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TCF1–LEF1 co-expression identifies a multipotent progenitor cell (TH2-MPP) across human allergic diseases

Nature Immunology (2024)Cite this article

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Abstract

Repetitive exposure to antigen in chronic infection and cancer drives T cell exhaustion, limiting adaptive immunity. In contrast, aberrant, sustained T cell responses can persist over decades in human allergic disease. To understand these divergent outcomes, we employed bioinformatic, immunophenotyping and functional approaches with human diseased tissues, identifying an abundant population of type 2 helper T (TH2) cells with co-expression of TCF7 and LEF1, and features of chronic activation. These cells, which we termed TH2-multipotent progenitors (TH2-MPP) could self-renew and differentiate into cytokine-producing effector cells, regulatory T (Treg) cells and follicular helper T (TFH) cells. Single-cell T-cell-receptor lineage tracing confirmed lineage relationships between TH2-MPP, TH2 effectors, Treg cells and TFH cells. TH2-MPP persisted despite in vivo IL-4 receptor blockade, while thymic stromal lymphopoietin (TSLP) drove selective expansion of progenitor cells and rendered them insensitive to glucocorticoid-induced apoptosis in vitro. Together, our data identify TH2-MPP as an aberrant T cell population with the potential to sustain type 2 inflammation and support the paradigm that chronic T cell responses can be coordinated over time by progenitor cells.

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Fig. 1: Single-cell atlas of GATA3+ lymphocytes across human allergic diseases.
Fig. 2: Aberrant maintenance of TCF1 and LEF1 expression by TH2 cells infiltrating human nasal polyps.
Fig. 3: Pseudobulk analysis identifies transcriptional signatures of TH2-MPP and circulating blood memory cells.
Fig. 4: Metabolic heterogeneity of type 2 lymphocytes.
Fig. 5: Single TCR clones exhibit clonal expansion and cross-cluster overlap along the TH2-MPP differentiation trajectory.
Fig. 6: TH2-MPP differentiation modeled in vitro.
Fig. 7: TH2-MPP cells couple self-renewal with effector differentiation to maintain the TH2 lineage.

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Data availability

The newly generated scRNA-seq data have been uploaded to the GEO repository (GSE255544)80. Other data sets used in this study include: asthma (GSE164015 and GSE193816), atopic dermatitis (GSE153760 and GSE158432), allergic conjunctivitis (GSE203217), bullous pemphigoid (https://zenodo.org/deposit/5228495), CRSwNP (GSE179292), EoE (GSE175930), lichen planus (https://zenodo.org/deposit/5228495), lymphedema (GSA HRA000901) and UC (Broad Single Cell Portal, SCP259).

Code availability

All analysis and figures were generated using publicly available software packages. No custom code was used in this study.

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Acknowledgements

This work was supported by National Institutes of Health grants U19AI095219, T32AI007306 and K23AI139352, the Lupus Research Alliance, the Food Allergy Science Initiative (FASI) at the Broad Institute, the Arthritis National Science Foundation Vic Braden Family Fellowship and generous support from the Vinik and Karol Families. We are grateful to the people who provided the biological samples used in this study. We thank M. Brenner (Brigham & Women’s Hospital) for providing the DN32 cell line. We thank A. Chicoine, J. Case and the Brigham and Women’s Hospital Center for Cellular Profiling Flow Cytometry Core for assistance with cell sorting, as well as G. Watts, Z. Zhu and the BWH Center for Cellular Profiling Single Cell Genomics Core, and the Dana–Farber Cancer Institute Center for Cancer Genomics Services Core for assistance with single-cell transcriptomics. We appreciate insightful comments on the paper provided by D. Schaefer-Babajew and M. ElTanbouly, Rockefeller University, and thank G. Manoim for assistance with streamlining import of public sequencing data sets.

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Authors and Affiliations

  1. Division of Allergy and Clinical Immunology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Radomir Kratchmarov, Wenqin He, Xiaojiong Jia, Tessa Ryan, Alanna McGill, Daniel F. Dwyer, Joshua A. Boyce, Kathleen M. Buchheit, Tanya M. Laidlaw & Patrick J. Brennan

  2. Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Sarah Djeddi & Maria Gutierrez-Arcelus

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Sarah Djeddi & Maria Gutierrez-Arcelus

  4. Division of Rheumatology, Inflammation, Immunity, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Garrett Dunlap & Deepak A. Rao

  5. Center for Immunology and Inflammatory Diseases and Food Allergy Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Caitlin M. Burk & Wayne G. Shreffler

  6. Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Jessica R. Allegretti

  7. Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Raghu P. Kataru & Babak J. Mehrara

  8. Division of Plastic and Reconstructive Surgery, Department of Surgery, Brigham and Women’s Hospital, Harvard University, Boston, MA, USA

    Erin M. Taylor & Shailesh Agarwal

  9. Massachusetts Eye & Ear Institute, Harvard Medical School, Boston, MA, USA

    Neil Bhattacharyya

  10. Division of Otolaryngology Head and Neck Surgery, Department of Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Regan W. Bergmark, Alice Z. Maxfield, Stella Lee & Rachel Roditi

  11. Center for Surgery and Public Health, Brigham and Women’s Hospital, Boston, MA, USA

    Regan W. Bergmark

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  1. Radomir Kratchmarov
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Contributions

R.K. performed the majority of experiments and analyzed data. R.K. and P.J.B. designed the project, supervised experiments and analysis and wrote the paper. S.D., G.D., D.F.D., D.A.R. and M.G.-A. analyzed computational data. X.J., W.H, T.R. and A.M. performed experiments and analyzed data. C.M.B., J.R.A., R.P.K., B.J.M., E.M.T., S.A., N.B., R.W.B., A.Z.M., S.L., R.R., W.G.S., T.M.L., K.M.B. and J.A.B. managed and contributed critical resources and aided in experimental design. All authors edited the paper.

Corresponding author

Correspondence to Patrick J. Brennan.

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Competing interests

S.L. reports clinical trial funding from Sanofi Aventis Regeneron, GlaxoSmithKline, AstraZeneca and OptiNose and participated on advisory boards for GlaxoSmithKline, AstraZeneca, Sanofi Aventis, Regeneron, Genentech and Novartis. R.W.B. reports unrelated clinical trial funding from I-Mab Biopharma (CTA: HLW-ALZ-NAS-001). N.B. reports consultant fees for unrelated epidemiology research for GlaxoSmithKline. D.A.R. reports consultant fees for unrelated work from GlaxoSmithKline, AstraZeneca, Pfizer, HiFiBio Therapeutics, Scipher Medicine and Bristol-Myers Squibb and grant support unrelated to this work from Janssen, Merck and Bristol-Myers Squibb. D.F.D. reports unrelated consultant fees from Celldex Therapeutics. The other authors declare no competing interests.

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Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: L. A. Dempsey, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 Transcriptomic characterization of key GATA3-expressing lymphocytes.

a, Violin plots showing expression level of key genes used to annotate UMAP clusters. b, Volcano plot of differentially expressed genes between peTh2/Th2A and ILC2 (left) and between γδ T cell clusters (right) from pseudobulk analysis and linear mixed model. Highlighted transcripts have absolute β > 1 and adjusted p-value <0.05 by False Discovery Rate (FDR). c, Violin plots showing expression level of key TRM-associated transcripts. d, Volcano plot of differentially expressed genes between TFH and Memory-like (left) and peTh2/Th2A and Memory-like clusters (right). Highlighted transcripts have absolute β > 1 and adjusted p-value <0.05 by FDR from pseudobulk analysis and linear mixed model.

Extended Data Fig. 2 Gene Set Enrichment Analysis of GATA3-expressing αβ T cell clusters.

Pseudobulk analysis was performed to identify marker transcripts differentiating each cluster from the integrated scRNAseq atlas dataset using a linear mixed model and controlling for dataset as random effect. Gene set enrichment analysis was then performed for each cluster based on biological process annotations. The top 10 pathways based on p-value and GeneRatio (count of genes in the core enrichment / total count of genes in this pathway) with a positive enrichment score and an adjusted p < 0.05 are displayed.

Extended Data Fig. 3 Pseudotime differentiation trajectories of memory-like cluster.

a, UMAP of αβ T cells from the tissue atlas (Fig. 1) after re-clustering. b, Violin plots of key transcripts used to annotate clusters. c, Left: Trajectories inferred by Slingshot algorithm superimposed on UMAP. Right: Three distinct lineages with cells from each lineage colored by pseudotime.

Extended Data Fig. 4 Transcription factor, cytokine, and surface marker expression profiles of GATA3+ T cell subsets in nasal polyps.

a, Quantification of GATA3+ T cells in healthy nasal mucosa, chronic rhinosinusitis without nasal polyps (CRSsNP), and chronic rhinosinusitis with nasal polyps (CRSwNP). For healthy mucosa and CRSsNP, N = 7 donors, for CRSwNP N = 14 donors. b, Identification of Th2 by CRTh2, GATA3, IL-4, IL-5, and IL-13 expression. c, Representative staining and quantification of IL-4, IFN-γ, IL-17, and CD38 for identification of T helper subsets. N = 15. d, Quantification of TCF1+/LEF1+ cell frequency in T helper subsets from indicated diseases. For EoE, N = 10 donors, for UC N = 6 donors, for Lymphedema, N = 3 donors. e, Left: Representative histogram of FOXO1 expression by CD8+ T cells (grey), GATA3-CD4+ T cells (blue), and GATA3+CD4+ T cells (black) in nasal polyps. Right: quantification of FOXO1 MFI for indicated subsets. N = 6 donors. f, Correlation of blood absolute eosinophil count (left, N = 15 donors), total serum IgE (middle, N = 10 donors), and nasal eosinophil cationic protein (ECP) (right, N = 21 donors) with TCF1/LEF1 co-expression by nasal polyp Th2 cells. p < 0.05. Pearson correlation coefficient. For all plots, ****p < 0.0001. ***p < 0.001. **p < 0.01. *p < 0.05. One-way ANOVA with Holm-Sidak correction for multiple comparisons. For all panels with box and whisker plots, box range denotes 25th to 75th percentiles, whiskers show minimum and maximum values.

Extended Data Fig. 5 Expression of pathogenic effector markers, inhibitory receptors associated with exhaustion, and transcription factors by Th2 in nasal polyps.

a, TRM marker expression by nasal polyp T cells. Left: Representative plots of CD69 and CD103 expression by total CD4+ and CD8+ T cells. Middle: Quantification of indicated subsets by CD69 and CD103 expression. Right: Frequencies of CD103 expression by indicated subsets of GATA3+CD4+ T cells. N = 12 donors. b, Expression of surface activation and inhibitory receptors by GATA3+CD4+ T cell subsets. CD69: N = 15 donors, CD38: N = 6 donors, PD-1: N = 6 donors, CD39: N = 8 donors, TIGIT: N = 6 donors. c, Expression of peTh2/Th2A markers by GATA3+CD4+ T cell subsets. CD200R: N = 10 donors, CD161 N = 9 donors, CD49d: N = 7 donors. d, Expression of TCF1 and LEF1 by GATA3+CD4+ T cell subsets. TCF1: N = 8 donors. LEF1: N = 11 donors. All panels: ****p < 0.0001. ***p < 0.001. **p < 0.01. *p < 0.05. n.s. not significant. One-way ANOVA with Tukey’s correction for multiple comparisons. For all panels with box and whisker plots, box range denotes 25th to 75th percentiles, whiskers show minimum and maximum values. For all panels, red line = FOXP3+, black line = CRTh2+, and blue line = CRTh2- subsets respectively.

Extended Data Fig. 6 scRNAseq identifies distinct T cell clusters in human nasal polyps and paired peripheral blood.

a, Representative gating strategy for sorting indicated subsets for scRNAseq. P1: ILCs (CD45+CD127+ lin-), P2: CD8+ T cells, P3: CRTh2+ Th2, P4: CCR4-CRTh2-CD4+, P5: CCR4+CXCR3- Th2. b, Expression of GATA3 and CCR4 by CD4+ T cells in nasal polyps. Plot is representative of 3 independent tissue donors. c, UMAP of nasal polyp lymphocytes split by donor. d, Heatmap of top 20 differentially expressed genes for each cluster with >0.5 log2 fold change and padjusted <0.05 cutoffs, Right column: key genes used to annotate clusters highlighted. e, Violin plots of expression level of indicated genes used to annotate UMAP clusters. f, Violin plots of key subset-defining surface markers detected by DNA-barcoded antibody labeling. g, Violin plot of SLC2A3, SLC16A3, ACSL4, and FFAR3 transcript expression by nasal polyp lymphocytes.

Extended Data Fig. 7 Iterative expansion index analysis for nasal polyp T cell clusters.

Left: Iterative pseudotime lineage tracing for each specified origin node. Right: Average expansion indices +/− SEM for clones with overlap between each specified root node and the specified lineages. Positive values indicate clonal expansion in the putative effector cluster relative to root node; negative values indicate clonal expansion in the root cluster. Size of dots are scaled relative to the number of clones in that lineage. ** p < 0.01. n.s. not significant. One sample, two tailed Wilcoxon test.

Extended Data Fig. 8 scTCRseq analysis of lymphocytes in atopic dermatitis.

a, UMAP of integrated TCR-expressing T cells in atopic dermatitis. N = 5,622 clonotyped cells. b, Dot Plot of lineage-defining transcripts used to identify clusters. c, Left: FeaturePlot of GATA3 expression. Right: Joint Density function plot of indicated transcripts used to identify putative progenitor and effector clusters. d, Circos plot of clonal overlap between each cluster. Each overlapping clone is connected by a line, with heavier line weights indicative of more overlapping clones. e, Upper Left: Pseudotime lineages with Th2-MPP cluster as starting node projected onto UMAP space. Right: UMAP space split by lineage, with cells from each lineage colored by pseudotime. Lower left: Average expansion indices +/− SEM for clones with overlap between Th2-MPP and the specified lineages. Positive values indicate clonal expansion in the putative effector cluster relative to Th2-MPP; negative values indicate clonal expansion in the Th2-MPP cluster. Size of dots are scaled relative to the number of clones in that lineage. ****p < 0.0001. **p < 0.01. n.s. not significant. One sample, two tailed Wilcoxon test. N = 9 donors integrated across datasets.

Extended Data Fig. 9 Iterative expansion index analysis for atopic dermatitis T cell clusters.

Left: Iterative pseudotime lineage tracing for each specified origin node. Right: Average expansion indices +/− SEM for clones with overlap between each specified root node and the specified lineages. Positive values indicate clonal expansion in the putative effector cluster relative to root node; negative values indicate clonal expansion in the root cluster. Size of dots are scaled relative to the number of clones in that lineage. ** p < 0.01. n.s. not significant. One sample, two tailed Wilcoxon test.

Extended Data Fig. 10 Modeling Th2 heterogeneity, differentiation, and persistence.

a, Expression of IFN-γ and IL-13 in Th1 and Th2 cultures at day 7 of differentiation. Results are representative of 4 independent donors. b, Expression of IL-13 and IL-4 at day 7, day 14, and day 21 of culture. c, Expression of CD38 and CD69 versus cell division by Th2 cells at day 4 of differentiation. d, Left: Representative plots of GATA3 and PPARγ expression. Right: Representative histograms of CD49d expression by CD27+CRTh2- (red), CRTh2+CD161- (blue), and CRTh2+CD161+ (black) subsets. e, In vitro sorting strategy: Representative histogram of TCF1 expression by CD27+CRTh2- progenitor cells (black) and CD27- effector Th2 cells in vitro (red). f, Differentiation of TCF1-lo cells requires TCR stimulation. CD27+CRTh2- progenitor cells were sorted, labeled with CTV, and cultured for additional 7 days with (right) or without (left) TCR stimulation. Upper: Expression of TCF1 versus cell division (TCF1-lo cells denoted by gate). Lower: Expression of CD27 versus cell division (CD27-lo cells denoted by gate). g, Upper: Steady state expression of IL-13 in CD27-hi and CD27-lo subsets. Lower: Steady state expression of TCF1 and LEF1 in Th2-MPP. (a-g) Results are representative of 3 independent donors. h, Proliferative capacity of Th2-MPP and peTh2/Th2A cells. Representative plots of GATA3 versus cell division. I, Sorting strategy for isolation of Th2-MPP for Treg and TFH cell fate analysis. j, Left: Expression of TCF1 and LEF1 following Th2-MPP differentiation. Right: Expression of cell fate markers CRTh2, CD161, FOXP3, and PD-1 by TCF1 and LEF1-hi/lo subsets. k, Expression of TSLP and IL-33 by basal epithelial cells from patients with CRSwNP or CRSsNP. **p < 0.01 Unpaired, two tailed T test with Welch’s correction. N = 8 donors. l, Survival of CD27+ Th2 progenitors, sorted as in Fig. 6b, in culture for 10 days with IL-2, IL-33, or TSLP. Representative plots of LEF1 expression versus viability dye. Results are representative of 4 independent donors. m, Left: Representative plots of CRTh2 and TSLPR expression by GATA3+CD4+ T cells in nasal polyps. Right: Quantification of TSLPR expression by indicated GATA3+CD4+ T cell subsets **p < 0.01 *p < 0.05, 2-way ANOVA. N = 7 donors. n, Violin plot of IL1RL1, NR3C1, and FKBP5 expression by nasal polyp lymphocytes. For all panels with box and whisker plots, box range denotes 25th to 75th percentiles, whiskers show minimum and maximum values.

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Kratchmarov, R., Djeddi, S., Dunlap, G. et al. TCF1–LEF1 co-expression identifies a multipotent progenitor cell (TH2-MPP) across human allergic diseases. Nat Immunol (2024). https://doi.org/10.1038/s41590-024-01803-2

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