Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Efferocytic remodelling of pancreatic islet macrophages by limited β-cell death

Nature (2025)Cite this article

Subjects

Abstract

The primary driver of type I diabetes is the autoimmune T cells that destroy insulin-producing β-cells within the islets of Langerhans in the pancreas1. Pancreatic islet macrophages have also been variably linked to disease onset and progression. As macrophage-mediated removal of dying cells through efferocytosis regulates tissue homeostasis and immune responses2, here we investigated how efferocytosis by intra-islet macrophages influences the immune environment of pancreatic islets. Using a series of complementary omics-based and functional approaches, we identify a subset of anti-inflammatory intra-islet efferocytic macrophages (e-Mac) within the pancreas of mice and humans. When limited β-cell apoptosis is induced in vivo in wild-type C57BL/6 mice and diabetic-prone NOD mice, islet macrophages adopt this e-Mac phenotype without an apparent increase in the total numbers of intra-islet macrophages. Such limited β-cell apoptosis and increase in e-Mac numbers led to long-term suppression of autoimmune diabetes in NOD mice. This e-Mac phenotype could also be recapitulated ex vivo by co-culturing macrophages with apoptotic β-cells. Mechanistically, the e-Mac-enriched populations imparted an anergic-like state on CD4+ T cells ex vivo and promoted accumulation of such anergic-like CD4+ T cells in vivo within the islets. Analysing macrophage–T cell interactions within pancreatic islets using NicheNet and targeted experimental validation, we identify the IGF-1–IGF1R axis as a contributor to the anergic-like T cell phenotype in the islets. Collectively, these data advance a concept that efferocytosis-associated reprogramming of the islet macrophages and the subsequent influence on the adaptive immune response could be beneficial in modulating diabetic autoimmunity.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Detection of intra-islet efferocytic macrophages in the mouse pancreas.
Fig. 2: Limited β-cell death affects islet macrophages in the NOD mouse model.
Fig. 3: Islet β-cell death affects CD4+ T cells in a mouse model of T1D.
Fig. 4: Efferocytosis-induced IGF-1 links e-Mac and anergic-like CD4+ T cells.
Fig. 5: Islet e-Mac cells are conserved between mouse and human.

Similar content being viewed by others

Data availability

All transcriptional data generated in the current study were deposited at the GEO and are publicly available: GSE304628 (scRNA-seq mouse islets), GSE304620 (scRNA-seq human donor islets) and GSE304616 (bulk RNA-seq CD4+ T cells, mouse). Publicly available datasets reanalysed in this study include the following: GSE244278 (anergic CD4+ T cell RNA-seq) and GSE143739 (anergic CD4+ T cell microarray). For comparing the single-cell transcriptional profile of islet macrophages with transcriptomics of macrophages from other tissues, the GSM7049631 (mouse microglia)36 and GSE128518 (macrophages in adipose tissue of mice fed a high-fat diet)37 datasets were used. Source data are provided with this paper.

References

  1. Herold, K. C., Vignali, D. A. A., Cooke, A. & Bluestone, J. A. Type 1 diabetes: translating mechanistic observations into effective clinical outcomes. Nat. Rev. Immunol. 13, 243–256 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Doran, A. C., Yurdagul, A. & Tabas, I. Efferocytosis in health and disease. Nat. Rev. Immunol. 20, 254–267 (2020).

    Article  CAS  PubMed  Google Scholar 

  3. Lavin, Y. et al. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 159, 1312–1326 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ginhoux, F. & Guilliams, M. Tissue-resident macrophage ontogeny and homeostasis. Immunity 44, 439–449 (2016).

    Article  CAS  PubMed  Google Scholar 

  5. Park, M. D., Silvin, A., Ginhoux, F. & Merad, M. Macrophages in health and disease. Cell 185, 4259–4279 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Calderon, B. et al. The pancreas anatomy conditions the origin and properties of resident macrophages. J. Exp. Med. 212, 1497–1512 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Brissova, M. et al. Islet microenvironment, modulated by vascular endothelial growth factor-A signaling, promotes β cell regeneration. Cell Metab. 19, 498–511 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Riley, K. G. et al. Macrophages are essential for CTGF-mediated adult β-cell proliferation after injury. Mol. Metab. 4, 584–591 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Thai, L. M. et al. β-Cell function is regulated by metabolic and epigenetic programming of islet-associated macrophages, involving Axl, Mertk, and TGFβ receptor signaling. iScience 26, 106477 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  10. Banaei-Bouchareb, L. et al. Insulin cell mass is altered in Csf1op/Csf1op macrophage-deficient mice. J. Leukoc. Biol. 76, 359–367 (2004).

    Article  CAS  PubMed  Google Scholar 

  11. Oschilewski, U., Kiesel, U. & Kolb, H. Administration of silica prevents diabetes in BB-rats. Diabetes 34, 197–199 (1985).

    Article  CAS  PubMed  Google Scholar 

  12. Carrero, J. A. et al. Resident macrophages of pancreatic islets have a seminal role in the initiation of autoimmune diabetes of NOD mice. Proc. Natl Acad. Sci. USA 114, E10418–E10427 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  13. Chen, D., Thayer, T. C., Wen, L. & Wong, F. S. Mouse models of autoimmune diabetes: the nonobese diabetic (NOD) mouse. Methods Mol. Biol. 2128, 87–92 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Calderon, B., Carrero, J. A. & Unanue, E. R. The central role of antigen presentation in islets of Langerhans in autoimmune diabetes. Curr. Opin. Immunol. 26, 32–40 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. Mohan, J. F. et al. Imaging the emergence and natural progression of spontaneous autoimmune diabetes. Proc. Natl Acad. Sci. USA 114, E7776–E7785 (2017).

  16. Zakharov, P. N., Hu, H., Wan, X. & Unanue, E. R. Single-cell RNA sequencing of murine islets shows high cellular complexity at all stages of autoimmune diabetes. J. Exp. Med. 217, e20192362 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Pugliese, A. Autoreactive T cells in type 1 diabetes. J. Clin. Invest. 127, 2881–2891 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Delong, T. et al. Pathogenic CD4 T cells in type 1 diabetes recognize epitopes formed by peptide fusion. Science 351, 711–714 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wan, X. et al. Pancreatic islets communicate with lymphoid tissues via exocytosis of insulin peptides. Nature 560, 107–111 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  20. DiLorenzo, T. P. & Serreze, D. V. The good turned ugly: immunopathogenic basis for diabetogenic CD8+ T cells in NOD mice. Immunol. Rev. 204, 250–263 (2005).

    Article  CAS  PubMed  Google Scholar 

  21. Noble, J. A. & Erlich, H. A. Genetics of type 1 diabetes. Cold Spring Harb. Perspect. Med. 2, a007732 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Wang, Y. J. et al. Multiplexed in situ imaging mass cytometry analysis of the human endocrine pancreas and immune system in type 1 diabetes. Cell Metab. 29, 769–783 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Fadok, V. A. et al. Macrophages that have ingested apoptotic cells in vitro inhibit proinflammatory cytokine production through autocrine/paracrine mechanisms involving TGF-beta, PGE2, and PAF. J. Clin. Invest. 101, 890–898 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Morioka, S. et al. Efferocytosis induces a novel SLC program to promote glucose uptake and lactate release. Nature 563, 714–718 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  25. Tufan, T. et al. Rapid unleashing of macrophage efferocytic capacity via transcriptional pause release. Nature 628, 408–415 (2024).

    Article  ADS  CAS  PubMed  Google Scholar 

  26. Hugues, S. et al. Tolerance to islet antigens and prevention from diabetes induced by limited apoptosis of pancreatic beta cells. Immunity 16, 169–181 (2002).

    Article  CAS  PubMed  Google Scholar 

  27. Boada-Romero, E., Martinez, J., Heckmann, B. L. & Green, D. R. The clearance of dead cells by efferocytosis. Nat. Rev. Mol. Cell Biol. 21, 398–414 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Turley, S., Poirot, L., Hattori, M., Benoist, C. & Mathis, D. Physiological β cell death triggers priming of self-reactive T cells by dendritic cells in a type-1 diabetes model. J. Exp. Med. 198, 1527–1537 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Lenzen, S. The mechanisms of alloxan- and streptozotocin-induced diabetes. Diabetologia 51, 216–226 (2008).

    Article  CAS  PubMed  Google Scholar 

  30. Furman, B. L. Streptozotocin‐induced diabetic models in mice and rats. Curr. Protoc. 1, e78 (2021).

    Article  CAS  PubMed  Google Scholar 

  31. Ferris, S. T. et al. The islet-resident macrophage is in an inflammatory state and senses microbial products in blood. J. Exp. Med. 214, 2369–2385 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Brosseau, C., Colas, L., Magnan, A. & Brouard, S. CD9 tetraspanin: a new pathway for the regulation of inflammation? Front. Immunol. 9, 2316 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Sano, H. et al. Critical role of galectin-3 in phagocytosis by macrophages. J. Clin. Invest. 112, 389–397 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Lemke, G. & Rothlin, C. V. Immunobiology of the TAM receptors. Nat. Rev. Immunol. 8, 327–336 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lindsay, R. S. et al. MERTK on mononuclear phagocytes regulates T cell antigen recognition at autoimmune and tumor sites. J. Exp. Med. 218, e20200464 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Millet, A., Ledo, J. H. & Tavazoie, S. F. An exhausted-like microglial population accumulates in aged and APOE4 genotype Alzheimer’s brains. Immunity 57, 153–170 (2024).

    Article  CAS  PubMed  Google Scholar 

  37. Jaitin, D. A. et al. Lipid-associated macrophages control metabolic homeostasis in a Trem2-dependent manner. Cell 178, 686–698 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290 (2017).

    Article  CAS  PubMed  Google Scholar 

  39. Carrero, J. A., Calderon, B., Towfic, F., Artyomov, M. N. & Unanue, E. R. Defining the transcriptional and cellular landscape of type 1 diabetes in the NOD mouse. PLoS ONE 8, e59701 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  40. Calderon, B., Carrero, J. A., Miller, M. J. & Unanue, E. R. Cellular and molecular events in the localization of diabetogenic T cells to islets of Langerhans. Proc. Natl Acad. Sci. USA 108, 1561–1566 (2011).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  41. Katz, J. D., Wang, B., Haskins, K., Benoist, C. & Mathis, D. Following a diabetogenic T cell from genesis through pathogenesis. Cell 74, 1089–1100 (1993).

    Article  CAS  PubMed  Google Scholar 

  42. Gonzalez, A. et al. Genetic control of diabetes progression. Immunity 7, 873–883 (1997).

    Article  CAS  PubMed  Google Scholar 

  43. Trefzer, A. et al. Dynamic adoption of anergy by antigen-exhausted CD4+ T cells. Cell Rep. 34, 108748 (2021).

    Article  CAS  PubMed  Google Scholar 

  44. Titcombe, P. J., Silva Morales, M., Zhang, N. & Mueller, D. L. BATF represses BIM to sustain tolerant T cells in the periphery. J. Exp. Med. 220, e20230183 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kalekar, L. A. et al. CD4+ T cell anergy prevents autoimmunity and generates regulatory T cell precursors. Nat. Immunol. 17, 304–314 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Price, J. D., Hotta-Iwamura, C., Zhao, Y., Beauchamp, N. M. & Tarbell, K. V. DCIR2+ cDC2 DCs and Zbtb32 restore CD4+ T-cell tolerance and inhibit diabetes. Diabetes 64, 3521–3531 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Shin, H. M. et al. Transient expression of ZBTB32 in anti-viral CD8+ T cells limits the magnitude of the effector response and the generation of memory. PLoS Pathog. 13, e1006544 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Alroy, I., Towers, T. L. & Freedman, L. P. Transcriptional repression of the interleukin-2 gene by vitamin D3: direct inhibition of NFATp/AP-1 complex formation by a nuclear hormone receptor. Mol. Cell. Biol. 15, 5789–5799 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Martinez, R. J. et al. Arthritogenic self-reactive CD4+ T cells acquire an FR4hiCD73hi anergic state in the presence of Foxp3+ regulatory T cells. J. Immunol. 188, 170–181 (2012).

    Article  CAS  PubMed  Google Scholar 

  50. Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).

    Article  CAS  PubMed  Google Scholar 

  51. Nackiewicz, D. et al. Islet macrophages shift to a reparative state following pancreatic beta-cell death and are a major source of islet insulin-like growth factor-1. iScience 23, 100775 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  52. Shapiro, M. R. et al. Insulin-like growth factor dysregulation both preceding and following type 1 diabetes diagnosis. Diabetes 69, 413–423 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Han, C. Z. et al. Macrophages redirect phagocytosis by non-professional phagocytes and influence inflammation. Nature 539, 570–574 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Mallol, C. et al. AAV-mediated pancreatic overexpression of Igf1 counteracts progression to autoimmune diabetes in mice. Mol. Metab. 6, 664–680 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Finegood, D. T., Scaglia, L. & Bonner-Weir, S. Dynamics of beta-cell mass in the growing rat pancreas. Estimation with a simple mathematical model. Diabetes 44, 249–256 (1995).

    Article  CAS  PubMed  Google Scholar 

  56. Scaglia, L., Cahill, C. J., Finegood, D. T. & Bonner-Weir, S. Apoptosis participates in the remodeling of the endocrine pancreas in the neonatal rat. Endocrinology 138, 1736–1741 (1997).

    Article  CAS  PubMed  Google Scholar 

  57. Trudeau, J. D. et al. Neonatal beta-cell apoptosis: a trigger for autoimmune diabetes? Diabetes 49, 1–7 (2000).

    Article  CAS  PubMed  Google Scholar 

  58. Kassem, S. A., Ariel, I., Thornton, P. S., Scheimberg, I. & Glaser, B. Beta-cell proliferation and apoptosis in the developing normal human pancreas and in hyperinsulinism of infancy. Diabetes 49, 1325–1333 (2000).

    Article  CAS  PubMed  Google Scholar 

  59. Ciecko, A. E. et al. Heterogeneity of islet-infiltrating IL-21+ CD4 T cells in a mouse model of type 1 diabetes. J. Immunol. 210, 935–946 (2023).

    Article  CAS  PubMed  Google Scholar 

  60. Foda, B. M. et al. The CD137 ligand is important for type 1 diabetes development but dispensable for the homeostasis of disease-suppressive CD137+FOXP3+ regulatory CD4 T cells. J. Immunol. 204, 2887–2899 (2020).

    Article  CAS  PubMed  Google Scholar 

  61. Mohan, J. F., Calderon, B., Anderson, M. S. & Unanue, E. R. Pathogenic CD4+ T cells recognizing an unstable peptide of insulin are directly recruited into islets bypassing local lymph nodes. J. Exp. Med. 210, 2403–2414 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Goudy, K. S. et al. Systemic overexpression of IL-10 induces CD4+CD25+ cell populations in vivo and ameliorates type 1 diabetes in nonobese diabetic mice in a dose-dependent fashion. J. Immunol. 171, 2270–2278 (2003).

    Article  CAS  PubMed  Google Scholar 

  63. Kolberg, L., Raudvere, U., Kuzmin, I., Vilo, J. & Peterson, H. gprofiler2—an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler. F1000Research 9, ELIXIR-709 (2020).

  64. Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Zinselmeyer, B. H. et al. The resident macrophages in murine pancreatic islets are constantly probing their local environment, capturing beta cell granules and blood particles. Diabetologia 61, 1374–1383 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Zhu, A., Ibrahim, J. G. & Love, M. I. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics 35, 2084–2092 (2019).

    Article  CAS  PubMed  Google Scholar 

  68. Blighe, K. et al. EnhancedVolcano: publication-ready volcano plots with enhanced colouring and labeling. https://doi.org/10.18129/B9.BIOC.ENHANCEDVOLCANO (Bioconductor, 2018).

  69. Zhang, Y., Parmigiani, G. & Johnson, W. E. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom. Bioinform. 2, lqaa078 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the members of the K.S.R. and X.W. laboratories for inputs and suggestions; J. I. Etchegaray, S. Arandjelovic, C. Maueröder, N. Srivastava, P. Mehrotra, M. Colonna and the members of the Colonna laboratory for inputs; C. Rothlin for providing the Mertkfl/fl mice; R. Birge for providing some reagents; the staff at the following Washington University School of Medicine cores: the Flow Cytometry Core, WUCCI Imaging Core and the Genome Technology Access Center at the McDonnell Genome Institute for RNA-seq library preparation and sequencing; A. Srivastav for help with some mouse studies and W. Beatty for help with electron microscopy imaging. K.S.R. is supported by supported by R01AI159551 (NIAID) and BJC Investigator Funds from the Washington University School of Medicine; X.W. is supported by R01AI162591 (NIAID); E.K. is supported by 1R01CA245277-01A1 (NCI), 5R01AR075959-02 (NIAMS) and a National Psoriasis Foundation Grant. Graphics in Figs. 1h,l,m, 2b, 3a,b, 4d,h, 5a,e,f and Extended Data Figs. 4a,b, 5a, 6a, 8f and 10d contain schematics created using a paid subscription of BioRender.

Author information

Authors and Affiliations

  1. Division of Immunobiology, Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA

    Pavel N. Zakharov, Chanchal S. Chowdhury, Orion J. Peterson, Brady Barron, Anthony N. Vomund, Laurent Gorvel, Emil R. Unanue, Eynav Klechevsky, Xiaoxiao Wan & Kodi S. Ravichandran

  2. Unit for Cell Clearance in Health and Disease, VIB Center for Inflammation Research and Department of Medical Molecular Biology, Ghent University, Ghent, Belgium

    Kodi S. Ravichandran

Authors
  1. Pavel N. Zakharov
    View author publications

    Search author on:PubMed Google Scholar

  2. Chanchal S. Chowdhury
    View author publications

    Search author on:PubMed Google Scholar

  3. Orion J. Peterson
    View author publications

    Search author on:PubMed Google Scholar

  4. Brady Barron
    View author publications

    Search author on:PubMed Google Scholar

  5. Anthony N. Vomund
    View author publications

    Search author on:PubMed Google Scholar

  6. Laurent Gorvel
    View author publications

    Search author on:PubMed Google Scholar

  7. Emil R. Unanue
    View author publications

    Search author on:PubMed Google Scholar

  8. Eynav Klechevsky
    View author publications

    Search author on:PubMed Google Scholar

  9. Xiaoxiao Wan
    View author publications

    Search author on:PubMed Google Scholar

  10. Kodi S. Ravichandran
    View author publications

    Search author on:PubMed Google Scholar

Contributions

P.N.Z., K.S.R., X.W. and E.R.U. designed the experiments and interpreted the data. K.S.R. and P.N.Z. wrote the manuscript with input from all of the authors. X.W. provided critical experimental inputs and reagents. P.N.Z. carried out most of the experiments, analysed the data and performed bioinformatics analysis of scRNA-seq and bulk RNA-seq data. C.S.C., P.N.Z. and O.J.P. carried out confocal microscopy. C.S.C. and P.N.Z. carried out image processing. C.S.C. performed 3D image rendering. P.N.Z. and O.J.P. performed pancreatic islet isolation. L.G. and E.K. provided scRNA-seq data for human donor C. B.B. carried out annexin V purification. A.N.V. assisted in several experiments.

Corresponding authors

Correspondence to Pavel N. Zakharov or Kodi S. Ravichandran.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Raymond Birge, Edward Thorp, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Efferocytosis by islet macrophages following STZ1-low treatment in C57BL/6 mice.

a. Immunofluorescent confocal microscopy imaging showing confocal planes of the pancreatic islets of C57BL/6 mice 12 h after STZ1-low or control vehicle treatment. Green – apoptotic cells (cleaved caspase-3, CC3), blue – nuclei (Hoechst). Scale bar 30 µm. b. Quantification of fraction of apoptotic cells (CC3+) relative to the total cell number in the islets (Hoechst+). n = 9 islets in both conditions. c. Confocal images of islets taken 12 h after STZ1-low treatment showing X-Y, Y-Z, and X-Z views of efferocytic events. Red – insulin, white – macrophages (F4/80), green – apoptotic cells (CC3), blue – nuclei (Hoechst). The CC3/insulin double positive areas inside the macrophages are considered as efferocytic events. The top row of images corresponds to (Fig. 1b). Scale bar – 30 µm. d. Quantification of efferocytic events via Imaris software automatic counting (see Methods). n = 9(Vehicle), n = 8(STZ1-low). e. Percentage of islet macrophages (Live, CD45+, F4/80+, CD11c+) relative to all islet cells based on flow cytometric analysis 10 days after treatment with STZ1-low or vehicle (C57BL/6 mice). n = 8(Vehicle), n = 11(STZ1-low) mice. Statistics - two-tailed unpaired t-test, data shown as mean ± SEM.

Source data

Extended Data Fig. 2 scRNASeq of islet leukocytes following limited β-cell death.

a. The UMAP plot showing scRNASeq analysis of CD45+ cells from the islets of C57BL/6 J mice 10 days after STZ1-low or vehicle treatment. b. Phenotypic markers expressed by cells from (a). c. Relative fractions of cells identified on (a) and (b).

Source data

Extended Data Fig. 3 Islet e-Mac shows similarity with pathology-associated macrophages from other tissues.

a. UMAP plots show scRNASeq of islet macrophages (C57BL/6 mice) and two publicly available scRNASeq datasets: i) microglia in the brain of an Alzheimer’s disease mouse model (GSM7049631 (ref. 36)), and ii) macrophages in adipose tissue of mice fed a high-fat-diet (GSE128518 (ref. 37)). Expression of some marker genes are shown on the right. b. Venn diagram illustrates overlaps among gene signatures from macrophage subsets in (a).

Extended Data Fig. 4 Gal-3 upregulation in islet and peritoneal macrophages following apoptotic β-cell uptake ex vivo.

a. Isolation of islet macrophages using CD11c+ magnetic beads (MACS) (non-autoimmune C57BL/6 J mice). Flow cytometric analysis on dispersed islet cells before enrichment (left), and MACS-enriched Live, CD45+ F4/80+ CD11c+ macrophages (right). b. Schematic of the assay when primary islet macrophages co-cultured with live or apoptotic β-cells (UV-irradiated Min6 cells). c. Flow cytometric evaluation of e-Mac frequencies in the co-culture experiment (b). Quantification is shown on the right. n = 4(UT), n = 2(Live), n = 8(Apo) biological replicates over N = 4 independent experiments for “UT” and “Apo” and N = 2 for “Live”. Two-tailed unpaired t-test with Welch’s correction. d. Flow cytometric analysis showing peritoneal macrophages. Peritoneal lavage cells were collected from C57BL/6 mice, plated for 2 h, washed to remove unbound cells, and analysed by flow cytometry 24 h after the beginning of the incubation. e. Flow cytometric analysis showing engulfment of apoptotic β-cells by peritoneal macrophages. Apoptotic β-cells (Min6 cell line) were labelled with pHrodo red dye and co-cultured with pMacs for 2 h; unbound apoptotic cells were then washed away, and incubation continued. The macrophage-to-β-cell ratio was 1:3. Flow cytometry was done 16 h after beginning of the incubation. f. Flow cytometric analysis of peritoneal macrophages after co-culture with live or apoptotic β-cells (Min6). Two negative controls were used: cytochalasin D (CytoD, 1 µM) – inhibitor of cytoskeletal reorganization that prevents corpse uptake; and annexin V (20 µg/ml) that binds phosphatidylserine (PtdSer) and masks it from scavenger receptors on the phagocytic cells. g. Quantification of (f). n = 6 (No β-cell), n = 6 (Live β-cell), n = 6 (Apo β-cell, alone), n = 3 (Apo β-cell, +CytoD), n = 3 (Apo β-cell, +Ann V) biological replicates; N = 2. Statistics - two-tailed unpaired t-test. Data are representative of N = 3 independent experiments (a, b). The diagrams in a and b were created in BioRender. Ravichandran, K. (2025) https://BioRender.com/oc6ssn7; Ravichandran, K. (2025) https://BioRender.com/laier1m.

Source data

Extended Data Fig. 5 Both e-Mac and non-e-Mac islet macrophages can uptake apoptotic β-cells.

a. Representative confocal images of the pancreatic islets of NOD mice 12 h after STZ1-low or control vehicle treatment. Green – apoptotic cells (cleaved caspase-3, CC3), blue – nuclei (Hoechst), red – Gal-3, white – F4/80, yellow – insulin. Scale bars: 20 µm (left, whole-islet view); 2 µm (right, zoomed-in view). b. Expression of Mer-TK by islet macrophages in Mertkfl/fl/Cx3cr1cre/wt (Mertk cKO) and Mertkfl/fl/Cx3cr1wt/wt, C57BL/6 mice. Data are representative of N = 3 independent experiments. The diagrams in a were created in BioRender. Ravichandran, K. (2025) https://BioRender.com/lwltf25.

Extended Data Fig. 6 Intra-islet macrophages are the primary cells engulfing apoptotic β-cells.

a. Representative confocal images of the pancreatic islets of NOD mice 12 h after STZ1-low or control vehicle treatment. Green – apoptotic cells (cleaved caspase-3, CC3), blue – nuclei (Hoechst), red – CD11c, white – F4/80, yellow – insulin. Scale bars: 20 µm (left, whole-islet view); 2 µm (right, zoomed-in view). b. Quantification of the fraction of MФ and DC among all islet cells (top), and absolute number of each cell type per islet (bottom). c. Quantification of the fraction of MФ and DC containing apoptotic remnants (CC3+insulin+) among all islet cells (top), and absolute number of MФ and DC with internalized apoptotic material (bottom). Scale bars: 20 µm (left, whole-islet view); 2 µm (right, zoomed-in view). Statistics - paired t-test; N = 2. The diagram in a was created in BioRender. Ravichandran, K. (2025) https://BioRender.com/lwltf25.

Source data

Extended Data Fig. 7 Leukocytes from islets of NOD mice following limited β-cell death.

a. The UMAP plot showing CD45+ cells from the islets of NOD mice 10 days after inducing limited β-cell death with STZ1-low treatment. b. Differentially expressed genes defining cell types from (a).

Extended Data Fig. 8 Efferocytosis induces an anti-inflammatory program in islet macrophages.

a. Heatmap showing differentially expressed genes defining macrophage subsets in the islets of NOD mice at 5.5 weeks of age (10 days after treatment). b. Overrepresentation pathway analysis based on the gene signatures of e-Mac, Mac-2, and Mac-3 subsets (hypergeometric test). c. Venn diagrams comparing gene signature of e-Mac with those of other macrophage subsets. Note that gene signatures of Mac-1 and e-Mac have overlap. d. Examples of islet infiltration scores based on hematoxylin and eosin (H&E) staining of pancreas sections of 30-week-old NOD female mice treated at 4 weeks with either STZ1-low or vehicle. Scale bar – 20 µm. N = 2. e. Flow cytometric chart of CD45+ cells from pancreatic islets isolated from NOD.Rag1−/− mice (aged 4–6 weeks; pooled from 5 mice). Representative of N = 4 independent experiments. f. Flow cytometric charts of experiment in which naive CD4+ T cells were activated by islet macrophages or by conventional dendritic cells (cDC) from spleens and lymph nodes. Islet macrophages from either STZ1-low- or vehicle-treated NOD.Rag1−/− mice were co-cultured with CD4+ T cells without adding cognate antigen for 96 h. When conventional dendritic cells were used as antigen-presenting cells, 0.5 µM BDC mimotope was added when indicated. Conventional dendritic cells were isolated from spleen and lymph nodes of NOD.Rag1−/− mice. (See also Fig. 3b). The diagram in f was created in BioRender. Ravichandran, K. (2025) https://BioRender.com/mrggrwq.

Extended Data Fig. 9 CD4+ T cells acquire partial anergic-like phenotype after activation by efferocytosis-associated islet macrophages both ex vivo and in vivo.

a. Peritoneal macrophages isolated from NOD mouse using magnetic-activated cell sorting (MACS). CD11bpos macrophages are enriched into Tim-4hi large peritoneal macrophages (LPM, expressing low level of MHC-II) and Tim-4low small peritoneal macrophages (SPM, MHC-IIhi). Representative of N = 3 independent experiments. b. Antigen presentation assay in which naive TCR transgenic BDC2.5 CD4+ T cells were co-cultured for 3 days with SPM or LPM at indicated concentrations of antigenic peptide; N = 2. c. Gene set enrichment analysis (GSEA) plot showing enrichment of gene signature of CD4+ T cells activated by “e-Mac enriched” macrophages interrogated against publicly available transcriptional dataset comparing anergic and naive CD4+ T cells (GSE14373943). d. Summary plot of GSEA using CD4+ T cells gene signature induced by islet macrophages from STZ1-low- or vehicle- treated mice (“e-Mac enriched” and “Vehicle” correspondingly). Differential expression comparisons between anergic cells and either naive or antigen-experienced CD4+ T cells were used as reference 43 (GSE143739). The CD4+ T cells in the reference dataset were exposed to different levels of antigen. e. GSEA pathways comparing gene expression pathways upregulated in CD4+ T cells activated by islet macrophages from STZ1-low- versus vehicle- treated mice. Gene Ontology MSigDB (GO:BP). f. Heatmap showing differentially expressed genes among CD4+ T cell subsets in the islets of NOD mice 10 days after the treatment with either STZ1-low- or control vehicle. g. GSEA plot showing islet anergic-like CD4+ T cells gene signature interrogated against bulk transcriptional dataset43 (GSE14373943) comparing anergic and naive cells. NES, normalized enrichment score. Statistics: weighted Kolmogorov-Smirnov test (c, e, g).

Source data

Extended Data Fig. 10 Macrophages from 12-week-old NOD mice and human islet leukocytes analysed by scRNASeq.

a. scRNASeq analysis (UMAP plots) showing macrophage subsets from pancreatic islets at 12 weeks of age (late pre-diabetic) from NOD mice treated with STZ1-low or vehicle at 4 weeks of age (at early pre-diabetic stage). b. Markers of macrophage subsets from (a). c. Reanalysis of a publicly available dataset showing Igf1r expression across CD4+ T cell subsets from spleen and lymph node (GSE24427844). (Wald statistics, DESeq2). Data are presented as mean values ± SEM, 3 biological replicates over N = 1 RNASeq experiment. d. Flow cytometric analysis of human donor pancreatic islet cells. Islet macrophages were identified as Live, CD45+ CX3CR1+ CD11c+ cells. Representative of N = 2. e. Human islet leukocytes analysed by scRNASeq, 3 individual donors. CD45+ cells were FACS-purified as shown on (d), left. Transcriptional data was integrated between the donors and subjected to clustering. f,g. Markers differentially expressed between the identified immune cell types in human pancreatic islets. The diagram in d was created in BioRender. Ravichandran, K. (2025) https://BioRender.com/ddu5zzj.

Source data

Supplementary information

Reporting Summary

Supplementary Videos 1–3

Representative videos showing an independent event of macrophage efferocytosis in the islets taken 12 h after STZ1-low treatment. Red, insulin; white, macrophages (F4/80); green, apoptotic cells (cleaved caspase 3, CC3); blue, nuclei (Hoechst). The CC3/insulin double-positive areas inside the macrophages are considered to be efferocytic events. Scale bar, 2 µm.

Source data

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zakharov, P.N., Chowdhury, C.S., Peterson, O.J. et al. Efferocytic remodelling of pancreatic islet macrophages by limited β-cell death. Nature (2025). https://doi.org/10.1038/s41586-025-09560-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41586-025-09560-4

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing