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Human pluripotent stem-cell-derived islets ameliorate diabetes in non-human primates

Nature Medicine volume 28, pages 272–282 (2022)Cite this article

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Abstract

Human pluripotent stem-cell-derived islets (hPSC-islets) are a promising cell resource for diabetes treatment1,2. However, this therapeutic strategy has not been systematically assessed in large animal models physiologically similar to humans, such as non-human primates3. In this study, we generated islets from human chemically induced pluripotent stem cells (hCiPSC-islets) and show that a one-dose intraportal infusion of hCiPSC-islets into diabetic non-human primates effectively restored endogenous insulin secretion and improved glycemic control. Fasting and average pre-prandial blood glucose levels significantly decreased in all recipients, accompanied by meal or glucose-responsive C-peptide release and overall increase in body weight. Notably, in the four long-term follow-up macaques, average hemoglobin A1c dropped by over 2% compared with peak values, whereas the average exogenous insulin requirement reduced by 49% 15 weeks after transplantation. Collectively, our findings show the feasibility of hPSC-islets for diabetic treatment in a preclinical context, marking a substantial step forward in clinical translation of hPSC-islets.

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Fig. 1: hCiPSC-derived islets generated in vitro resemble primary human islets and reverse diabetes in mice.
Fig. 2: Intraportal infusion of hCiPSC-islets led to improvement of glycemic control in immunosuppressed diabetic rhesus macaques.
Fig. 3: hCiPSC-islet transplanted diabetic macaques showed significant reduction of exogenous insulin requirement and overall increase of body weight.
Fig. 4: Detection of secreted C-peptide in hCiPSC-islet transplanted diabetic rhesus macaques.
Fig. 5: Amelioration of diabetes by intrahepatic infusion of hCiPSC-islets into a macaque (Monkey 5), which was treated with exogenous insulin for 6 months after STZ injection.

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Data availability

The RNA sequencing data reported in this paper have been deposited in the Gene Expression Omnibus under accession number GSE185038. Any other requests for raw or processed data will be reviewed by the Peking University Stem Cell Research Centre to verify whether the data requested are subject to any intellectual property or confidentiality obligations. Data and materials that can be shared will be released via a material transfer agreement. Source data are provided with this paper.

References

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

    Article  CAS  PubMed  Google Scholar 

  2. Nair, G. G., Tzanakakis, E. S. & Hebrok, M. Emerging routes to the generation of functional β-cells for diabetes mellitus cell therapy. Nat. Rev. Endocrinol. 16, 506–518 (2020).

    Article  PubMed  Google Scholar 

  3. Harding, J. D. Nonhuman primates and translational research: progress, opportunities, and challenges. ILAR J. 58, 141–150 (2017).

    Article  CAS  PubMed  Google Scholar 

  4. Katsarou, A. et al. Type 1 diabetes mellitus. Nat. Rev. Dis. Prim. 3, 17016 (2017).

    Article  PubMed  Google Scholar 

  5. Vantyghem, M. C., de Koning, E. J. P., Pattou, F. & Rickels, M. R. Advances in β-cell replacement therapy for the treatment of type 1 diabetes. Lancet 394, 1274–1285 (2019).

    Article  CAS  PubMed  Google Scholar 

  6. Shapiro, A. M. et al. Islet transplantation in seven patients with type 1 diabetes mellitus using a glucocorticoid-free immunosuppressive regimen. N. Engl. J. Med. 343, 230–238 (2000).

    Article  CAS  PubMed  Google Scholar 

  7. Shapiro, A. M., Pokrywczynska, M. & Ricordi, C. Clinical pancreatic islet transplantation. Nat. Rev. Endocrinol. 13, 268–277 (2017).

    Article  CAS  PubMed  Google Scholar 

  8. Rickels, M. R. & Robertson, R. P. Pancreatic islet transplantation in humans: recent progress and future directions. Endocr. Rev. 40, 631–668 (2019).

    Article  PubMed  Google Scholar 

  9. Gibly, R. F. et al. Advancing islet transplantation: from engraftment to the immune response. Diabetologia 54, 2494–2505 (2011).

    Article  CAS  PubMed  Google Scholar 

  10. Lacotte, S., Berney, T., Shapiro, A. J. & Toso, C. Immune monitoring of pancreatic islet graft: towards a better understanding, detection and treatment of harmful events. Expert Opin. Biol. Ther. 11, 55–66 (2011).

    Article  PubMed  Google Scholar 

  11. Johnson, J. D. et al. Different effects of FK506, rapamycin, and mycophenolate mofetil on glucose-stimulated insulin release and apoptosis in human islets. Cell Transplant. 18, 833–845 (2009).

    Article  PubMed  Google Scholar 

  12. Tanemura, M. et al. Rapamycin induces autophagy in islets: relevance in islet transplantation. Transplant. Proc. 41, 334–338 (2009).

    Article  CAS  PubMed  Google Scholar 

  13. Blau, H. M. & Daley, G. Q. Stem cells in the treatment of disease. N. Engl. J. Med. 380, 1748–1760 (2019).

    Article  CAS  PubMed  Google Scholar 

  14. Harding, J., Roberts, R. M. & Mirochnitchenko, O. Large animal models for stem cell therapy. Stem Cell Res. Ther. 4, 23 (2013).

    Article  PubMed  Google Scholar 

  15. 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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Liu, H. et al. Systematically labeling developmental stage-specific genes for the study of pancreatic β-cell differentiation from human embryonic stem cells. Cell Res. 24, 1181–1200 (2014).

    Article  CAS  PubMed  Google Scholar 

  18. Hogrebe, N. J. & Augsornworawat, P. Targeting the cytoskeleton to direct pancreatic differentiation of human pluripotent stem cells. Nat. Biotechnol. 38, 460–470 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Veres, A. et al. Charting cellular identity during human in vitro β-cell differentiation. Nature 569, 368–373 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hou, P. et al. Pluripotent stem cells induced from mouse somatic cells by small-molecule compounds. Science 341, 651–654 (2013).

    Article  CAS  PubMed  Google Scholar 

  21. Zhao, T. et al. Single-cell RNA-seq reveals dynamic early embryonic-like programs during chemical reprogramming. Cell Stem Cell 23, 31–45 (2018).

    Article  CAS  PubMed  Google Scholar 

  22. Li, X. et al. Small-molecule-driven direct reprogramming of mouse fibroblasts into functional neurons. Cell Stem Cell 17, 195–203 (2015).

    Article  CAS  PubMed  Google Scholar 

  23. Augsornworawat, P., Maxwell, K. G., Velazco-Cruz, L. & Millman, J. R. Single-cell transcriptome profiling reveals β cell maturation in stem cell-derived islets after transplantation. Cell Rep. 32, 108067 (2020).

    Article  CAS  PubMed  Google Scholar 

  24. Zhu, H., Yu, L., He, Y. & Wang, B. Nonhuman primate models of type 1 diabetes mellitus for islet transplantation. J. Diabetes Res. 2014, 785948 (2014).

    Article  PubMed  Google Scholar 

  25. Hecht, G. et al. Embryonic pig pancreatic tissue for the treatment of diabetes in a nonhuman primate model. Proc. Natl Acad. Sci. USA 106, 8659–8664 (2009).

    Article  CAS  PubMed  Google Scholar 

  26. Shin, J. S. et al. Long-term control of diabetes in immunosuppressed nonhuman primates (NHP) by the transplantation of adult porcine islets. Am. J. Transplant. 15, 2837–2850 (2015).

    Article  CAS  PubMed  Google Scholar 

  27. Tattersall, R. B. Brittle diabetes revisited: the Third Arnold Bloom Memorial lecture. Diabet. Med. 14, 99–110 (1997).

    Article  CAS  PubMed  Google Scholar 

  28. Hirsch, I. B. & Gaudiani, L. M. A new look at brittle diabetes. J. Diabetes Complications 35, 107646 (2021).

  29. Coe, T. M., Markmann, J. F. & Rickert, C. G. Current status of porcine islet xenotransplantation. Curr. Opin. Organ Transplant. 25, 449–456 (2020).

    Article  PubMed  Google Scholar 

  30. Kim, J. M. & Hong, S. H. Long-term porcine islet graft survival in diabetic non-human primates treated with clinically available immunosuppressants. Xenotransplantation 28, e12659 (2021).

    Article  PubMed  Google Scholar 

  31. Muthyala, S. et al. The effect of hypoxia on free and encapsulated adult porcine islets—an in vitro study. Xenotransplantation 24, 10.1111/xen.12275 (2017).

  32. Shin, J. S. et al. Failure of transplantation tolerance induction by autologous regulatory T cells in the pig-to-non-human primate islet xenotransplantation model. Xenotransplantation 23, 300–309 (2016).

    Article  PubMed  Google Scholar 

  33. Graham, M. L., Bellin, M. D., Papas, K. K., Hering, B. J. & Schuurman, H. J. Species incompatibilities in the pig-to-macaque islet xenotransplant model affect transplant outcome: a comparison with allotransplantation. Xenotransplantation 18, 328–342 (2011).

    Article  PubMed  Google Scholar 

  34. Delaune, V., Berney, T., Lacotte, S. & Toso, C. Intraportal islet transplantation: the impact of the liver microenvironment. Transpl. Int. 30, 227–238 (2017).

    Article  PubMed  Google Scholar 

  35. Association, A. D. Standards of medical care in diabetes—2014. Diabetes Care 37, S14–S80 (2014).

    Article  Google Scholar 

  36. Sherwani, S. I., Khan, H. A., Ekhzaimy, A., Masood, A. & Sakharkar, M. K. Significance of HbA1c test in diagnosis and prognosis of diabetic patients. Biomark. Insights 11, 95–104 (2016).

    Article  CAS  PubMed  Google Scholar 

  37. Ponticelli, C. & Passerini, P. Gastrointestinal complications in renal transplant recipients. Transpl. Int. 18, 643–650 (2005).

    Article  PubMed  Google Scholar 

  38. Peters, A. et al. Posttransplant lymphoproliferative disorder after clinical islet transplantation: report of the first two cases. Am. J. Transplant. 17, 2474–2480 (2017).

    Article  CAS  PubMed  Google Scholar 

  39. Sečník, P. Jr. et al. Immunoglobulin abnormalities in 1677 solid organ transplant recipients. Implications for posttransplantation follow-up. Transpl. Immunol. 57, 101229 (2019).

    Article  PubMed  Google Scholar 

  40. Dufrane, D. et al. Streptozotocin-induced diabetes in large animals (pigs/primates): role of GLUT2 transporter and β-cell plasticity. Transplantation 81, 36–45 (2006).

    Article  CAS  PubMed  Google Scholar 

  41. Saisho, Y. et al. Ongoing β-cell turnover in adult nonhuman primates is not adaptively increased in streptozotocin-induced diabetes. Diabetes 60, 848–856 (2011).

    Article  CAS  PubMed  Google Scholar 

  42. Koulmanda, M. et al. The effect of low versus high dose of streptozotocin in cynomolgus monkeys (Macaca fascilularis). Am. J. Transplant. 3, 267–272 (2003).

    Article  CAS  PubMed  Google Scholar 

  43. Bottino, R. et al. Recovery of endogenous β-cell function in nonhuman primates after chemical diabetes inducation islet. Transplant. Diabetes 58, 442–447 (2009).

    Article  CAS  PubMed  Google Scholar 

  44. Zhu, Y., Liu, Q., Zhou, Z. & Ikeda, Y. PDX1, neurogenin-3, and MAFA: critical transcription regulators for beta cell development and regeneration. Stem Cell Res. Ther. 8, 240 (2017).

    Article  CAS  PubMed  Google Scholar 

  45. Bastidas-Ponce, A. et al. Foxa2 and Pdx1 cooperatively regulate postnatal maturation of pancreatic β-cells. Mol. Metab. 6, 524–534 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Nordmann, T. M. et al. The role of inflammation in β-cell dedifferentiation. Sci. Rep. 7, 6285 (2017).

    Article  PubMed  Google Scholar 

  47. Eizirik, D. L., Colli, M. L. & Ortis, F. The role of inflammation in insulitis and β-cell loss in type 1 diabetes. Nat. Rev. Endocrinol. 5, 219–226 (2009).

    Article  CAS  PubMed  Google Scholar 

  48. Triñanes, J. et al. Deciphering tacrolimus-induced toxicity in pancreatic β cells. Am. J. Transplant. 17, 2829–2840 (2017).

    Article  PubMed  Google Scholar 

  49. Gruessner, R. W. & Gruessner, A. C. What defines success in pancreas and islet transplantation—insulin independence or prevention of hypoglycemia? A review. Transplant. Proc. 46, 1898–1899 (2014).

    Article  CAS  PubMed  Google Scholar 

  50. Barton, F. B. et al. Improvement in outcomes of clinical islet transplantation: 1999–2010. Diabetes Care 35, 1436–1445 (2012).

    Article  CAS  PubMed  Google Scholar 

  51. Stratton, I. M. et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 321, 405–412 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Warnock, G. L. et al. A multi-year analysis of islet transplantation compared with intensive medical therapy on progression of complications in type 1 diabetes. Transplantation 86, 1762–1766 (2008).

    Article  PubMed  Google Scholar 

  53. Lablanche, S. et al. Islet transplantation versus insulin therapy in patients with type 1 diabetes with severe hypoglycaemia or poorly controlled glycaemia after kidney transplantation (TRIMECO): a multicentre, randomised controlled trial.Lancet Diabetes Endocrinol. 6, 527–537 (2018).

    Article  CAS  PubMed  Google Scholar 

  54. Kojayan, G. G., Alexander, M., Imagawa, D. K. & Lakey, J. R. T. Systematic review of islet cryopreservation. Islets 10, 40–49 (2018).

    Article  CAS  PubMed  Google Scholar 

  55. Suman, S., Domingues, A., Ratajczak, J. & Ratajczak, M. Z. Potential clinical applications of stem cells in regenerative medicine. Adv. Exp. Med. Biol. 1201, 1–22 (2019).

    Article  CAS  PubMed  Google Scholar 

  56. Hentze, H. et al. Teratoma formation by human embryonic stem cells: evaluation of essential parameters for future safety studies. Stem Cell Res. 2, 198–210 (2009).

    Article  PubMed  Google Scholar 

  57. Martin, R. M. et al. Improving the safety of human pluripotent stem cell therapies using genome-edited orthogonal safeguards. Nat. Commun. 11, 2713 (2020).

    Article  CAS  PubMed  Google Scholar 

  58. Kimura, Y. et al. Human genomic safe harbors and the suicide gene-based safeguard system for iPSC-based cell therapy. Stem Cells Transl. Med. 8, 627–638 (2019).

    Article  CAS  PubMed  Google Scholar 

  59. Wiebking, V., Patterson, J. O. & Martin, R. Metabolic engineering generates a transgene-free safety switch for cell therapy. Nat. Biotechnol. 38, 1441–1450 (2020).

    Article  CAS  PubMed  Google Scholar 

  60. Bochenek, M. A. et al. Alginate encapsulation as long-term immune protection of allogeneic pancreatic islet cells transplanted into the omental bursa of macaques. Nat. Biomed. Eng. 2, 810–821 (2018).

    Article  CAS  PubMed  Google Scholar 

  61. Ludwig, B. et al. Transplantation of human islets without immunosuppression. Proc. Natl Acad. Sci. USA 110, 19054–19058 (2013).

    Article  CAS  PubMed  Google Scholar 

  62. Lembert, N. et al. Areal density measurement is a convenient method for the determination of porcine islet equivalents without counting and sizing individual islets. Cell Transplant. 12, 33–41 (2003).

    Article  CAS  PubMed  Google Scholar 

  63. Zhang, B. et al. A DNAH17 missense variant causes flagella destabilization and asthenozoospermia. J. Exp. Med. 217, e20182365 (2020).

  64. Peters, P. J. & Pierson, J. Immunogold labeling of thawed cryosections. Methods Cell Biol. 88, 131–149 (2008).

    Article  CAS  PubMed  Google Scholar 

  65. Slot, J. W. & Geuze, H. J. Cryosectioning and immunolabeling. Nat. Protoc. 2, 2480–2491 (2007).

    Article  CAS  PubMed  Google Scholar 

  66. Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Lun, A. T. L. et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  68. McCarthy, D. J., Campbell, K. R., Lun, A. T. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).

  69. Lun, A. T., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res. 5, 2122 (2016).

    PubMed  PubMed Central  Google Scholar 

  70. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Enge, M. et al. Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns. Cell 171, 321–330 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. de Sena Brandine, G. & Smith, A. D. Falco: high-speed FastQC emulation for quality control of sequencing data. F1000Res. 8, 1874 (2019).

    Article  PubMed  Google Scholar 

  75. Li, J., Galbo, P. M. Jr. & Gong, W. ZMYND11-MBTD1 induces leukemogenesis through hijacking NuA4/TIP60 acetyltransferase complex and a PWWP-mediated chromatin association mechanism. Nat. Commun. 12, 1045 (2021).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  77. Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  79. 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 

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2017YFA0103000 to H.D., 2018YFA0108102 to H.D. and 2020YFA0803704 to S.W.); the National Natural Science Foundation of China (31521004 to H.D., 31730059 to H.D., 82070805 to S.W., 81870535 to S.W., 82100840 to T.L. and 82100841 to R.L.); the Key Project and Team Program of Tianjin (XB202011 to S.W.); and the CAMS Innovation Fund for Medical Sciences (2021-1-I2M-024 to X.P.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank S. Lu at the Department of Hepatobiliary Surgery, Chinese PLA General Hospital, Beijing, for assistance with providing hADFs; Z. Chen at Shanghai Institute of Biochemistry and Cell Biology for assistance with the dynamic glucose-stimulated insulin secretion assay; Y. Li of the Institute of Medical Biology of CAMS for routine blood and biochemical tests; Q. Yao at the Department of Clinical Pharmacology of First Affiliated Hospital of Kunming Medical University for therapeutic drug monitoring; C. Yang of the Center of Cryo-Electron Microscopy of Zhejiang University for assistance on cryo-electron microscopy; and Y. Hu, Y. Xie and P. Dong at the Core Facilities of School of Life Sciences and the National Center for Protein Sciences at Peking University for their professional technical assistance in electron microscopy sample preparation and image analysis. We thank J. Vaughan at the Salk Institute for Biological Studies and M. Huising at the University of California, Davis for their kind provision of the UCN3 antibody. We thank the following people for their contributions to this study: J. Cao, L. Zou, J. Bai, Y. Yan, F. Bai and L. Xu for clinical assistance with the macaques; Y. Yang and D. Zhang for guidance on immune response experiments; X. Fang, L. Zhao, T. Zhang, J. Ma and H. Liu for technical assistance; X. Zhou for assistance with animal care; C. Wang for guidance on matters of animal research ethics; and B. Liu, W. Lai, J. Xu, Y. Fu, L. Cheng and Y. Lv for discussions in the course of the preparation of this manuscript.

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Author notes

  1. These authors contributed equally: Yuanyuan Du, Zhen Liang, Shusen Wang, Dong Sun, Xiaofeng Wang, Liew Soon Yi, Shuaiyao Lu.

Authors and Affiliations

  1. MOE Engineering Research Center of Regenerative Medicine, School of Basic Medical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China

    Yuanyuan Du, Zhen Liang, Dong Sun, Soon Yi Liew, Zhengyuan Zhang, Jingyang Guan, Jinlin Wang, Shicheng Sun, Gaofan Meng, Yinan Liu & Hongkui Deng

  2. Hangzhou Reprogenix Bioscience, Hangzhou, China

    Yuanyuan Du, Zhen Liang, Xiaofeng Wang, Shuangshuang Wu, Yong Jiang, Zhi Lu, Yanling Wei, Gaofan Meng & Jiabin Gu

  3. Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, China

    Shusen Wang, Boya Zhang, Yun Zhang, Rui Liang, Tengli Liu, Le Wang & Zhongyang Shen

  4. Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan, China

    Shuaiyao Lu, Wenhai Yu, Haiting Long & Xiaozhong Peng

  5. School of Life Sciences, Center for Bioinformatics, Peking University, Beijing, China

    Yaqi Wang & Cheng Li

  6. Hangzhou Repugene Technology, Hangzhou, China

    Yue Pu, Shanshan Xiao & Tao Wang

  7. Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China

    Huixia Ren & Chao Tang

  8. State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China

    Jiaxu Zhao

  9. State Key Laboratory of Medical Molecular Biology, Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Medical Primate Research Center, Neuroscience Center, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China

    Xiaozhong Peng

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  1. Yuanyuan Du
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Contributions

H.D., X.P. and Z.S. supervised the research. H.D. and Y.D. conceived of the experimental design. Z.L., D.S. and S.L. performed most of the non-human primate experiments. S.W. and B.Z. performed cell transplantation surgery in non-human primates. X.W., L.S.Y., Y. L. and G.M. performed the main in vitro experiments. Y.D. and L.S.Y. wrote the manuscript. S.W., Z.L., Y.Z. and Y.W. prepared the cells for transplantation into non-human primates. Y.J. and J.G. performed mice transplantation and conducted related testing. C.L., Y.W., Y.P., S.X. and T.W. performed bulk and single-cell transcriptome sequencing and data analysis. W.Y. and H.L. performed the postmortem anatomical analysis of monkeys and related tests. Z.Z., J.G. and J.W. established the hCiPSC lines. H.R. and C.T. performed the calcium flux assay. J.Z. and Z.C. performed the dynamic glucose-stimulated insulin secretion assay. S.W., R.L., T.L. and L.W. isolated human islets from donor pancreata. S.S. edited and reviewed the manuscript.

Corresponding authors

Correspondence to Zhongyang Shen, Xiaozhong Peng or Hongkui Deng.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Establishment of an efficient hCiPSC-islet generation protocol and characterization of hCiPSC-islets.

a, Flow cytometry analysis comparing differentiation efficiencies between planar culture and suspension culture at various stages of the protocol in terms of pancreatic progenitor markers at the end of Stage 4 and β cell markers at the end of Stage 6 (n = 5). b, Flow cytometry analysis of β cell marker expression in Stage 6 aggregates without and with addition of small molecules ISX9 and Wnt-C59, individually or in combination at Stage 5, detected at S6D2 (n = 4). c, Continuous stage-wise tracking of pancreatic progenitor, endocrine progenitor, and β cell markers by flow cytometry throughout the differentiation protocol (n = 3). d, qRT-PCR analysis of key pancreatic β cell genes in hCiPSC-islets aggregates (n = 6) and human islets (n = 5). e, Representative immunostaining of key β cell transcription factors in sectioned hCiPSC-islets. Scale bar, 50 μm. f, Continuous stage-wise tracking of UCN3 expression by qRT-PCR analysis during hCiPSC-islet differentiation (n = 3) and in human islet sample (n = 3). Relative gene expression was normalized to hCiPSCs (n = 3). g, Immunofluorescence staining of UCN3, C-peptide and GCG in sectioned hCiPSC-islet and sectioned human pancreas as control. Scale bar, 25 μm (top), 50 μm (bottom). Similar results were obtained on three independent hCiPSC differentiation batches. Data presented as mean values ± s.e.m.

Source data

Extended Data Fig. 2 Characterization of glucose stimulated responses and granule properties of hCiPSC-islets.

a, C-peptide secretion of Stage 6 aggregates (n = 7) and primary human islets (n = 6) in static glucose stimulation assay under low glucose (2.8 mM), high glucose (16.7 mM) and depolarization by 30 mM KCl. Glucose stimulation index as indicated above bars. b, Insulin secretion of human islets (top; n = 4) and hCiPSC-islets (bottom; n = 5) in dynamic perifusion assay. c, Dynamic Cal-520-AM fluorescence intensity trace of human islets (top; n = 10) and hCiPSC-islets (bottom; n = 10) during sequential glucose challenge with low (2 mM), high (20 mM) glucose or depolarization with 30 mM KCl. d, Representative immuno-electron micrographs of secretory granules double immunogold labeled with insulin (6 nm) and glucagon (15 nm), with enlarged images of an individual granule shown on the right. Scale bar, 500 nm. e, Representative transmission electron micrographs of hCiPSC-islet cells (left) and human islets (right), showing polymorphous crystalline insulin granules (top) or glucagon granules (bottom), with magnified images of representative granules shown on the right. Scale bar, 1 μm. f, Proportions of insulin, glucagon and mixed granule containing cells quantified by morphological analysis of TEM images of hCiPSC-islets (n = 6). Data presented as mean values ± s.e.m.

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Extended Data Fig. 3 hCiPSC-islets restored glucose clearance and improved overall survival when transplanted into diabetic mice.

a, Left: representative image of nephrectomized kidney showing the hCiPSC-islet graft beneath the kidney capsule. Scale bar, 0.1 cm. Middle, right: H&E histology of kidney section, depicting hCiPSC-islet graft and graft vascularization. Scale bar, 200 μm (middle), 75 μm (right). b, Representative immunofluorescence staining of GCG and key β cell transcription factors PDX1 and NKX6.1 in hCiPSC-islet graft sections at 16 wpt. Scale bar, 50 μm. c, Quantification of SST and C-peptide expressing subpopulations in hCiPSC-islet graft sections at 16 wpt (n = 4). d, Tracking of body weight of hCiPSC-islet transplanted diabetic mice (n = 22). e, Changes in blood glucose levels in response to intraperitoneal glucose tolerance test (IPGTT) of healthy (n = 8) and STZ-induced diabetic mice groups with (n = 17) and without (n = 8) hCiPSC-islet transplantation at 16 wpt. f, Survival rate of STZ-induced diabetic mice groups with (red; n = 63) and without (black; n = 22) hCiPSC-islet transplantation. Data presented as mean values ± s.e.m.

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Extended Data Fig. 4 The established differentiation protocol performed stably across hCiPS cell lines.

Similar marker expression pattern and capacity for hyperglycemia reversal were observed across three other hCiPSC lines subject to the established differentiation protocol. a, Representative flow cytometry of pancreatic developmental markers during differentiation showed similar distribution and efficiencies along progressive stages across three other hCiPSC lines. b, Representative immunofluorescence staining of islet hormones of hCiPSC-islet sections derived from three other independent hCiPSC lines. Scale bar, 50 μm. c, Long-term tracking of fasting blood glucose (left) and body weight (right) in diabetic mice transplanted with hCiPSC-islets derived from three other independent hCiPSC lines. d, Long-term tracking of fasting human C-peptide secretion in non-diabetic mice. In c-d, n = 21, 15 and 22 animals transplanted for hCiPSC line #2, #3 and #4 respectively. e-f, Representative immunofluorescence staining of β cell markers (e) and islet hormones and maturation marker UCN3 (f) in hCiPSC-islet graft at 48 wpt. Scale bar, 50 μm. Data presented as mean values ± s.e.m.

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Extended Data Fig. 5 Postmortem examination of major organs in transplanted diabetic macaques.

Gross anatomy (a, c, e, g) and H&E staining (b, d, f, h) of major organs of Monkey-#1 (a-b), Monkey-#2 (c-d), Monkey-#3 (e-f) and Monkey-#4 (g-h). Scale bar, 400 μm.

Extended Data Fig. 6 Gross anatomy, histo- and immunological analysis of native pancreas of STZ-treated recipient monkeys.

a, Gross anatomy and H&E staining of pancreas of healthy monkey and recipient Monkey-#1 to #4, with islet structures outlined in green. Scale bar, 50 μm. b, Left: C-peptide staining of pancreas sections of healthy control monkey and STZ-treated recipient Monkey-#1 to #4. Scale bar, 400 μm. Middle, Right: Magnified panels of boxed areas. Scale bar, 100 μm. In contrast to pancreas sections of healthy control monkey, pancreas sections of STZ-induced recipient monkeys showed extremely low occurrence of C-peptide positive cells (indicated by yellow arrowheads), with most islets (encircled in red) showing no C-peptide positive cells. c-d, Representative immunofluorescence staining of endocrine marker CHGA (c) and islets hormone C-peptide, GCG and SST (d) in pancreas of STZ-treated recipient Monkey-#1 to #4. Scale bar, 50 μm.

Extended Data Fig. 7 Immuno- and histological analysis of intrahepatic hCiPSC-islet grafts.

a, Representative immunofluorescence staining of GCG and β cell transcription factors PDX1 and NKX6.1 in intrahepatic graft of Monkey-#3 at 101 dpt. Scale bar, 50 μm. b, Proportions of C-peptide positive and GCG positive cells in the intraportal-islet grafts in liver sections of Monkey-#3 (n = 35). c, Immunohistochemistry staining of human cell-specific marker (Stem121), T cell marker (CD3), B cell marker (CD20) and macrophage marker (CD68) on liver sections of Monkey-#3. Magnified panels shown in bottom row. Scale bar, 200 μm (top), 50 μm (bottom). Data presented as mean values ± s.e.m.

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Extended Data Fig. 8 Metabolic testing and immuno- and histological analysis of Monkey-#5.

a, C-peptide secretion in response to glucose potentiated arginine (Arg) stimulation, conducted at 9 wpt (n = 3, technical replicates). b, Gross anatomy (top) and H&E staining (bottom) of major organs of Monkey-#5. Scale bar, 400 μm. c, Gross anatomy (left) of pancreas and H&E staining of pancreas section (right) of Monkey-#5, with islet structure outlined in green. Scale bar, 100 μm. d, Left: C-peptide staining of pancreas sections of STZ-treated recipient Monkey-#5. Scale bar, 400 μm. Middle, Right: magnified panels of boxed areas. C-peptide positive cells are indicated by yellow arrowheads. Scale bar, 50 μm. e-f, Representative immunofluorescence staining of endocrine marker CHGA (e) and islet hormones (f) of pancreatic islets post-STZ treatment in pancreas sections of Monkey-#5. Scale bar, 50 μm. g, Representative immunofluorescence staining of GCG and key β cell markers PDX1 and NKX6.1 in intrahepatic hCiPSC-islet grafts in Monkey-#5 liver sections. Scale bar, 50 μm. h, Proportions of C-peptide and GCG positive cells in hCiPSC-islet grafts, quantified from immunofluorescence staining of liver sections (n = 27). Data presented as mean values ± s.e.m.

Source data

Extended Data Fig. 9 Immune responses to hCiPSC-islets detected in Monkey-#5.

a-b, Flow cytometry histograms (a) and peak fluorescence intensities (b) depicting fluorescence shift in detection of monkey immunoglobulin G (IgG) in hCiPSC-islet cells co-incubated with serum of recipient Monkey-#5 (sampled at 8 wpt), and serum of two non-transplanted monkeys (Without Tx-#1 and #2) as controls (n = 4). c-d, Flow cytometry histograms (c) and quantification (d) of Annexin V-positive populations in hCiPSC-islet cells co-incubated with serum of recipient Monkey-#5 and serum of two non-transplanted monkeys (Without Tx-#1 and #2) as controls in complement dependent cytotoxicity assay (n = 4). Serum dilutions and incubation periods as indicated on the left. e, Immunofluorescence detection of complement (C4) deposition in hepatic hCiPSC-islet grafts in liver sections of Monkey-#5. Scale bar, 50 μm. f, Immunohistochemistry staining of CD3 (T cell marker) in hCiPSC-islet containing liver sections of Monkey-#5. Scale bar, 50 μm. g, Representative bright field images of IFN-γ ELISpot wells incubated with peripheral blood mononuclear cells (PBMC) of two non-transplanted monkeys (Without Tx-#1 and #2) or Monkey-#5 (sampled pre-transplantation (Pre-Tx) or at 8 wpt), stimulated with hCiPSC-islets. PBMCs of Monkey-#5 post-transplant (8 wpt) alone (Null) or incubated with CD3 antibody were applied as negative and positive control. h, Number of spots (left) detected in IFN-γ ELISpot assay and cytokine activity (right) of various incubation conditions as quantified by ELISpot reader analysis (n = 3, technical replicates). Data presented as mean values ± s.e.m.

Source data

Extended Data Fig. 10 Characterization of PDX1+NKX6.1+C-peptide− cells of hepatic hCiPSC-islet grafts by immunofluorescence staining.

a, PDX1+NKX6.1+C-peptide− cells were detected in the hepatic hCiPSC-islet grafts at postmortem analysis. They co-express pancreatic endocrine transcription factors (NKX2.2 and NeuroD1) and endodermal transcription factor (FOXA2). b, hCiPSC-islet grafts were negative for pancreatic endocrine progenitor marker (NGN3), ductal cell marker (SOX9) or acinar cell marker (CPA1). Corresponding positive staining controls on the right (hCiPSC-derived endocrine progenitors at Stage 5, day 2 or adult pancreas tissue section). c, hCiPSC-islet grafts were negative for markers of liver (ALB) and intestine (CDX2) tissue. Corresponding positive staining controls shown on the right (adult liver and duodenal tissue section). Scale bar, 50 μm.

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Du, Y., Liang, Z., Wang, S. et al. Human pluripotent stem-cell-derived islets ameliorate diabetes in non-human primates. Nat Med 28, 272–282 (2022). https://doi.org/10.1038/s41591-021-01645-7

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