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Spatial microniches of IL-2 combine with IL-10 to drive lung migratory TH2 cells in response to inhaled allergen

Nature Immunology volume 25, pages 2124–2139 (2024)Cite this article

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Abstract

The mechanisms that guide T helper 2 (TH2) cell differentiation in barrier tissues are unclear. Here we describe the molecular pathways driving allergen-specific TH2 cells using temporal, spatial and single-cell transcriptomic tracking of house dust mite-specific T cells in mice. Differentiation and migration of lung allergen-specific TH2 cells requires early expression of the transcriptional repressor Blimp-1. Loss of Blimp-1 during priming in the lymph node ablated the formation of TH2 cells in the lung, indicating early Blimp-1 promotes TH2 cells with migratory capability. IL-2/STAT5 signals and autocrine/paracrine IL-10 from house dust mite-specific T cells were essential for Blimp-1 and subsequent GATA3 upregulation through repression of Bcl6 and Bach2. Spatial microniches of IL-2 in the lymph node supported the earliest Blimp-1+TH2 cells, demonstrating lymph node localization is a driver of TH2 initiation. Our findings identify an early requirement for IL-2-mediated spatial microniches that integrate with allergen-driven IL-10 from responding T cells to drive allergic asthma.

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Fig. 1: Blimp-1 kinetics in allergen-specific CD4+T cells in response to inhaled HDM.
Fig. 2: Autocrine/paracrine IL-10 from allergen-specific T cells supports Blimp-1 and TH2 cells in the lung.
Fig. 3: TH2 initiation occurs early in the medLN.
Fig. 4: Early expression of Blimp-1 is required for TH2 cells in the lung.
Fig. 5: Single-cell transcriptomics identifies distinct subpopulations of TFH and TH2 cells.
Fig. 6: IL-2/STAT5 supports Blimp-1 and GATA3 in nascent TH2 cells indirectly via repression of Bcl6 and Bach2.
Fig. 7: Spatial imaging and transcriptomics identify TH2 cells localized to T–B border.
Fig. 8: Spatial activation microniches of IL-2 support Blimp-1 in nascent TH2 cells.

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Data availability

All scRNA-seq and Spatial Transcriptomics data described in the paper have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database and are accessible through the GEO SuperSeries accession number GSE245112. Public data analyzed in this paper are also available under the GEO accession code GSE77656 (ref. 22). Source data are provided with this paper.

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Acknowledgements

We are grateful to R. Peralta and G. M. Delgoffe for experimental advice in CRISPR–Cas9 assays. We thank T. W. Hand and all members of the Poholek Lab for critical reading of the manuscript and technical support. We thank the UPMC Children’s Hospital of Pittsburgh Flow Core, the Pitt Single Cell Core, especially T. Tabib, the Health Sciences Sequencing Core and UPMC Genome Center, the University of Pittsburgh Center for Research Computing and the Center for Biologic Imaging. This work was supported by the Research Advisory Committee at UPMC Children’s Hospital of Pittsburgh (K.H.), Clinical and Translational Science Institute Pilot Award (J.D. and A.C.P.), American Lung Association Innovation Award (A.C.P.) and the US National Institutes of Health grants DP2AI164325 (J.D.) and AI153104 and AI156093 (A.C.P.).

Author information

Authors and Affiliations

  1. Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Kun He, William A. MacDonald, Zhongli Xu, Wei Chen & Amanda C. Poholek

  2. Center for Systems Immunology, Department of Immunology, University of Pittsburgh, Pittsburgh, PA, USA

    Hanxi Xiao, Isha Mehta, Akash Kishore, Augusta Vincent, Jishnu Das & Amanda C. Poholek

  3. Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Hanxi Xiao, Isha Mehta, Akash Kishore, Augusta Vincent & Jishnu Das

  4. Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, USA

    Hanxi Xiao

  5. Health Sciences Sequencing Core, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    William A. MacDonald

  6. School of Medicine, Tsinghua University, Beijing, China

    Zhongli Xu

  7. Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Anuradha Ray

  8. Department of Biostatistics, University of Pittsburgh School of Public Health, Pittsburgh, PA, USA

    Wei Chen

  9. Department of Pathology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA

    Casey T. Weaver

  10. Laboratory of Mucosal Immunology, VIB-UGent Center for Inflammation Research, Ghent University, Ghent, Belgium

    Bart N. Lambrecht

  11. Department of Internal Medicine and Pediatrics, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium

    Bart N. Lambrecht

  12. Department of Pulmonary Medicine, Erasmus MC, Rotterdam, the Netherlands

    Bart N. Lambrecht

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Contributions

K.H. conceived the project, designed and performed the experiments, analyzed the data and wrote the paper. W.A.M. designed and performed all spatial transcriptomics assays. H.X., I.M., A.K., A.V. and Z.X. performed bioinformatics analysis of scRNA-seq, scATAC-seq and spatial transcriptomics experiments, A.R. provided expertise and scientific insights. W.C. and J.D. provided oversight of bioinformatics and scientific insights. C.T.W. and B.L. provided crucial reagents and scientific insights. A.C.P. helped to conceive and design experiments, analyzed the data, cowrote the paper, provided overall direction and acquired funding.

Corresponding author

Correspondence to Amanda C. Poholek.

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Nature Immunology thanks Mark Kaplan and the other, anonymous, reviewer(s) 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 1DER T cells proliferation kinetics in medLN and lung.

a, naïve 1DERYFP CD4 + T cells were enriched and analyzed for purity before adoptive transfer experiments. b-c, Proportion and kinetics of Ki67 expression in 1DER T cells from medLN and lung after the HDM administration for 3d (n = 6), 5d (n = 9) and 10d (n = 6). Each point represents one individual sample. Data are shown as means ± SD above and presented two independent experiments (b-c). Kruskal-Wallis one−way ANOVA test (c). *P = 0.0135, **P = 0.0017, ****P < 0.0001.

Source data

Extended Data Fig. 2 Cellular phenotype and source of IL-10 driving Th2 cells in the lung.

a, Representative flow plots (a, left) and percentage (a, right, n = 17 in CRISPRcontrol, n = 14 in CRISPRSTAT3) of Blimp-1 YFP+ cells within Th2 cells in the lung. b-e, flow cytometry analysis of Th2 cells(gated on live TCRβ + CD4+FoxP3-) in the lung (b) isolated from control (IL-10f/fCD11cCre−) or IL-10CD11cCre (IL-10f/fCD11cCre+) animals i.n. immunized with HDM. c-d, percent of infiltrating eosinophils (c) and monocytes (d) in the BAL. e, Percent of effector CD4 + T cells expressing Blimp-1 in the lung (gated on live TCRβ + CD4+FoxP3-). Blimp-1f/fCD4Cre+ (Blimp-1 cKO) mice were used as gating control for Blimp-1 detection. b-e, control (IL-10f/fCD11cCre−, n = 19), IL-10CD11cCre (IL-10f/fCD11cCre+, n = 18). f-i, flow cytometry analysis of Th2 cells (gated on live TCRβ + CD4+FoxP3-) in the lung (f) isolated from control (IL-10f/fCD19Cre−) or IL-10CD19Cre (IL-10f/fCD19Cre+) animals i.n. immunized with HDM. g, percent of infiltrating eosinophils (g) and monocytes (h) in the BAL. i, Percent of effector CD4 + T cells expressing Blimp-1 in the lung (gated on live TCRβ + CD4+FoxP3-). Blimp-1 cKO mice were used as gating control for Blimp-1 detection. For f, i, control (IL-10f/fCD19Cre−, n = 16) or IL-10CD19Cre (IL-10f/fCD19Cre+, n = 20). For g-h, control (IL-10f/fCD19Cre−, n = 11) or IL-10CD19Cre (IL-10f/fCD19Cre+, n = 13). j, number of infiltrating monocytes in the BAL from control (IL-10f/fCD4Cre−, n = 18) or IL-10CD4Cre (IL-10f/fCD4Cre+, n = 15) animals i.n. immunized with HDM. k-l, experimental setup of naïve IL-10CD4Cre-1DERYFP (CD45.1/2+) CD4 + T cells transferred into IL-10 KO (CD45.2+) recipients followed by 5d i.n. HDM immunization. Flow analysis (k) and absolute cell number of Th2 cells (l, left, n = 4) and Blimp-1 YFP+ expression within Th2 cells (l, right, n = 4) isolated from lung after HDM. m, experiment diagram for 50:50 mixes of 1DERYFP T cells for data in Fig. 2 (k-o). Each point represents one individual sample. Data are shown as means ± SD above and presented two independent experiments (l) or three independent experiments (a, b-e, f-i, j). Two-tailed unpaired Mann-Whitney t test (a, l). a: ****P < 0.0001; l (left): p = 0.0286, l (right): p = 0.0286.

Source data

Extended Data Fig. 3 Blimp-1 + 1DER effector T cells migrate from medLN to the lung.

a-c, Histograms of transcription factor RORγt and T-bet from Teff population (live TCRβ + CD4 + CD45.1 + CD45.2+FoxP3- cells) in 1DER T cell isolated from medLN after 3d (n = 8) and 5d (n = 9) HDM. d-e, naïve CD451 + CD45.2 + 1DERYFP T cells were adoptive transferred into CD45.2+ recipients followed by total 3 (doses) immunization. FTY720 was given by i.p. 6 h after the 2nd dose of HDM and was given 6 h before the 3rd dose of HDM (n = 7). DMSO injection was as control group (n = 6). Representative flow cytometry and absolute cell number on YFP+ cells showing Blimp-1 expression from Teff population in the medLN (d) and lung (e). f, 1DER TCR Tg mice were crossed to IL-10 GFP mice (1DERGFP) and naïve 1DERGFP CD4 T cells (CD45.1/2+) were transferred into host (CD45.2+), followed by 3d of HDM immunization (i.n). Representative flow plots of IL-10GFP gated on 1DERGFP T cells and percentage of IL-10-expressing (GFP+) Teff and TFH cells in the medLN. n = 5. g, Sensitization and challenge protocol using HDM. Mouse lung tissue was harvested 3 min after antibody anti-Thy1.2 was injected by i.v. Tissue resident CD4 + T cells were identified by CD44+Thy1.2- (h-i) and Blimp-1 YFP+ cells (j) were identified on d30 from the lung (n = 3 for unimmunized group, n = 2 for HDM group). k-l, Representative flow cytometry of Blimp-1 expression from lung analyzed on d23 after immunization. Gated from TCRβ + CD4+FoxP3- effector cells. l, Frequency of Blimp-1 expression among effector cells. n = 2 in Blimp-1CD4Cre, n = 12 in iBlimp-1Δ/Δ (day-4 ~ 0), n = 7 in iBlimp-1Δ/Δ (day-1 ~ 3), n = 12 in iBlimp-1Δ/Δ(day1 ~ 5), n = 7 in iBlimp-1Δ/Δ(day6 ~ 10), n = 5 in iBlimp-1Δ/Δ(day11 ~ 15), n = 20 in iBlimp-1+/+, n = 9 in Blimp-1+/++vehicle, n = 9 in Blimp-1Δ/Δ +vehicle. Each point represents one individual sample. Data are shown as means ± SD above and presented one independent experiment (j), two independent experiments (b-f) or three independent experiments (l). Two-tailed unpaired Mann-Whitney t test (d-f), Kruskal-Wallis one-way ANOVA test (l). d: **p = 0.0023, e: ***P = 0.006; f: **p = 0.0079; l: *p < 0.036, **p < 0.0079.

Source data

Extended Data Fig. 4 CITE-seq and response with CCR8 deficiency.

a, UMAP visualization to identify labeled CD45.1 + 1DER T cells. b, Pseudotime trajectory analysis of CD4 T clusters. c, Pseudotime trajectory analysis using Slingshot assay. d-g, Naïve 1DERYFP (CD45.1/2+) CD4 + T cells were electroporated with CRISPR sgRNA/Cas9 RNPs targeting CCR8 or scramble control and immediately transferred into WT (CD45.2+) recipients immunized (24 h later) with HDM for 5d. (d, f) flow cytometry plots, absolute number of Th2 cells (e, n = 11) and (g, n = 11) Blimp-1 YFP+ cells within Th2 cells from 1DERYFP T cells in the lung. Data are shown as means ± SD and presented two independent experiments. Two-tailed unpaired Mann-Whitney t test (e, g), p > 0.05.

Source data

Extended Data Fig. 5 Impacts of STAT5a, STAT5b Bcl6 and Bach2 on 1DER Th2 formation and Blimp-1.

a, Representative flow plots of TFH (CXCR5 + PD-1+) T cells showing IL-2Rα and Bcl6 expression cells 3d after HDM in the medLN. b-c, Representative flow plots and percentage of pSTAT5+ (blue dots) in IL-2Rα + (red dots) 1DER T cells after 24 h (n = 4), 48 h (n = 7) and 72 h (n = 7). Naïve CD45.1 + 1DER T cells (gray) were used as gating control. d, schematic model of hypothesis. e-j, naïve 1DERYFP (CD45.1/2+) CD4 + T cells were electroporated with targeting or nontargeting sgRNA/Cas9 RNPs for mouse STAT5a or STAT5b. e-f, Representative flow plots representative flow plots and percent data shows 1DER cells in the medLN (e, g, left) and lung (f, g, right) after STAT5a or STAT5b deficiency on d5. h-j, percent of Treg (h), TH1 (i) and TH17 (j) cells from 1DER T cells in the lung gated on live TCRβ + CD4 + CD45.2 + CD45.1 + . For g-j, n = 11 in CRISPRcontrol, n = 8 in CRISPRSTAT5a, n = 8 in CRISPRSTAT5b. k, Representative flow plots (k, left) and percentage (k, right, n = 13 in CRISPRcontrol, n = 5 in CRISPRBcl6) of IL-2Rα+ cells in the absence of Bcl6 at 72 h in the medLN. l-n, representative flow plots (l, left) and percentage of IL-2Rα+ cells (l, right, n = 10 in CRISPRcontrol, n = 9 in CRISPRBach2) in the absence of Bach2 from Teff isolated from medLN at 72 h. Percentages of GATA3+ (m, left) and Bcl6+ (m, right, n = 10 in CRISPRcontrol, n = 9 in CRISPRBach2) cells from medLN in the absence of Bach2. n, T-Bet + , RORγt + , TFH and Treg of 1DER T cells isolated from medLN 3d after HDM in the absence of Bach2 (n = 8 in CRISPRcontrol, n = 9 in CRISPRBach2). Each point represents one individual sample. Data are shown as means ± SD above and presented two independent experiments (c, g-n). Two-tailed unpaired Mann-Whitney t test (k, l, n), Kruskal-Wallis one-way ANOVA test (c, h). * P < 0.05, ** P < 0.01, *** P < 0.001, ****P < 0.0001. Specific P values are as follows: c: **p = 0.0073; h: ***p = 0.0007; n: *p = 0.0206 for Tbet+, *p = 0.0152 for RORγt+.

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Extended Data Fig. 6 IL-2-STAT5 supports Blimp-1 and GATA3 in nascent Th2 cells by suppressing Bcl6 and Bach2.

Naïve 1DER T cells (CD45.1/2+) were labeled with CTV prior to adoptive transfer into CD45.2+ recipients followed by 3d HDM i.n. immunization. a, Representative RNA flow plots of Prdm1 and Bach2 expression by CTV of 1DER T cells concatenated from 7 samples (n = 7). b, Naïve 1DER T cells were processed for CRISPR knockout targeting STAT5b sgRNA or scramble control sgRNA followed by culture for 72 h in the presence of anti-CD3, anti-CD28, then cells were collected for qPCR assay (n = 4 biological samples). naive 1DER T cells were used as control. c-d, Representative flow plots (c), percentage of Blimp-1 YFP+ (d) and Bcl6+ cells (e) at 72 h in the medLN in the absence of STAT5b and/or Bach2. For d-e, n = 7 in CRISPRcontrol, n = 5 in CRISPRSTAT5b, n = 7 in CRISPRBach2, n = 5 in CRISPRSTAT5b+Bach2. f-h, Representative flow plots (f), percentage of Blimp-1 YFP+ (g) cells at 72 h in the medLN in Bach2-deficient alone (n = 5), STAT5b and Bach2-deficient (n = 4), Bcl6 and Bach2-deficient (n = 7) or with IL-2 blockade (n = 4). h, Naïve 1DER T cells were processed for CRISPR knockout targeting Bach2 or scramble control sgRNAs followed by culture for 72 h in the presence of anti-CD3, anti-CD28, and additional cytokine conditions [IL-2 (100U) or anti-IL2 (20 µg/ml) or IL-10 (20 µg/ml)] as indicated above (h, left), percentage of Blimp-1 YFP+ cells (h, right) cells at 72 h. Data was collected from 3 independent culture experiments. naive 1DER T cells were used as control for gating. i, schematic model summarizing the molecular pathways for Th2 cell initiation to inhaled allergen. Each point represents one individual sample. Data are shown as means ± SD above and presented two independent experiments (d, e, g). Kruskal-Wallis one-way ANOVA test (d, e, g). * P < 0.05, ** P < 0.01, ****P < 0.0001. Specific P values are as follows: d: CRISPRcontrol versus CRISPRBach2 = 0.074, CRISPRSTAT5b versus CRISPRBach2+STAT5b = 0.0379, ****P < 0.0001; e: **p = 0.007; g: CRISPRBach2 versus CRISPRBach2+STAT5b = 0.0138.

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Extended Data Fig. 7 Spatial imaging and transcriptomics identifies factors that underlie Th2 and TFH cell localization.

a-d, The medLN were collected at indicated time points for immunofluorescence staining of B220 (blue), YFP (yellow) and CD45.1 (red). Scale bar, 500 µm. Zoom-in images show separately acquired regions of interest of the T cell zone based on CD4 and B220 staining. (d) Blimp-1-YFP + 1DER CD4 + T cells in T cell zone and B cell zone were quantified after 0d (n = 6), 3d (n = 9), 5d (n = 10) and 10d (n = 8). e-f, The medLN were collected for cross-section and immunofluorescence staining of B220 (blue), YFP (yellow), GATA3 (green) and CD45.1 (red) 3d after HDM immunization (left). Scale bar, 500 µm. Zoom-in images show separately acquired regions of interest of the T-B border. Quantification of the images showing the location of GATA3 + YFP + 1DER cells in T cell center or T-B border within the medLN (f, n = 8). g, genes in significant latent factor Z13 with high weighted expression in Th2 or TFH regions. h-m, Naïve CD45.1/2 + 1DER T cells were adoptive transferred into CD45.2+ recipients followed by HDM immunization for 3d (n = 2) or 5d (n = 2). medLN were collected for Curio Biosciences Slide Seeker protocol (Slide-seq). Serial sections were collected and stained for immunofluorescence of B220 (blue), CD4 (green), CD45.1 (red) and CD11c (magenta) (h, k, left). RCTD deconvolution was performed to assign regions with 7 cell types (i, l). RCTD, robust cell-type decomposition. Isolated regions mapped with Th2 transcriptomes or B cell transcriptomes were visualized by spatial location (j, m). n-o, Pathways analysis of genes identified in standalone latent factors enriched via SLIDE analysis performed on Slide-seq datasets at d3 (n) and d5 (o), the latent factors were identified for d3 (delta=0.085, lambda=1, spec=0.5, FDR = 0.1) and for d5 (delta=0.085, lambda=1, spec=0.1, FDR = 0.1). Each point represents one individual sample. Data are shown as means ± SD above and presented two (f) or three (d) independent experiments. Ordinary two-way ANOVA test (d), two-tailed unpaired Mann-Whitney t test (f). d: 0d versus 3d (T cell area)=0.0001, 0d versus 5d (T cell area)=0.0023; f: * P = 0.0281.

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Extended Data Fig. 8 Spatial imaging identifies IL-2+ microniches that impact Th2 cells.

a-b, Representative images as in (a) and (b) showing CD45.1 co-staining with RNA targets Cd19, Prdm1, Gata3, Il2 and Il2ra. B cell zone and T cell area were annotated by Cd19 and Cd4 RNA regions. c, Zoom-in images show separately acquired regions of interest of the T-B border for data in Fig. 7f. d-h, Naïve CD45.1/2 + 1DER T cells were adoptive transferred into CD45.2 + IL2eGFP reporter recipients followed by HDM immunization for 3d. Distance (µm) of IL-2eGFP+ cells to annotated B cell zone (d, n = 4) and frequency of IL-2eGFP+ cells distribution across medLN (e, n = 4). f, number of IL-2eGFP+ cells from immune cells in the medLN after 2d HDM i.n. immunization by flow cytometry assay (n = 3). naive unimmunized IL-2eGFP mouse were set as control (n = 2). g, Distance of GATA3 + , GATA3 + IL-2Ra + , IL-2Ra + 1DER T cells (CD45.1+) within a 50-μm-radius circle from IL-2GFP+ cells (n = 4). h, abundance of indicated FoxP3+ and GATA3 + CD4 + T cells surrounding IL2GFP+ cells within 50-μm-radius circle (n = 4). Data are shown as means ± SD above and representative of two independent experiments. Kruskal-Wallis one-way ANOVA test (g), Ordinary two-way ANOVA test (h). * P < 0.05, ** P < 0.01, ****P < 0.0001. Specific P values are as follows: g: CD45.1+IL2Ra+GATA3+ versus CD45.1+IL2Ra+=0.0098, CD45.1+GATA3+ versus CD45.1+IL2Ra+<0.0001; h: FoxP3+ (T cell zone) versus FoxP3+ (T-B border)=0.0333, ****P < 0.0001.

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Supplementary information

Supplementary Information

Supplementary information.

Reporting Summary

Supplementary Table 1

Gene list by cluster scRNA-seq.

Supplementary Table 2

Antibodies used in study.

Supplementary Data 1

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He, K., Xiao, H., MacDonald, W.A. et al. Spatial microniches of IL-2 combine with IL-10 to drive lung migratory TH2 cells in response to inhaled allergen. Nat Immunol 25, 2124–2139 (2024). https://doi.org/10.1038/s41590-024-01986-8

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