Immune-evasive human islet-like organoids ameliorate diabetes

Abstract

Islets derived from stem cells hold promise as a therapy for insulin-dependent diabetes, but there remain challenges towards achieving this goal1,2,3,4,5,6. Here we generate human islet-like organoids (HILOs) from induced pluripotent stem cells and show that non-canonical WNT4 signalling drives the metabolic maturation necessary for robust ex vivo glucose-stimulated insulin secretion. These functionally mature HILOs contain endocrine-like cell types that, upon transplantation, rapidly re-establish glucose homeostasis in diabetic NOD/SCID mice. Overexpression of the immune checkpoint protein programmed death-ligand 1 (PD-L1) protected HILO xenografts such that they were able to restore glucose homeostasis in immune-competent diabetic mice for 50 days. Furthermore, ex vivo stimulation with interferon-γ induced endogenous PD-L1 expression and restricted T cell activation and graft rejection. The generation of glucose-responsive islet-like organoids that are able to avoid immune detection provides a promising alternative to cadaveric and device-dependent therapies in the treatment of diabetes.

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Fig. 1: WNT4 induces functional maturation of HILOs.
Fig. 2: Exogenous PD-L1 expression extends wHILO functionality in immune-competent mice.
Fig. 3: wHILOs (PD-L1) provide extended glucose control in humanized mice.
Fig. 4: Enhanced endogenous PD-L1 expression generates immune-evasive wHILOs.

Data availability

RNA-seq and ATAC–seq data that support the findings of this study have been deposited in the National Center for Biotechnology Information Sequence Read Archive database under accession no. PRJNA505532Source data are provided with this paper.

References

  1. 1.

    Yoshihara, E. et al. ERRγ is required for the metabolic maturation of therapeutically functional glucose-responsive β cells. Cell Metab. 23, 622–634 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Hrvatin, S. et al. Differentiated human stem cells resemble fetal, not adult, β cells. Proc. Natl Acad. Sci. USA 111, 3038–3043 (2014).

    CAS  PubMed  ADS  Google Scholar 

  3. 3.

    Rezania, A. et al. Reversal of diabetes with insulin-producing cells derived in vitro from human pluripotent stem cells. Nat. Biotechnol. 32, 1121–1133 (2014).

    CAS  PubMed  Google Scholar 

  4. 4.

    Pagliuca, F. W. et al. Generation of functional human pancreatic β cells in vitro. Cell 159, 428–439 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Kieffer, T. J. Closing in on mass production of mature human beta cells. Cell Stem Cell 18, 699–702 (2016).

    CAS  PubMed  Google Scholar 

  6. 6.

    Liu, J. S. & Hebrok, M. All mixed up: defining roles for β-cell subtypes in mature islets. Genes Dev. 31, 228–240 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Takebe, T. et al. Vascularized and functional human liver from an iPSC-derived organ bud transplant. Nature 499, 481–484 (2013).

    CAS  PubMed  ADS  Google Scholar 

  8. 8.

    Asai, A. et al. Paracrine signals regulate human liver organoid maturation from induced pluripotent stem cells. Development 144, 1056–1064 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Bader, E. et al. Identification of proliferative and mature β-cells in the islets of Langerhans. Nature 535, 430–434 (2016).

    CAS  PubMed  ADS  Google Scholar 

  10. 10.

    van der Meulen, T. et al. Urocortin3 mediates somatostatin-dependent negative feedback control of insulin secretion. Nat. Med. 21, 769–776 (2015).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Blum, B. et al. Functional beta-cell maturation is marked by an increased glucose threshold and by expression of urocortin 3. Nat. Biotechnol. 30, 261–264 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    van der Meulen, T. et al. Urocortin 3 marks mature human primary and embryonic stem cell-derived pancreatic alpha and beta cells. PLoS ONE 7, e52181 (2012).

    PubMed  PubMed Central  ADS  Google Scholar 

  13. 13.

    Prentki, M., Matschinsky, F. M. & Madiraju, S. R. Metabolic signaling in fuel-induced insulin secretion. Cell Metab. 18, 162–185 (2013).

    CAS  PubMed  Google Scholar 

  14. 14.

    Huang, S. M. et al. Tankyrase inhibition stabilizes axin and antagonizes Wnt signalling. Nature 461, 614–620 (2009).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  15. 15.

    Baas, M. et al. TGFβ-dependent expression of PD-1 and PD-L1 controls CD8+ T cell anergy in transplant tolerance. eLife 5, e08133 (2016).

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Martinov, T., Spanier, J. A., Pauken, K. E. & Fife, B. T. PD-1 pathway-mediated regulation of islet-specific CD4+ T cell subsets in autoimmune diabetes. Immunoendocrinology 3, e1164 (2016).

    PubMed  Google Scholar 

  17. 17.

    Keir, M. E. et al. Tissue expression of PD-L1 mediates peripheral T cell tolerance. J. Exp. Med. 203, 883–895 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Ansari, M. J. et al. The programmed death-1 (PD-1) pathway regulates autoimmune diabetes in nonobese diabetic (NOD) mice. J. Exp. Med. 198, 63–69 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Ma, D. et al. PD-L1 deficiency within islets reduces allograft survival in mice. PLoS ONE 11, e0152087 (2016).

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Rui, J. et al. β Cells that resist immunological attack develop during progression of autoimmune diabetes in NOD Mice. Cell Metab. 25, 727–738 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Wang, C. J. et al. Protective role of programmed death 1 ligand 1 (PD-L1) in nonobese diabetic mice: the paradox in transgenic models. Diabetes 57, 1861–1869 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Colli, M. L. et al. PDL1 is expressed in the islets of people with type 1 diabetes and is up-regulated by interferons-α and-γ via IRF1 induction. eBioMedicine 36, 367–375 (2018).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Osum, K. C. et al. Interferon-gamma drives programmed death-ligand 1 expression on islet β cells to limit T cell function during autoimmune diabetes. Sci. Rep. 8, 8295 (2018).

    PubMed  PubMed Central  ADS  Google Scholar 

  24. 24.

    Eizirik, D. L. & Mandrup-Poulsen, T. A choice of death—the signal-transduction of immune-mediated beta-cell apoptosis. Diabetologia 44, 2115–2133 (2001).

    CAS  PubMed  Google Scholar 

  25. 25.

    Russ, H. A. et al. Controlled induction of human pancreatic progenitors produces functional beta-like cells in vitro. EMBO J. 34, 1759–1772 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Nair, G. G. et al. Recapitulating endocrine cell clustering in culture promotes maturation of human stem-cell-derived β cells. Nat. Cell Biol. 21, 263–274 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Sneddon, J. B. et al. Stem cell therapies for treating diabetes: progress and remaining challenges. Cell Stem Cell 22, 810–823 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Zhou, Q. & Melton, D. A. Pancreas regeneration. Nature 557, 351–358 (2018).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  29. 29.

    Turner, M. et al. Toward the development of a global induced pluripotent stem cell library. Cell Stem Cell 13, 382–384 (2013).

    CAS  PubMed  Google Scholar 

  30. 30.

    Morizane, A. et al. MHC matching improves engraftment of iPSC-derived neurons in non-human primates. Nat. Commun. 8, 385 (2017).

    PubMed  PubMed Central  ADS  Google Scholar 

  31. 31.

    Wei, Z. et al. Vitamin D switches BAF complexes to protect β cells. Nat. Commun. 173, 1135–1149 (2018).

    CAS  Google Scholar 

  32. 32.

    Yoshihara, E. et al. Disruption of TBP-2 ameliorates insulin sensitivity and secretion without affecting obesity. Nat. Commun. 1, 127 (2010).

    PubMed  PubMed Central  ADS  Google Scholar 

  33. 33.

    Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21.29.1–21.29.9 (2015).

    Google Scholar 

  34. 34.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Google Scholar 

  36. 36.

    Trapnell, C. et al. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol. 31, 46–53 (2013).

    CAS  PubMed  Google Scholar 

  37. 37.

    Roberts, A., Pimentel, H., Trapnell, C. & Pachter, L. Identification of novel transcripts in annotated genomes using RNA-seq. Bioinformatics 27, 2325–2329 (2011).

    CAS  PubMed  Google Scholar 

  38. 38.

    van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729 (2018).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Huang da. W. et al. Extracting biological meaning from large gene lists with DAVID. Curr. Protoc. Bioinformatics Ch. 13, https://doi.org/10.1002/0471250953.bi1311s27 (2009).

  42. 42.

    Walter, W., Sánchez-Cabo, F. & Ricote, M. GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics 31, 2912–2914 (2015).

    CAS  Google Scholar 

Download references

Acknowledgements

We thank J. Norris, H. Song, B. Henriquez, B. Collins and H. Juguilon for technical assistance; L. Ong and C. Brondos for administrative assistance; and M. Ahmadian and S. Liu for sharing materials and helpful discussion. scRNA-seq and ATAC–seq were supported by Next Generation Sequencing Core (N. Hah) of the Salk Institute and the UCSD IGM Genomics Center (K. Jepsen). Cell sorting and flow cytometry analyses, TEM and stem cell cultures were supported by the Flow Cytometry Core, the Waitt Advanced Biophotonics Core and the Stem Cell Core facilities at the Salk Institute, with funding from NIH-NCI CCSG: P30 014195, S10-OD023689, and the Waitt Foundation. R.M.E. is an investigator of the Howard Hughes Medical Institute at the Salk Institute and March of Dimes Chair in Molecular and Developmental Biology. This work is supported by grants from CIRM (DISC2-11175), NIH (1RO1DK120480-01), the Glenn Foundation for Medical Research, the Leona M. and Harry B. Helmsley Charitable Trust (2017-PG-MED001), Ipsen/Biomeasure, and by a gift from Steven and Lisa Altman. C.L. and M.D. are funded by grants from the National Health and Medical Research Council of Australia Project (512354, 632886, and 1043199). E.Y. is supported by DRC P&F grant (P30 DK063491), and Z.W. is supported by NIH (1K01DK120808). We dedicate this work to the memory of our inspirational friend and colleague Maryam Ahmadian.

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Authors

Contributions

E.Y. conceived and designed the study; E.Y., C.O., E.G., Z.W., T.G.O., T.W.T., D.W., F.C., Y.D., R.T.Y. and C.L. performed experiments; E.Y., C.O., E.G., Z.W., T.G.O., R.T.Y., C.L., A.R.A., M.D. and R.M.E analysed data. E.Y., M.D. and R.M.E. supervised the study, and E.Y., R.T.Y., A.R.A., M.D. and R.M.E. prepared the manuscript.

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Correspondence to Ronald M. Evans.

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The authors declare no competing interests.

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Peer review information Nature thanks Anne Grapin-Botton, Heiko Likert and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Cellular crosstalk drives functional maturation of hiPSC-derived β-like cells.

a, Principal component analysis of transcriptomes from human iPSCs (hiPSCs), primary human pancreatic epithelial cells (hPanc Epithelial), human adipose-derived stem cells (hADSCs), human pancreatic fibroblasts (hPanc Fibroblast), human umbilical vein endothelial cells (HUVECs) and human pancreatic microvascular endothelial cells (hPanc Endothelial) (n = 3). b, Time course of human adipose-derived stem cell (hADSC) culture in Matrigel (1:1 dilution in hADSC media, 2 million cells in 300 μl) showing intrinsic self-organization. Scale bar 1 mm. c, Schematic for multicellular islet-like spheroids (MCS) and islet-like spheroid (IS) generation. hiPSC-derived endocrine progenitors were co-cultured with hADSC and endothelial cells (EC, HUVECs) in gellan gum-based 3D culture system (left). MCS generated in Matrigel showing the incorporation of ECs (Lentivirus-mCherry expression) and insulin expression (Lentivirus-GFP, right). Scale bar 100 μm. d, MCS cultured in 3D gellan gum system showing insulin expression (Lentivirus-GFP, upper panel). Electron microscopy images of MCS showing insulin granules (lower right) and lipid droplets in hADSC (lower right). e, Gene expression in sorted insulin-expressing cells (GFP+) from IS, MCS, or human islets (hislets) (n = 3). f, Human C-peptide secretion in response to 3 mM (G3) or 20 mM (G20) glucose from IS, MCS and hislets (n = 9). g, Random fed blood glucose levels in STZ-induced diabetic NOD/SCID mice after sham treatment or transplantation of MCS (500) or human islets (n = 3, 3 and 5 respectively). h, Serum human C-peptide levels during feeding, fasting, and refeeding cycles in mice 4 weeks after transplantation (n = 3 per group). i, Heat map of expression changes during hADSC culture in Matrigel (left). Most significantly affected gene ontology category in indicated gene clusters (right) (n = 3). j, Temporal expression of WNTs during hADSC self organization shown in b (n = 3). Error bars represent ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, one-tailed, Student’s paired t-test. Data representative of 3 independent experiments (b, dg) or experimental triplicates (a, hj). Source data

Extended Data Fig. 2 WNT expression in human islets.

a, Heat map of relative expression of WNTs in human islets (n = 5). b, t-SNE clustering of human islet single cell transcriptomes (n = 3,245). Annotated cell types assigned based on known marker gene expression. c, Heat map of expression of top 10 signature genes in human islet cell clusters from b. d, Single cell expression of signature hormonal and cell type-specific genes in human islets. e, f, Single cell (e) and violin plots (f) of WNT2B, WNT4, WNT5A, WNT7A, WNT7B and WNT9A expression in human islets, statistics provided in Supplementary Table 5. Data from 3 pooled human islets (bf).

Extended Data Fig. 3 Phenotypic and genotypic characterization of HILOs.

a, Schematic of CRISPR–Cas9 knock-in for endogenous human insulin promoter-driven GFP expression in hiPSC. b, Representative differential interference contrast (DIC) images of wHILOs with insulin-GFP and UCN3-RFP expression (scale bar, 100 μm, n = 3). c, Relative expression of ISL1, SYT4, PDX1, GCK, NEUROD1, NKX2-2, INS, NKX6-1, MAFA, MAFB and UCN3 in wHILOs and human islets determined by qPCR (n = 8 per sample type). d, Extracellular acidification rate (ECAR) measured in day 0 hiPSC spheroids (purple), PBS-treated HILOs (green), WNT4-treated HILOs (red) and human islets (blue) (n = 3). e, Kinetics of human C-peptide secretion from WNT4-treated HILOs in response to progressive exposure to 3 mM glucose, 20 mM glucose, 20 mM glucose + 100 nM GLP-1, 3 mM glucose, and 3 mM glucose + 20 mM KCl. f, Glucose-stimulated human C-peptide secretion from wHILOs treated with and without XAV939 to promote β-catenin degradation (XAV939, 1 μM for 3 days) (n = 8). g, Temporal gene expression (NANOG, NGN3, PDX1, INS) during differentiation of hiPS, HUES8 and H1ES cells to HILOs (upper panel). Insulin-driven GFP expression in day 21 HILOs derived from HUES8 (lower panel, scale bar 100 μm). h, In vitro C-peptide secretion in response to 3 mM (G3) and 20 mM (G20) glucose in wHILOs from HUES8 and H1ES (n = 3). i, Schematic depicting culture conditions for commercially available hiPSC-derived β-like cells (left) and light microscopy image of cultured cells (right, scale bar 100 μm). j, In vitro C-peptide secretion in response to 3 mM (G3) and 20 mM (G20) glucose from cultures described in i (n = 8). k, Blood glucose levels in STZ-induced diabetic NOD/SCID mice. Transplantation (TP) of 500 wHILOs, hislets, or sham surgery was performed at day 3 (n = 7, 6, and 3, respectively). l, Gene ontology of transcriptional changes induced by WNT4 treatment (100 ng ml−1 WNT4 from day 26 to day 33) in wHILOs. Error bars represent ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, one-tailed, Student’s paired t-test. Data representative of 3 independent experiments (bh, i lower panel, k) or experimental triplicates (i upper panel, l). Source data

Extended Data Fig. 4 WNT4 promotes mitochondrial maturation of HILOs.

a, Representative images of insulin-GFP expression and MitoTracker staining (red) in PBS- and WNT4-treated HILOs (scale bar, 100 μm). b, Flow cytometry quantification of insulin expression (GFP) and mitochondrial content in HILOs treated with recombinant human WNT4 (rhWNT4), WNT5A (rhWNT5A), or conditioned media (CM) from control or WNT5A overexpressing fibroblasts (n = 3). c, Venn diagram showing overlap between WNT4-induced increases in chromatin accessibility in GFP+ cells and increases in HILO gene expression (upper panel), and gene ontology pathways enriched in the intersection gene set. d, Motifs enriched in the intersection gene set from c. e, Chromatin accessibility at ERRγ target genes determined by ATAC–seq in insulin-expressing cells sorted from HILOs treated with PBS or WNT4 (wHILO) for 7 days (fold change >1.5). Error bars represent ± s.e.m. *P < 0.05, one-tailed, Student’s paired t-test. Data representative of 3 (a, b) or 2 (ce) independent experiments. Source data

Extended Data Fig. 5 ERRγ is required for WNT4-driven metabolic maturation.

a, b, Postnatal islets (day P11-14) from WT and β cell specific ERRγKO mice were cultured with or without rhWNT4 (100 ng ml−1) for >5 days. Relative gene expression measured by qPCR (a), and insulin secretion in response to 3 mM and 20 mM glucose (b). n = 3. Error bars represent ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, one-tailed, Student’s paired t-test. Data representative of 2 independent experiments (a, b). Source data

Extended Data Fig. 6 Immunofluorescence characterization of wHILOs.

a–c, Confocal images of wHILOs stained for C-peptide (a), β cell enriched markers NKX2-2, NKX6-1, MAFA, MAFB, PDX1 (b), and endocrine markers chromogranin A (CHGA), Synaptophysin (red) with Insulin-GFP (green) visualization (c). d, Magnification of 75 μm x 75 μm boxed regions shown in b and c. e, Immunofluorescence images of wHILOs showing insulin (GFP), β cell markers MAFA and MAFB, and α cell marker glucagon expression. Hoechst nuclei staining (blue). Scale bar: 100 μm (ac), 10 μm (e). Images are representative of 3 independent experiments.

Extended Data Fig. 7 Flow cytometry analysis of HILOs.

a, Representative flow cytometry results for β cell and endocrine marker co-staining in HILOs with and without WNT4 treatment. b, Quantification of results in a (n = 6). Source data

Extended Data Fig. 8 Single cell analysis of wHILOs.

a, t-SNE clustering of single cell transcriptomes from WNT4-treated HILOs (wHILOs, n = 4840). b, c, Violin plots (b) and single-cell expression (c) of INS, CHGA, SOX9, HES1 in wHILOs. d, Expression of β cell-enriched (INS, PDX1, NKX6-1, NKX2-2, NEUROD1, NPTX2, ITGA1, PCSK1, MAFA, MAFB, UCN3, CHGA), α cell-enriched (GCG, ARX) and δ cell-enriched genes (SST, RBP4) overlaid on t-SNE clustering. e, Heat map of top 10 differentially expressed genes in each cell cluster. f, t-SNE clusters coloured according to cell type (Panc P = pancreatic progenitor, Rep = replicating, UK = unknown). g, UMAP clustering of combined HILOs and wHILO single cell datasets after expression restoration of sparse counts using SAVER. h, Heat map shows the differentially expressed genes (logFC) between HILOs and wHILOs in ESRRG and INS double positive cells using WhichCells function (expression >0.1). i, Distribution of ERRγ (ESRRG; min: 0.10, 0.50; 1st Q: 0.14, 1.15; mean: 0.54, 1.22; 3rdQ 0.90, 1.56; max: 1.60, 1.60 for HILOs and wHILOs respectively), NDUFV3 (min: −0.31, 1.21; 1stQ; −0.01, 1.33; mean: 0.49, 1.67; 3rdQ 1.00, 1.98; max: 2.22, 2.14 for HILOs and wHILOs respectively), LDHA (min: 0.05, 1.02; 1stQ; 0.50, 1.21; mean: 1.35, 1.33; 3rdQ 1.90, 1.44; max: 3.53, 1.67 for HILOs and wHILOs respectively) gene expression in ESRRG and INS double-positive HILOs (n = 38) and wHILOs (n = 9). Data pooled from three independent samples (ai).

Extended Data Fig. 9 PD-L1 expression in HILOs.

a, Endogenous PD-L1 expression highlighted in red in human islet cells (n = 3,245) (β cells are outlined in red). b, Heat map of top differentially expressed genes between PD-L1+ and PD-L1- β cells. c, Immunohistochemistry showing overlap of lentiviral-driven PD-L1 expression and insulin promoter-driven GFP expression in wHILOs (scale bar, 100 μm). d, Human PD-L1 and human insulin expression in wHILOs with and without lentiviral PD-L1 overexpression, as measured by qPCR (n = 3). e, Transplantation of PD-L1 overexpressing wHILOs into the kidney capsule of STZ-induced diabetic mice. f, Blood glucose levels of C57BL6J mice treated with high dose streptozotocin (HD-STZ) before transplantation of wHILOs with and without PD-L1 overexpression (500 wHILOs into each kidney, total 1,000 wHILOs) (n = 3). g, PD-L1 expression in human islet 12 h after IFN-γ stimulation (n = 5). h, PD-L1 expression in wHILOs 12 h after indicated IFN-γ stimulation (n = 3). Error bars represent ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, one-tailed, Student’s paired t-test. Data were pooled from 3 independent samples (a) or representative of 3 independent experiments (c, d, g, h). Source data

Extended Data Fig. 10 Immune profiling of C57BL6J wHILO grafts.

a, Flow cytometry analysis of insulin expressing and mouse immune (CD45+) cells recovered from kidney capsule grafts 27 days after transplantation of wHILOs with and without PD-L1 expression. CD45+ cells were further categorized as B cells (CD19+), T cells (CD3+) and NK cells (NK1.1+). b, Quantification of a, (n = 6 and 6). c, wHILO (PD-L1) cells in kidney graft 27 days after transplantation (insulin promoter driven GFP expression). Scale bar, 100 μm. Error bars represent ± s.e.m. *P < 0.05, one tailed, Student’s paired t-test. Data representative of 2 independent experiments. Source data

Extended Data Fig. 11 IFN-γ-induced changes in wHILOs.

a, Venn diagram of differentially regulated genes upon acute (12 h at 10 ng ml−1) and multi pulse-stimulated (MPS; 2 h at 10 ng ml−1 for 3 days) IFN-γ treatment of wHILOs. b, Heat map of differentially expressed genes upon acute and MPS IFN-γ stimulation. Sustainable PD-L1 gene expression by MPS is indicated. c, Gene ontology of selectively regulated genes upon MPS-IFN-γ (top panel) and acute IFN-γ (bottom panel) treatments. d, Browser tracks showing chromatin accessibility at selected genes 7 days after last IFN-γ treatment in MPS or 12 h after acute IFN-γ stimulation in wHILOs. Data were from triplicate (ac) or duplicate (d) samples.

Extended Data Fig. 12 Immune evasive wHILOs by enhanced endogenous PD-L1 expression.

a, Schematic showing multi-low dose streptozotocin treatment (MLD-STZ, 50 mg/kg/day for 5 days) of Hu-PBMC-NSG mice to make immune competent diabetic model. MPS induced PD-L1 expressing wHILOs (n = 500) were transplanted under kidney capsule. b, Random-fed blood glucose levels in STZ-induced diabetic Hu-PBMC-NSG mice after transplantation of wHILOs with or without MPS (n = 6). Data for wHILOs (−) from Fig. 3c was used since those experiments were performed in parallel. c, Flow cytometry analyses of insulin-expressing and human immune (CD45+) cells recovered from kidney capsule grafts 27 days after transplantation of wHILOs with or without MPS. Error bars represent ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, one-tailed, Student’s paired t-test. Data were compiled from b or representative of 2 independent experiments. Source data

Supplementary information

Supplementary Tables

Supplementary Table 1: Differentially expressed genes during hADSC self organization. Gene Ontology: Top Features in each gene cluster of Extended Data Fig. 2c. P values for Gene Ontology (GO) analysis determined by default setting of DAVID42 1096. Supplementary Table 2: Summary of droplet-based scRNA-seq results. Supplementary Table 3: Differentially expressed genes between wHILOs and human islets. Gene Ontology: Top 200 differentially expressed genes of Fig. 2b. P value unadjusted (p_val) and adjusted (p_val_adj) for differentially expressed genes and p value (p value) and Benjamini correction (Benjamini) for Gene Ontology (GO) analysis were determined by default setting of Seurat40 and DAVID42 1105, respectively. Supplementary Table 4: Information on primers, antibodies and plasmids.

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Supplementary Table 5: Violin plot elements.

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Yoshihara, E., O’Connor, C., Gasser, E. et al. Immune-evasive human islet-like organoids ameliorate diabetes. Nature 586, 606–611 (2020). https://doi.org/10.1038/s41586-020-2631-z

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