Emerging approaches and technologies in transplantation: the potential game changers

Abstract

Newly emerging technologies are rapidly changing conventional approaches to organ transplantation. In the modern era, the key challenges to transplantation include (1) how to best individualize and possibly eliminate the need for life-long immunosuppression and (2) how to expand the donor pool suitable for human transplantation. This article aims to provide readers with an updated review of three new technologies that address these challenges. First, single-cell RNA sequencing technology is rapidly evolving and has recently been employed in settings related to transplantation. The new sequencing data indicate an unprecedented cellular heterogeneity within organ transplants, as well as exciting new molecular signatures involved in alloimmune responses. Second, sophisticated nanotechnology platforms provide a means of therapeutically delivering immune modulating reagents to promote transplant tolerance. Tolerogenic nanoparticles with regulatory molecules and donor antigens are capable of targeting host immune responses with tremendous precision, which, in some cases, results in donor-specific tolerance. Third, CRISPR/Cas9 gene editing technology has the potential to precisely remove immunogenic molecules while inserting desirable regulatory molecules. This technology is particularly useful in generating genetically modified pigs for xenotransplantation to solve the issue of the shortage of human organs. Collectively, these new technologies are positioning the transplant community for major breakthroughs that will significantly advance transplant medicine.

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References

  1. 1.

    Wu, H., Kirita, Y., Donnelly, E. L. & Humphreys, B. D. Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: Rare cell types and novel cell states revealed in fibrosis. J. Am. Soc. Nephrol. 30, 23–32 (2019).

  2. 2.

    Naeimi Kararoudi, M. et al. Clustered regularly interspaced short palindromic repeats/Cas9 gene editing technique in xenotransplantation. Front. Immunol. 9, 1711 (2018).

  3. 3.

    Tasciotti, E. et al. The emerging role of nanotechnology in cell and organ transplantation. Transplantation 100, 1629–1638 (2016).

  4. 4.

    Wang, J. & Song, Y. Single cell sequencing: a distinct new field. Clin. Transl. Med. 6, 10 (2017).

  5. 5.

    Kitchens, W. H. & Adams, A. B. Nonhuman primate models of transplant tolerance: closer to the holy grail. Curr. Opin. Organ Transplant. 21, 59–65 (2016).

  6. 6.

    Xie, J., Lee, S. & Chen, X. Nanoparticle-based theranostic agents. Adv. Drug Deliv. Rev. 62, 1064–1079 (2010).

  7. 7.

    Chopra, B. & Sureshkumar, K. K. Changing organ allocation policy for kidney transplantation in the United States. World J. Transplant. 5, 38–43 (2015).

  8. 8.

    Meier, R. P. H. et al. Xenotransplantation: back to the future? Transpl. Int. 31, 465–477 (2018).

  9. 9.

    Altschuler, S. J. & Wu, L. F. Cellular heterogeneity: do differences make a difference? Cell 141, 559–563 (2010).

  10. 10.

    Wu, H. & Humphreys, B. D. The promise of single-cell RNA sequencing for kidney disease investigation. Kidney Int. 92, 1334–1342 (2017).

  11. 11.

    Villani, A. C., et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science. 2017; 356. https://doi.org/10.1126/science.aah4573.

  12. 12.

    Shalek, A. K. et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363–369 (2014).

  13. 13.

    Chu, L. F. et al. Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm. Genome Biol. 17, 173 (2016).

  14. 14.

    Lambrechts, D. et al. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat. Med. 24, 1277–1289 (2018).

  15. 15.

    Lee, M. C. et al. Single-cell analyses of transcriptional heterogeneity during drug tolerance transition in cancer cells by RNA sequencing. Proc. Natl Acad. Sci. USA 111, E4726–E4735 (2014).

  16. 16.

    Sun, Z. et al. Single-cell RNA sequencing reveals gene expression signatures of breast cancer-associated endothelial cells. Oncotarget 9, 10945–10961 (2018).

  17. 17.

    Zhao, T. et al. Single-cell RNA-seq reveals dynamic early embryonic-like programs during chemical reprogramming. Cell Stem Cell 23, 31–45.e7 (2018).

  18. 18.

    Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

  19. 19.

    Tung, P. Y. et al. Batch effects and the effective design of single-cell gene expression studies. Sci. Rep. 7, 39921 (2017).

  20. 20.

    Wang, Y. & Navin, N. E. Advances and applications of single-cell sequencing technologies. Mol. Cell 58, 598–609 (2015).

  21. 21.

    Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011).

  22. 22.

    Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

  23. 23.

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

  24. 24.

    Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).

  25. 25.

    Pierson, E. & Yau, C. ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol. 16, 241 (2015).

  26. 26.

    Wang, Y. J. et al. Single-cell transcriptomics of the human endocrine pancreas. Diabetes 65, 3028–3038 (2016).

  27. 27.

    Xu, C. & Su, Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31, 1974–1980 (2015).

  28. 28.

    Bendall, S. C. et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014).

  29. 29.

    Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016).

  30. 30.

    Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

  31. 31.

    Park, J. et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360, 758–763 (2018).

  32. 32.

    Wu, H. et al. Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response. J. Am. Soc. Nephrol. 29, 2069–2080 (2018).

  33. 33.

    Gladka, M. M. et al. Single-cell sequencing of the healthy and diseased heart reveals cytoskeleton-associated protein 4 as a new modulator of fibroblasts activation. Circulation 138, 166–180 (2018).

  34. 34.

    Dick, S. A. et al. Self-renewing resident cardiac macrophages limit adverse remodeling following myocardial infarction. Nat. Immunol. 20, 29–39 (2019).

  35. 35.

    Deng, Q., Ramskold, D., Reinius, B. & Sandberg, R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343, 193–196 (2014).

  36. 36.

    Miyamoto, D. T. et al. RNA-Seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance. Science 349, 1351–1356 (2015).

  37. 37.

    Ross, I. L., Browne, C. M. & Hume, D. A. Transcription of individual genes in eukaryotic cells occurs randomly and infrequently. Immunol. Cell Biol. 72, 177–185 (1994).

  38. 38.

    Svensson, V. et al. Power analysis of single-cell RNA-sequencing experiments. Nat. Methods 14, 381–387 (2017).

  39. 39.

    Soares, S., Sousa, J., Pais, A. & Vitorino, C. Nanomedicine: Principles, properties, and regulatory issues. Front. Chem. 6, 360 (2018).

  40. 40.

    Pober, J. S. & Tellides, G. Participation of blood vessel cells in human adaptive immune responses. Trends Immunol. 33, 49–57 (2012).

  41. 41.

    Piotti, G., Palmisano, A., Maggiore, U. & Buzio, C. Vascular endothelium as a target of immune response in renal transplant rejection. Front. Immunol. 5, 505 (2014).

  42. 42.

    van Buul, J. D. et al. ICAM-1 clustering on endothelial cells recruits VCAM-1. J. Biomed. Biotechnol. 2010, 120328 (2010).

  43. 43.

    Tietjen, G. T., Bracaglia, L. G., Saltzman, W. M. & Pober, J. S. Focus on fundamentals: Achieving effective nanoparticle targeting. Trends Mol. Med. 24, 598–606 (2018).

  44. 44.

    Glotz, D., Lucchiari, N., Pegaz-Fiornet, B. & Suberbielle-Boissel, C. Endothelial cells as targets of allograft rejection. Transplantation 82, S19–S21 (2006).

  45. 45.

    Al-Lamki, R. S., Bradley, J. R. & Pober, J. S. Endothelial cells in allograft rejection. Transplantation 86, 1340–1348 (2008).

  46. 46.

    Muro, S. et al. Endothelial targeting of high-affinity multivalent polymer nanocarriers directed to intercellular adhesion molecule 1. J. Pharmacol. Exp. Ther. 317, 1161–1169 (2006).

  47. 47.

    Cui, J. et al. Ex vivo pretreatment of human vessels with siRNA nanoparticles provides protein silencing in endothelial cells. Nat. Commun. 8, 191 (2017).

  48. 48.

    Tietjen, G. T. et al. Nanoparticle targeting to the endothelium during normothermic machine perfusion of human kidneys. Sci. Transl. Med. 9 (2017). https://doi.org/10.1126/scitranslmed.aam6764.

  49. 49.

    Zhu, P. et al. Organ preservation with targeted rapamycin nanoparticles: a pre-treatment strategy preventing chronic rejection in vivo. RSC Adv. 8, 25909–25919 (2018).

  50. 50.

    Schroeder, R. A., Marroquin, C. E. & Kuo, P. C. Tolerance and the “Holy Grail” of transplantation. J. Surg. Res. 111, 109–119 (2003).

  51. 51.

    Kishimoto, T. K. & Maldonado, R. A. Nanoparticles for the induction of antigen-specific immunological tolerance. Front. Immunol. 9, 230 (2018).

  52. 52.

    Steinman, R. M. & Hemmi, H. Dendritic cells: translating innate to adaptive immunity. Curr. Top. Microbiol. Immunol. 311, 17–58 (2006).

  53. 53.

    Zhang, A. H., Rossi, R. J., Yoon, J., Wang, H. & Scott, D. W. Tolerogenic nanoparticles to induce immunologic tolerance: Prevention and reversal of FVIII inhibitor formation. Cell. Immunol. 301, 74–81 (2016).

  54. 54.

    Pang, L., Macauley, M. S., Arlian, B. M., Nycholat, C. M. & Paulson, J. C. Encapsulating an immunosuppressant enhances tolerance induction by siglec-engaging tolerogenic liposomes. Chembiochem 18, 1226–1233 (2017).

  55. 55.

    Stead, S. O. et al. Murine and non-human primate dendritic cell targeting nanoparticles for in vivo generation of regulatory T-cells. ACS Nano 12, 6637–6647 (2018).

  56. 56.

    Bryant, J. et al. Nanoparticle delivery of donor antigens for transplant tolerance in allogeneic islet transplantation. Biomaterials 35, 8887–8894 (2014).

  57. 57.

    Shahzad, K. A. et al. On-target and direct modulation of alloreactive T cells by a nanoparticle carrying MHC alloantigen, regulatory molecules and CD47 in a murine model of alloskin transplantation. Drug Deliv. 25, 703–715 (2018).

  58. 58.

    Leventhal, J. et al. Chimerism and tolerance without GVHD or engraftment syndrome in HLA-mismatched combined kidney and hematopoietic stem cell transplantation. Sci. Transl. Med. 4, 124ra28 (2012).

  59. 59.

    Scandling, J. D. et al. Tolerance and chimerism after renal and hematopoietic-cell transplantation. N. Engl. J. Med. 358, 362–368 (2008).

  60. 60.

    Kawai, T. et al. HLA-mismatched renal transplantation without maintenance immunosuppression. N. Engl. J. Med. 358, 353–361 (2008).

  61. 61.

    Hlavaty, K. A. et al. Tolerance induction using nanoparticles bearing HY peptides in bone marrow transplantation. Biomaterials 76, 1–10 (2016).

  62. 62.

    Braza, M. S. et al. Inhibiting inflammation with myeloid cell-specific nanobiologics promotes organ transplant acceptance. Immunity 49, 819–28.e6 (2018).

  63. 63.

    Dangi, A., Yu, S. & Luo, X. Apoptotic cell-based therapies for promoting transplantation tolerance. Curr. Opin. Organ Transplant. 23, 552–558 (2018).

  64. 64.

    Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012).

  65. 65.

    Lander, N., Chiurillo, M. A. & Docampo, R. Genome editing by CRISPR/Cas9: A game change in the genetic manipulation of protists. J. Eukaryot. Microbiol. 63, 679–690 (2016).

  66. 66.

    Doudna, J. A. & Charpentier, E. Genome editing. The new frontier of genome engineering with CRISPR-Cas9. Science 346, 1258096 (2014).

  67. 67.

    Cowan, P. J. & Tector, A. J. The resurgence of xenotransplantation. Am. J. Transplant. 17, 2531–2536 (2017).

  68. 68.

    Denner, J. Paving the path toward porcine organs for transplantation. N. Engl. J. Med. 377, 1891–1893 (2017).

  69. 69.

    Fung, R. K. & Kerridge, I. H. Gene editing advance re-ignites debate on the merits and risks of animal to human transplantation. Intern. Med. J. 46, 1017–1022 (2016).

  70. 70.

    Niu, D. et al. Inactivation of porcine endogenous retrovirus in pigs using CRISPR-Cas9. Science 357, 1303–1307 (2017).

  71. 71.

    Salomon, D. R. A CRISPR way to block PERVs--engineering organs for transplantation. N. Engl. J. Med. 374, 1089–1091 (2016).

  72. 72.

    Shrock, E. & Guell, M. CRISPR in animals and animal models. Prog. Mol. Biol. Transl. Sci. 152, 95–114 (2017).

  73. 73.

    Wiedenheft, B., Sternberg, S. H. & Doudna, J. A. RNA-guided genetic silencing systems in bacteria and archaea. Nature 482, 331–338 (2012).

  74. 74.

    Lau, R. W., Wang, B. & Ricardo, S. D. Gene editing of stem cells for kidney disease modelling and therapeutic intervention. Nephrology 23, 981–990 (2018).

  75. 75.

    Cruz, N. M. & Freedman, B. S. CRISPR gene editing in the kidney. Am. J. Kidney Dis. 71, 874–883 (2018).

  76. 76.

    Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

  77. 77.

    Guo, T. et al. Harnessing accurate non-homologous end joining for efficient precise deletion in CRISPR/Cas9-mediated genome editing. Genome Biol. 19, 170 (2018).

  78. 78.

    Estrada, J. L. et al. Evaluation of human and non-human primate antibody binding to pig cells lacking GGTA1/CMAH/beta4GalNT2 genes. Xenotransplantation 22, 194–202 (2015).

  79. 79.

    Petersen, B. et al. Efficient production of biallelic GGTA1 knockout pigs by cytoplasmic microinjection of CRISPR/Cas9 into zygotes. Xenotransplantation 23, 338–346 (2016).

  80. 80.

    Fischer, K., Kind, A. & Schnieke, A. Assembling multiple xenoprotective transgenes in pigs. Xenotransplantation 25, e12431 (2018).

  81. 81.

    Lambrigts, D., Sachs, D. H. & Cooper, D. K. Discordant organ xenotransplantation in primates: world experience and current status. Transplantation 66, 547–561 (1998).

  82. 82.

    Fischer, K. et al. Efficient production of multi-modified pigs for xenotransplantation by ‘combineering’, gene stacking and gene editing. Sci. Rep. 6, 29081 (2016).

  83. 83.

    Byrne, G. W., McGregor, C. G. A. & Breimer, M. E. Recent investigations into pig antigen and anti-pig antibody expression. Int. J. Surg. 23, 223–228 (2015).

  84. 84.

    Sato, M. et al. The combinational use of CRISPR/Cas9-based gene editing and targeted toxin technology enables efficient biallelic knockout of the alpha-1,3-galactosyltransferase gene in porcine embryonic fibroblasts. Xenotransplantation 21, 291–300 (2014).

  85. 85.

    Gao, H. et al. Production of alpha1,3-galactosyltransferase and cytidine monophosphate-N-acetylneuraminic acid hydroxylase gene double-deficient pigs by CRISPR/Cas9 and handmade cloning. J. Rreprod. Dev. 63, 17–26 (2017).

  86. 86.

    Zhang, R. et al. Reducing immunoreactivity of porcine bioprosthetic heart valves by genetically-deleting three major glycan antigens, GGTA1/beta4GalNT2/CMAH. Acta Biomater. 72, 196–205 (2018).

  87. 87.

    Adams, A. B. et al. Xenoantigen deletion and chemical immunosuppression can prolong renal xenograft survival. Ann. Surg. 268, 564–573 (2018).

  88. 88.

    Reyes, L. M. et al. Creating class I MHC-null pigs using guide RNA and the Cas9 endonuclease. J. Immunol. 193, 5751–5757 (2014).

  89. 89.

    Kemter, E., Denner, J. & Wolf, E. Will genetic engineering carry xenotransplantation of pig islets to the clinic? Curr. Diab. Rep. 18, 103 (2018).

  90. 90.

    Martens, G. R. et al. Humoral reactivity of renal transplant-waitlisted patients to cells from GGTA1/CMAH/B4GalNT2, and SLA class I knockout pigs. Transplantation 101, e86–e92 (2017).

  91. 91.

    Hara, H. et al. Human dominant-negative class II transactivator transgenic pigs—effect on the human anti-pig T-cell immune response and immune status. Immunology 140, 39–46 (2013).

  92. 92.

    Zhang, W. et al. Generation of complement protein C3 deficient pigs by CRISPR/Cas9-mediated gene targeting. Sci. Rep. 7, 5009 (2017).

  93. 93.

    Iwase, H. et al. Immunological and physiological observations in baboons with life-supporting genetically engineered pig kidney grafts. Xenotransplantation. 24 (2017). https://doi.org/10.1111/xen.12293.

  94. 94.

    Mohiuddin, M. M. et al. Chimeric 2C10R4 anti-CD40 antibody therapy is critical for long-term survival of GTKO.hCD46.hTBM pig-to-primate cardiac xenograft. Nat. Commun. 7, 11138 (2016).

  95. 95.

    Denner, J. How active are porcine endogenous retroviruses (PERVs)? Viruses 8 (2016). https://doi.org/10.3390/v8080215.

  96. 96.

    Ross, M. J. & Coates, P. T. Using CRISPR to inactivate endogenous retroviruses in pigs: an important step toward safe xenotransplantation? Kidney Int. 93, 4–6 (2018).

  97. 97.

    Patience, C., Takeuchi, Y. & Weiss, R. A. Infection of human cells by an endogenous retrovirus of pigs. Nat. Med. 3, 282–286 (1997).

  98. 98.

    Yang, L. et al. Genome-wide inactivation of porcine endogenous retroviruses (PERVs). Science 350, 1101–1104 (2015).

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Acknowledgements

This work was supported by grants from the National Institutes of Health (R01 EB009910) (A.D. and X.L.) and the Chinese Scholarship Council (S.Y.).

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Correspondence to Xunrong Luo.

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Keywords

  • Transplantation
  • Single cell sequencing
  • Crisper Cas9
  • Nanotechnology