriction.
Fig. 3: Notch1 expression depends on a conserved enhancer.
Fig. 4: ETP multipotency requires the Notch1 DNSe.
Fig. 5: Increase in ILC potential and developmental arrest of ΔDNSe DN3.
Fig. 6: Promiscuous expression of IL genes in ΔDNSe DN3 cells.
Fig. 7: Regulation of DNSe by lineage-specific transcription factors.

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Data availability

All data that support the findings of the present study are available from the corresponding authors upon request. ChIP–seq and RNA-seq datasets generated during the present study have been deposited in the Gene Expression Omnibus (GEO) under accession nos. GSE186764 and GSE211079. The publicly available NGS datasets used during the present study can be found in the GEO or PRJNA, under accession nos.: GSE60103, GSE73143, GSE61149, GSE115742, GSE33679, GSE32311, GSE77695, GSE183056, GSE109125, GSE15330, GSE128964, GSE95337, GSE33513, GSE154304, GSE93755, GSE115744, PRJNA487507, GSE79422, GSE89848, GSE19923, PRJNA451505, GSE99159, GSE100738, GSE31233, GSE103953, GSE93572, GSE30518, GSE162292, GSE89847, GSE90958, GSE79422; they are listed in Supplementary Table 6. Source data are provided with this paper.

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Acknowledgements

We thank H. Petrie at the Scripps Research Institute for providing us sorted ex vivo T cell progenitors. We thank K. White, J. M. Park and F. Gounari for critical review of the manuscript and E. Wu and R. Czyzewski for mouse husbandry. Research was supported by the National Institutes of Health (grant nos. R01HL140622 and R01CA158006 to K.G., R21AR074748 to B.A.M. and R35GM128938 to F.A.). Gene targeting was performed at the Cutaneous Biology Research Center (CBRC) and at the targeting core at the University of Chicago. Cell sorting was performed at the Flow and Mass Cytometry Core/Massachusetts General Hospital and at CBRC. High-throughput RNA-seq was performed at the Bauer Center for Genomic research at Harvard University and at CBRC.

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Authors and Affiliations

  1. Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA

    Mariko Kashiwagi, Bruce A. Morgan & Katia Georgopoulos

  2. Centers for Autoimmunity, Inflammation and Cancer Immunotherapy, La Jolla Institute for Immunology, La Jolla, CA, USA

    Daniela Salgado Figueroa & Ferhat Ay

  3. Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA

    Daniela Salgado Figueroa & Ferhat Ay

  4. Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA

    Ferhat Ay

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  1. Mariko Kashiwagi
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Contributions

M.K. designed and performed the experiments, and analyzed and interpreted the data. B.A.M. and K.G. supervised the study. D.S.F. and F.A. performed RNA velocity and Hi-C analysis. M.K., B.A.M. and K.G. wrote the manuscript.

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Correspondence to Mariko Kashiwagi or Katia Georgopoulos.

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Nature Immunology thanks Michele Anderson and Ichiro Taniuchi for their contribution to the peer review of this work. L. A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Generation of mice with deletion of the DNS region and effects of the DNS and Notch1 deletions on gene expression in DN3 thymocytes.

(a) Strategy to generate a conditional deletion of the DNS region. A 4.6 kb region containing the DNS promoter and a region enriched for transcription factor binding sites (TF BS) were flanked by loxP sites (black arrowhead). The Neo marker was flanked by Frt sites (white arrowhead). Flp recombinase excises the Neo gene from the targeting construct and generates the DNS floxed allele. CD2-Cre or Mx-Cre recombinase were used to induce deletion of the DNS region in T cell progenitors or in all hematopoietic cells and progenitors, respectively. DNS1 exons (red square) and DNS2 and DNS3 exons (blue square) and TF BS (yellow square) are shown. (b, c) Genome browser tracks of normalized RNAseq reads at Notch1 ((-) strand), DNS1 ((-) strand), and DNS2 and DNS3 ((+) strand) genes in WT, ΔDNSCD2cre, and ΔN1cCD2cre DN3 thymocytes are shown. Complete loss of Notch1 exon1 (E1) was detected in ΔN1cCD2cre DN3 (b, c). Splicing events in ΔN1cCD2cre are drawn by angled lines (c). (d) Expression of DNS1-3 RNAs in DN3 is shown (mean+/– SEM). Data shown for WT DN3 is also used in Fig. 1g. (e) Number of RNA-seq reads that span splicing junctions are shown for two independent ΔN1cCD2cre DN3 data sets. (f) K-means clustering of genes significantly up-or down- regulated in ΔN1cCD2cre vs WT (log2FC > 0 or <0, adj.p < 0.05) in WT, ΔDNSCD2cre, and ΔN1cCD2cre DN3 cells is shown. A similar trend of up- and down-regulated genes was seen with ΔDNSCD2cre and ΔN1cCD2cre DN3 cells relative to WT DN3 cells although gene expression changes observed in ΔDNSCD2cre were smaller than those seen in ΔN1cCD2cre DN3. (g) GO Pathway analyses of up- or down-regulated genes in ΔN1cCD2cre vs WT. Data shown were generated from two (ΔDNSCD2cre DN3 and ΔN1cCD2cre DN3) and three (WT DN3) independent experimental groups with pooled samples from at least two biological replicates.

Source data

Extended Data Fig. 2 Generation of mice with deletion of the DNS enhancer and interaction of the DNS enhancer with the canonical Notch1 and DNS promoters.

(a) Strategy to generate the DNS enhancer mutant allele. A 600 bp region containing the conserved TF BS were deleted by CRISPR-Cas9-mediated gene editing. DNS1 (red square) and DNS2 and 3 (blue square) exons, and TF BS (yellow square) are shown by respectively colored squares. (b) Statistically significant (FDR < 0.01) long distance interactions at Notch1 locus identified by HiC at 10kb resolution in DN2 thymocytes are shown together with histone modifications and chromatin accessibility. HiC data shown in (b) was downloaded publicly available data (GSE90958).

Extended Data Fig. 3 Characterization of ΔDNSe mutant thymus.

(a) FACS profiles of total thymocytes with markers for, DN, DP, and SP stages of αβT and B cells are shown. (b) Absolute cell numbers for αβT cell subsets in the WT and ΔDNSe thymi at different ages are shown. Unpaired t-test (two-tailed) was used for statistical analysis. N.S., not significant. (c) FACS profiles of γδT cells are shown. Data shown in (a) were representative FACS profiles from one experiment with 28-day-old WT (N = 3) and ΔDNSe (N = 3) mice, in (b) were generated from 4 independent experiments with mice at different age groups (WT N = 13, ΔDNSe N = 12), in (c) were representative FACS profiles from 3 independent experiments with WT (N = 8) and ΔDNSe (N = 8) mice.

Source data

Extended Data Fig. 4 scRNAseq analysis with replicate 2 samples.

(a) Integrated analysis and UMAP visualization of WT ETPs (3341 cells) and ΔDNSe ETPs (2952 cells), is shown in 12 colored sub-clusters. Absolute cell number in each cluster is shown in parenthesis. (b) Heatmap of the top 20 enriched genes in each sub-cluster ordered by approximate developmental progression based on gene expression and connectivity in the UMAP display is shown. (c) ETP subcluster composition in WT and ΔDNSe ETPs are shown by bar graph. (d) Independent visualization of WT and ΔDNSe ETP subclusters and expression of Flt3 (a vETP marker), Spi1 (a marker for multipotentETP and innate cells), Bcl11b (a marker for proT), Id2 (a marker for ILC/NK), and Gata2 (a marker for Ery/Meg) in the UMAP defined clusters are shown. (e) Expression of genes that define ETP subsets and downstream stages of T cell differentiation as well as genes representative of ILC/NK, GN, Ery/Meg, DC, and B cell differentiation are shown. Color intensity is proportional to the average gene expression across cells in the indicated clusters. The size of circles is proportional to the percent of cells expressing the indicated genes.

Source data

Extended Data Fig. 5 Deducing lineage affiliation of cells in ETP sub-clusters (replicate 1).

(a) Previously established gene expression data sets from HSC, early lineage progenitors and thymic DC were used to define the cell type affiliation of ETP cluster-defining gene markers. The list of markers is summarized in Supplementary Table 1. (b) Expression of Hoxa9, Cd34, and Spi1 in the UMAP defined clusters are shown. (c) Expression of genes that define ETP subsets and downstream stages of T cell differentiation as well as genes representative of ILC/NK, GN, Ery/Meg, DC, and B cell differentiation are shown. Color intensity is proportional to the average gene expression across cells in the indicated clusters. The size of circles is proportional to the percent of cells expressing the indicated genes. Two replicates of HSC, MPP, CLP, ILC2p, NKp, and thymic DC were obtained from published datasets (GEO: GSE77695, GSE183056). ETP, DN2, DN3, proB, La.preB, Sm.preB, and thymic B data sets were generated in this study (as described in Fig. 1 and Fig. 2).

Extended Data Fig. 6 Analysis of RNA velocity (replicate 1).

(a) Velocity streams of scRNA analysis for WT and ΔDNSe ETPs are shown separately. Stream arrows represent relative transition probabilities based on un-spliced over spliced transcript ratio calculations. The vector calculation was performed with (top) or without (bottom) 1248 cell-cycle-associated genes that belonged to either GO term: GO:0022402 or Reactome Pathway ID: R-MMU-1640170. (b) Selected genes contributing to velocities of each cluster is shown. Genes were classified as 1) common to WT and ΔDNSe, 2) only in WT, 3) only in ΔDNSe. Genes added to or excluded from the list after exclusion of cell-cycle associated genes are also shown.

Extended Data Fig. 7 Gene expression profiling for cultured WT and ΔDNSe DN3 cells.

(a) Hierarchical clustering of genes expressed in cultured DN3, ex vivo T cell progenitors and innate lymphoid progenitors is shown. Genes with > 50 reads in at least one data set were clustered. (b) Enrichment of ΔDNSe deregulated genes in the Notch Signaling (Hallmark Notch Signaling) and Myc (Hallmark Myc target V1) pathways are shown by GSEA. Genes ranked from most upregulated (left end) to most downregulated (right end) in ΔDNSe relative to WT DN3 cells are plotted on the X axis. Enrichment profiles of genes are plotted on Y axis and calculated enrichment score (ES) and FDR are shown. (c) The full expression panel for Trbv, Trbc, Trbd and Trbj genes in cultured DN3 cells as determined by RNAseq is shown (mean + /– SEM. * adj. p < 0.01 Expression data for Trbv genes is also shown in Fig. 6e. Reduction in expression of genes encoding Vβ region (Trbv) were detected in ΔDNSe relative to WT DN3 with the exception of Trbv12-2 and Trbv13-2. Expression of genes encoding Cβ, Dβ and Jβ regions (Trbc, Trbd, and Trbj) were similar or even higher in ΔDNSe DN3, suggesting that ΔDNSe DN3 have already undergone D-J rearrangement. (d) Heatmaps based on significance of functional pathway enrichment (-log10(pval)) are shown for down-regulated genes classified as a DN3 and ΔDNSe-dwn signatures as shown in Fig. 6c. (e) Expression of genes related to innate lymphoid lineages in cultured DN3 cells as determined by RNAseq is shown (mean + /– SEM). * adj. p < 0.01. Data for cultured DN3 cells were generated from three independent experimental groups for each genotype. Data for ex vivo ETP, DN2 and DN3 were generated in this study as in Fig. 1. Data for ILC2p and NKp were obtained from published datasets (GEO: GSE77695) as in Fig. 6 and Extended Data Fig. 5.

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Kashiwagi, M., Figueroa, D.S., Ay, F. et al. A double-negative thymocyte-specific enhancer augments Notch1 signaling to direct early T cell progenitor expansion, lineage restriction and β-selection. Nat Immunol 23, 1628–1643 (2022). https://doi.org/10.1038/s41590-022-01322-y

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