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Temporal and spatial atlas of eosinophil specialization across tissues

Nature Immunology volume 27, pages 364–375 (2026)Cite this article

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Abstract

Eosinophils are multifunctional granulocytes involved in immune regulation, metabolism and tissue repair; however, the extent of their heterogeneity across tissues and the principles governing their specialization are not well defined. Here, we present a single-cell transcriptomic and proteomic atlas of mouse eosinophils spanning immune, barrier and metabolic sites. By integrating transcriptional profiling, high-dimensional surface proteomics and in vivo fate mapping, we show that eosinophil identity is shaped by both tissue-derived cues and the duration of local residency. Long-lived eosinophils in the small intestine diversify into transcriptionally and phenotypically distinct subsets, whereas short-lived and intermediate-lived populations in the lungs and colon, respectively, remain comparatively uniform. We identify trajectory-associated surface markers that stratify eosinophil maturation from bone-marrow progenitors to long-term tissue-resident subsets. This atlas establishes a unified framework in which tissue-specific residency time drives eosinophil maturation and diversification, providing a molecular toolkit for resolving eosinophil states in health and disease.

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Fig. 1: Single-cell transcriptomics analysis identifies tissue-specific eosinophil states.
Fig. 2: High-dimensional protein profiling shows tissue-imprinted eosinophil identities.
Fig. 3: Residency-dependent lifespan stratifies eosinophils across tissues.
Fig. 4: Eosinophil heterogeneity emerges in the long-lived small intestinal niche.
Fig. 5: Residency-associated trajectories resolve eosinophil maturation states.
Fig. 6: Spatially compartmentalized diversification of intestinal eosinophils.

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Data availability

The scRNA-seq data reported in this paper have been deposited into the National Center for Biotechnology Information Sequence Read Archive under accession code PRJNA1301548. Processed scRNA-seq data can be viewed in figshare at https://doi.org/10.6084/m9.figshare.29816303.v1 (ref. 45). scRNA-seq datasets are available in an interactive portal: Cell-omics Data Coordinate Platform under dataset ID SCDS0000645. All other data supporting the findings of this study are available in the main text and supplementary files. Source data are provided with this paper.

Code availability

The code for scRNA-seq data analysis can be viewed in GitHub at https://github.com/liziyie/eosinophil_atlas.

Change history

  • 16 January 2026

    In the version of the article initially published, Fig. 6 erroneously contained the panel label “f” which has now been removed from the HTML and PDF versions of the article.

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Acknowledgements

This work was supported by the National Key R&D Program of China (no. 2023YFC2306300) to S.C., the National Natural Science Fund of China Research Fund for International Excellent Young Scientists (no. W2532025) to S.C., the National Natural Science Fund of China (no. 32470920) to S.C., The National Natural Science Fund for Excellent Young Scientists Fund Program (Overseas) to S.C. and the Shanghai Frontiers Science Center of Cellular Homeostasis and Human Diseases to S.C. L.G.N. is supported by the National Natural Science Foundation of China (grant no. 92374205) and Research Fund for International Senior Scientist (grant no. W2431020). We thank Shanghai Jiao Tong University School of Medicine and Shanghai Institute of Immunology for financial support. The sequencing data analysis was supported by the High-performance Computing Platform of the Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College.

Author information

Authors and Affiliations

  1. Shanghai Institute of Immunology, Department of Immunology and Microbiology, and Center for Human Translational Immunology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Yanan Hu, Sihao Qu, Xiaotong Yu, Jiaqian Xu, Ziyi Xu, Jialing He, Zhaoyuan Liu, Liming Lu, Bing Su, Florent Ginhoux & Svetoslav Chakarov

  2. National Key Laboratory of Immunity and Inflammation, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou, China

    Lianfu Wu & Ziyi Li

  3. Qinghai University, Xining, China

    Lianfu Wu

  4. Gustave Roussy Cancer Campus, Bâtiment de Médecine Moléculaire, Villejuif, France

    Wan Ting Kong, Camille Blériot & Florent Ginhoux

  5. Department of Obstetrics and Gynecology, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China

    Chong Wang

  6. School of Medicine, Westlake University, Hangzhou, China

    Lai Guan Ng

  7. Shanghai Immune Therapy Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Lai Guan Ng

  8. Institut Necker Enfants Malades (INEM), INSERM U1151, CNRS UMR 8253, Université de Paris Cité, IMMEDIAB Laboratory, Paris, France

    Camille Blériot

  9. Singapore Immunology Network (SIgN), A*STAR, Singapore, Singapore

    Florent Ginhoux

  10. Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore

    Florent Ginhoux

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  1. Yanan Hu
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Contributions

Conception: S.C. Discussion: S.C., Y.H., Z. Li, F.G., C.B., Z. Liu, L.G.N., B.S. and L.L. Research design and experimentation: S.C., Y.H., Z. Li, S.Q., J.X., X.Y., J.H. and Z.X. Data analysis: Y.H., Z. Li, L.W., S.Q., W.T.K., C.W. and S.C. Writing draft and editing: S.C. and Y.H. Project administration: S.C.

Corresponding authors

Correspondence to Ziyi Li or Svetoslav Chakarov.

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Nature Immunology thanks Ariel Munitz and the other anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Nick Bernard, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 Single-cell transcriptomics analysis reveals tissue-specific eosinophil states.

a, Schematic gating strategy for flow cytometric sorting of eosinophils across indicated tissues (BM, blood, lung, colon, SI, scWAT, eWAT and BAT). Skin eosinophils were isolated from ear skin. b, Quality control metrics for all cells of single-cell RNA sequencing (scRNA-seq), including gene counts, UMI counts, and mitochondrial read percentages. c, Proportional distribution of eosinophil clusters identified by scRNA-seq within each tissue compartment. d, Heatmap of DEGs across eosinophil clusters. e, Bubble plot of enriched pathway analysis showing significantly altered biological processes (dot size: gene ratio; color: adjusted p-value). f, Top: Hierarchical clustering of gene expression similarity between scRNA-seq clusters and Yu et al. dataset. Bottom: Heatmap of conserved signature genes shared between datasets.

Extended Data Fig. 2 High dimensional protein profiling reveals tissue-imprinted eosinophil identities.

a, Schematic workflow of the InfinityFlow antibody screening pipeline. b, Unified UMAP projection of eosinophils colored by predicted surface markers (left) and tissue origin (right). c, Differentially expressed surface proteins across eosinophil subclusters. d, Multi-tissue overlay plots of representative markers showing expression density distributions across Blood (blue), Lung (green), SI (purple) and eWAT (red).

Extended Data Fig. 3 High dimensional protein profiling reveals tissue-imprinted eosinophil identities.

a, Unified UMAP projection of eosinophils using the optimized panel. b, Marker validation heatmap: Z-score scaled expression of panel-derived signature markers. c, Tissue distribution on UMAP. d, Median fluorescence intensity (MFI) plots of key surface markers across tissues on gated eosinophils (n = 3 /group, skin samples in CLEC4a4 plot: n = 2).

Source data

Extended Data Fig. 4 Residency-dependent lifespan stratifies eosinophils across tissues.

a, Heatmap of developmentally regulated genes ordered by Monocle3-predicted trajectory (columns: single cells; rows: core latency genes). b, tdTomato labeling rates in eosinophils from tissue of Ms4a3Cre-Rosa26tdTomato mice (n = 2 /group). c, Ms4a3 expression (qPCR) in granulocyte-monocyte progenitors (GMP), bone marrow eosinophils (BME), and peripheral blood eosinophils (n = 3 /group). The data were normalized to Actb expression and are presented relative to the GMP group (set as 1). d, Fate-mapping principle: Schematic of tamoxifen-inducible Ms4a3CreERT2-Rosa26tdTomato system for eosinophil lineage tracing and pulse-chase design: Experimental timeline for inducible fate-mapping. e, Mathematical framework for estimating eosinophil half-life (t1/2) and lifespan (t5%) (shaded areas: 95% CI). f, Tissue residency kinetics: tdTomato+ decay curves fitted to model (e), (n = 6 for days 3, 7, 14, 21, and 28 in Lung and scWAT, and days 3, 7, and 14 in eWAT; n  = 3 for remaining time points). g, Eosinophil frequency and absolute counts across BM, blood and tissue (n = 3 /group). h, in vivo anti-CD45 antibody pulse-chase system schematic for measuring cellular turnover rates. Each dot represents an individual animal; bars show mean ± SEM (****P ≤ 0.0001) by one-way ANOVA with Tukey’s correction (c).

Source data

Extended Data Fig. 5 Eosinophil heterogeneity emerges in the long-lived small intestine niche.

a, Heatmap of different subgroups of lung eosinophils based on GSEA pathway enrichment. b, Heatmap of conserved signature genes shared between datasets. c, Cd101 expression on lung eosinophil clusters. d, Flow cytometry gating strategy for Cd101tdTomato reporter (left) vs. anti-CD101 antibody staining (right) in eosinophils and neutrophils. e, Quantification of the Cd101tdTomato and anti-CD101 antibody staining. Each dot represents an individual animal; bars show mean ± SEM (****P ≤ 0.0001) f, Heatmap of different subgroups of colon eosinophils based on GSEA pathway enrichment. g, Heatmap of different subgroups of SIE based on GSEA pathway enrichment. See the Methods section for details of the enrichment procedure. h, Hierarchical clustering of gene expression similarity between our SI eosinophil clusters with public dataset (W. Wang, et al.).

Source data

Extended Data Fig. 6 Residency-associated trajectories in the small intestine resolve eosinophil maturation states.

a, Heatmap displaying latency-associated gene expression patterns across eosinophil subsets. Clec12a, Itgal, Fcgr4 and Cd22 were highlighted. b, Heatmap showing expression levels of key surface markers (CD371, CD11a, CD16.2, CD22) across eosinophil subpopulations. c, Schematic gating strategy for identifying five distinct eosinophil subsets. d, Flow cytometry analysis of CD117 (c-Kit) expression in BME (n = 3 /group). e, Representative flow plots supplementing the gating strategy for the five subsets. f, Quantitative distribution of the five subpopulations among different tissues (n = 3 /group in BM, Blood and Colon. n = 4/group in Lung and SI). g, Expression patterns of CLEC4a4 and immune checkpoint molecules (PD-L1 and CD80) across eosinophil subsets in different tissues (n = 3 /group). Values shown as mean ± SEM. Statistic tests: Two-tailed unpaired t-test (d), one-way ANOVA (f, g) with Tukey’s correction for multiple comparisons. ns = not significant; *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001; ****P ≤ 0.0001.

Source data

Extended Data Fig. 7 Spatially compartmentalized diversification of intestinal eosinophils.

a, Representative immunofluorescence image of eosinophils on Ms4a3CreERT2-Rosa26tdTomato lung slides. Scale bar=20μm. b, Representative immunofluorescence image of colocalization of blood vessels with eosinophils on wild-type (WT) lung slides. Scale bar=10μm. c, Schematic diagram of intestinal segment selection for analysis. Scale bar=500μm (colon). Scale bar=1 mm (SI). d, Representative immunofluorescence images of CD22 staining in different intestinal segments 3 weeks post-tamoxifen induction on Ms4a3CreERT2-Rosa26tdTomato intestine slides. Representative of 5 independent experiments. Scale bar=20μm. Duode, duodenum; Jeju, jejunum; Ile, ileum; Proxi-Colon, proximal colon; Dis-colon, distal colon.

Extended Data Fig. 8 Spatially compartmentalized diversification of intestinal eosinophils.

a, Representative immunofluorescence images of CD11a staining in different intestinal segments 3 weeks post-tamoxifen induction on Ms4a3CreERT2-Rosa26tdTomato intestine slides. Representative of 5 independent experiments. Scale bar=20μm. b, Quantification of tdTomato+ eosinophil proportions across colonic segments (proximal to distal) by immunofluorescence analysis (n = 3 /group). Each dot represents a region of interest in individual mice. Values shown as mean ± SEM. Statistic tests: Two-tailed unpaired t-test (b), *P ≤ 0.05. Jeju, jejunum; Ile, ileum; Proxi-Colon, proximal colon; Dis-colon, distal colon.

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Hu, Y., Wu, L., Qu, S. et al. Temporal and spatial atlas of eosinophil specialization across tissues. Nat Immunol 27, 364–375 (2026). https://doi.org/10.1038/s41590-025-02382-6

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