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Non-invasive early detection of acute transplant rejection via nanosensors of granzyme B activity


The early detection of the onset of transplant rejection is critical for the long-term survival of patients. The diagnostic gold standard for detecting transplant rejection involves a core biopsy, which is invasive, has limited predictive power and carries a morbidity risk. Here, we show that nanoparticles conjugated with a peptide substrate specific for the serine protease granzyme B, which is produced by recipient T cells during the onset of acute cellular rejection, can serve as a non-invasive biomarker of early rejection. When administered systemically in mouse models of skin graft rejection, these nanosensors preferentially accumulate in allograft tissue, where they are cleaved by granzyme B, releasing a fluorescent reporter that filters into the recipient’s urine. Urinalysis then discriminates the onset of rejection with high sensitivity and specificity before features of rejection are apparent in grafted tissues. Moreover, in mice treated with subtherapeutic levels of immunosuppressive drugs, the reporter signals in urine can be detected before graft failure. This method may enable routine monitoring of allograft status without the need for biopsies.

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Fig. 1: Granzyme B activity nanosensors detect onset of acute allograft rejection by amplifying detection signals into urine.
Fig. 2: Activity nanosensors detect proteolytic cleavage by GzmB.
Fig. 3: Sensing GzmB activity during alloreactive T cell killing.
Fig. 4: Granzyme B activity during ACR triggers a urine pharmacokinetic switch.
Fig. 5: Urinary prediction of ACR upon administration of GzmB activity nanosensors.
Fig. 6: Urinary prediction of allograft rejection under subtherapeutic immunosuppression.

Data availability

All data supporting the findings of this study are available within the manuscript and its Supplementary Information. Raw data are available from the corresponding authors.


  1. 1.

    Mas, V. R., Mueller, T. F., Archer, K. J. & Maluf, D. G. Identifying biomarkers as diagnostic tools in kidney transplantation. Expert. Rev. Mol. Diagn. 11, 183–196 (2011).

    CAS  Article  Google Scholar 

  2. 2.

    Gwinner, W. Renal transplant rejection markers. World J. Urol. 25, 445 (2007).

    Article  Google Scholar 

  3. 3.

    Cornell, L. D., Smith, R. N. & Colvin, R. B. Kidney transplantation: mechanisms of rejection and acceptance. Annu. Rev. Pathol. Mech. Dis. 3, 189–220 (2008).

    CAS  Article  Google Scholar 

  4. 4.

    Nankivell, B. J. & Alexander, S. I. Rejection of the kidney allograft. N. Engl. J. Med. 363, 1451–1462 (2010).

    CAS  Article  Google Scholar 

  5. 5.

    Sijpkens, Y. W. J. et al. Early versus late acute rejection episodes in renal transplantation. Transplantation 75, 204 (2003).

    Article  Google Scholar 

  6. 6.

    Moreau, A., Varey, E., Anegon, I. & Cuturi, M.-C. Effector mechanisms of rejection. Cold Spring Harb. Perspect. Med. 3, a015461 (2013).

    Article  Google Scholar 

  7. 7.

    Furness, P. N., Taub, N. & Convergenge of European Renal Transplant Pathology Assessment Procedures (CERTPAP) Project). International variation in the interpretation of renal transplant biopsies: report of the CERTPAP Project. Kidney Int. 60, 1998–2012 (2001).

  8. 8.

    Piovesan, A. C. et al. Multifocal renal allograft biopsy: impact on therapeutic decisions. Transplant. Proc. 40, 3397–3400 (2008).

    CAS  Article  Google Scholar 

  9. 9.

    Jaffa, M. A. et al. Analyses of renal outcome following transplantation adjusting for informative right censoring and demographic factors: a longitudinal study. Ren. Fail. 32, 691–698 (2010).

    CAS  Article  Google Scholar 

  10. 10.

    Josephson, M. A. Monitoring and managing graft health in the kidney transplant recipient. Clin. J. Am. Soc. Nephrol. 6, 1774–1780 (2011).

    Article  Google Scholar 

  11. 11.

    Vlaminck, I. D. et al. Circulating cell-free DNA enables noninvasive diagnosis of heart transplant rejection. Sci. Transl. Med. 6, 241ra77–241ra77 (2014).

    Article  Google Scholar 

  12. 12.

    Choy, J. C. Granzymes and perforin in solid organ transplant rejection. Cell Death Differ. 17, 567–576 (2010).

    CAS  Article  Google Scholar 

  13. 13.

    Wagrowska-Danilewicz, M. & Danilewicz, M. Immunoexpression of perforin and granzyme B on infiltrating lymphocytes in human renal acute allograft rejection. Nefrologia 23, 538–544 (2003).

    CAS  PubMed  Google Scholar 

  14. 14.

    Rowshani, A. T. et al. Hyperexpression of the granzyme B inhibitor PI-9 in human renal allografts: a potential mechanism for stable renal function in patients with subclinical rejection. Kidney Int. 66, 1417–1422 (2004).

    CAS  Article  Google Scholar 

  15. 15.

    Kummer, J. A. et al. Expression of granzyme A and B proteins by cytotoxic lymphocytes involved in acute renal allograft rejection. Kidney Int. 47, 70–77 (1995).

    CAS  Article  Google Scholar 

  16. 16.

    Suthanthiran, M. et al. Urinary-cell mRNA profile and acute cellular rejection in kidney allografts. N. Engl. J. Med. 369, 20–31 (2013).

    CAS  Article  Google Scholar 

  17. 17.

    Simon, T., Opelz, G., Wiesel, M., Ott, R. C. & Süsal, C. Serial peripheral blood perforin and granzyme B gene expression measurements for prediction of acute rejection in kidney graft recipients. Am. J. Transplant. 3, 1121–1127 (2003).

    CAS  Article  Google Scholar 

  18. 18.

    Calafiore, R. & Basta, G. Clinical application of microencapsulated islets: actual prospectives on progress and challenges. Adv. Drug Deliv. Rev. 67–68, 84–92 (2014).

    Article  Google Scholar 

  19. 19.

    Li, B. et al. Noninvasive diagnosis of renal-allograft rejection by measurement of messenger RNA for perforin and granzyme B in urine. N. Engl. J. Med. 344, 947–954 (2001).

    CAS  Article  Google Scholar 

  20. 20.

    Sun, J. et al. A cytosolic granzyme B inhibitor related to the viral apoptotic regulator cytokine response modifier A is present in cytotoxic lymphocytes. J. Biol. Chem. 271, 27802–27809 (1996).

    CAS  Article  Google Scholar 

  21. 21.

    Edgington, L. E., Verdoes, M. & Bogyo, M. Functional imaging of proteases: recent advances in the design and application of substrate-based and activity-based probes. Curr. Opin. Chem. Biol. 15, 798–805 (2011).

    CAS  Article  Google Scholar 

  22. 22.

    Sanman, L. E. & Bogyo, M. Activity-based profiling of proteases. Annu. Rev. Biochem. 83, 249–273 (2014).

    CAS  Article  Google Scholar 

  23. 23.

    Konishi, M. et al. Imaging granzyme B activity assesses immune-mediated myocarditis. Circ. Res. 117, 502–512 (2015).

    CAS  Article  Google Scholar 

  24. 24.

    Larimer, B. M. et al. Granzyme B PET imaging as a predictive biomarker of immunotherapy response. Cancer Res. 77, 2318–2327 (2017).

    CAS  Article  Google Scholar 

  25. 25.

    Whitley, M. J. et al. A mouse-human phase 1 co-clinical trial of a protease-activated fluorescent probe for imaging cancer. Sci. Transl. Med. 8, 320ra4–320ra4 (2016).

    Article  Google Scholar 

  26. 26.

    Olson, E. S. et al. In vivo fluorescence imaging of atherosclerotic plaques with activatable cell-penetrating peptides targeting thrombin activity. Integr. Biol. 4, 595–605 (2012).

    CAS  Article  Google Scholar 

  27. 27.

    Kwong, G. A. et al. Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease. Nat. Biotechnol. 31, 63–70 (2013).

    CAS  Article  Google Scholar 

  28. 28.

    Lin, K. Y., Kwong, G. A., Warren, A. D., Wood, D. K. & Bhatia, S. N. Nanoparticles that sense thrombin activity as synthetic urinary biomarkers of thrombosis. ACS Nano 7, 9001–9009 (2013).

    CAS  Article  Google Scholar 

  29. 29.

    Warren, A. D., Kwong, G. A., Wood, D. K., Lin, K. Y. & Bhatia, S. N. Point-of-care diagnostics for noncommunicable diseases using synthetic urinary biomarkers and paper microfluidics. Proc. Natl Acad. Sci. USA 111, 3671–3676 (2014).

    CAS  Article  Google Scholar 

  30. 30.

    Kwong, G. A. et al. Mathematical framework for activity-based cancer biomarkers. Proc. Natl Acad. Sci. USA 112, 12627–12632 (2015).

    CAS  Article  Google Scholar 

  31. 31.

    Holt, B. A., Mac, Q. D. & Kwong, G. A. Nanosensors to detect protease activity in vivo for noninvasive diagnostics. J. Vis. Exp. 137, e57937 (2018).

    Google Scholar 

  32. 32.

    Fosgerau, K. & Hoffmann, T. Peptide therapeutics: current status and future directions. Drug Discov. Today 20, 122–128 (2015).

    CAS  Article  Google Scholar 

  33. 33.

    Anselmo, A. C. & Mitragotri, S. Nanoparticles in the clinic. Bioeng. Transl. Med. 1, 10–29 (2016).

    Article  Google Scholar 

  34. 34.

    Arami, H., Khandhar, A., Liggitt, D. & Krishnan, K. M. In vivo delivery, pharmacokinetics, biodistribution and toxicity of iron oxide nanoparticles. Chem. Soc. Rev. 44, 8576–8607 (2015).

    CAS  Article  Google Scholar 

  35. 35.

    Park, J.-H. et al. Magnetic iron oxide nanoworms for tumor targeting and imaging. Adv. Mater. 20, 1630–1635 (2008).

    CAS  Article  Google Scholar 

  36. 36.

    Jokerst, J. V., Lobovkina, T., Zare, R. N. & Gambhir, S. S. Nanoparticle PEGylation for imaging and therapy. Nanomed. 6, 715–728 (2011).

    CAS  Article  Google Scholar 

  37. 37.

    Harris, J. L., Peterson, E. P., Hudig, D., Thornberry, N. A. & Craik, C. S. Definition and redesign of the extended substrate specificity of granzyme B. J. Biol. Chem. 273, 27364–27373 (1998).

    CAS  Article  Google Scholar 

  38. 38.

    Waugh, S. M., Harris, J. L., Fletterick, R. & Craik, C. S. The structure of the pro-apoptotic protease granzyme B reveals the molecular determinants of its specificity. Nat. Struct. Mol. Biol. 7, 762–765 (2000).

    CAS  Article  Google Scholar 

  39. 39.

    Ruggles, S. W., Fletterick, R. J. & Craik, C. S. Characterization of structural determinants of granzyme B reveals potent mediators of extended substrate specificity. J. Biol. Chem. 279, 30751–30759 (2004).

    CAS  Article  Google Scholar 

  40. 40.

    Casciola-Rosen, L. et al. Mouse and human granzyme B have distinct tetrapeptide specificities and abilities to recruit the bid pathway. J. Biol. Chem. 282, 4545–4552 (2007).

    CAS  Article  Google Scholar 

  41. 41.

    Huppa, J. B. & Davis, M. M. T-cell-antigen recognition and the immunological synapse. Nat. Rev. Immunol. 3, 973 (2003).

    CAS  Article  Google Scholar 

  42. 42.

    Dustin, M. L. & Long, E. O. Cytotoxic immunological synapses. Immunol. Rev. 235, 24–34 (2010).

    CAS  Article  Google Scholar 

  43. 43.

    Balaji, K. N., Schaschke, N., Machleidt, W., Catalfamo, M. & Henkart, P. A. Surface cathepsin B protects cytotoxic lymphocytes from self-destruction after degranulation. J. Exp. Med. 196, 493–503 (2002).

    CAS  Article  Google Scholar 

  44. 44.

    Locke, F. L. et al. Phase 1 results of ZUMA-1: a multicenter study of KTE-C19 anti-CD19 CAR T cell therapy in refractory aggressive lymphoma. Mol. Ther. 25, 285–295 (2017).

    CAS  Article  Google Scholar 

  45. 45.

    Goldbach-Mansky, R. et al. Raised granzyme B levels are associated with erosions in patients with early rheumatoid factor positive rheumatoid arthritis. Ann. Rheum. Dis. 64, 715–721 (2005).

    CAS  Article  Google Scholar 

  46. 46.

    Clarke, S. Rm et al. Characterization of the ovalbumin-specific TCR transgenic line OT-I: MHC elements for positive and negative selection. Immunol. Cell Biol. 78, 110–117 (2000).

    CAS  Article  Google Scholar 

  47. 47.

    Kurschus, F. C., Fellows, E., Stegmann, E. & Jenne, D. E. Granzyme B delivery via perforin is restricted by size, but not by heparan sulfate-dependent endocytosis. Proc. Natl Acad. Sci. USA 105, 13799–13804 (2008).

    CAS  Article  Google Scholar 

  48. 48.

    Adrain, C., Duriez, P. J., Brumatti, G., Delivani, P. & Martin, S. J. The cytotoxic lymphocyte protease, granzyme B, targets the cytoskeleton and perturbs microtubule polymerization dynamics. J. Biol. Chem. 281, 8118–8125 (2006).

    CAS  Article  Google Scholar 

  49. 49.

    Giesübel, U., Dälken, B., Mahmud, H. & Wels, W. S. Cell binding, internalization and cytotoxic activity of human granzyme B expressed in the yeast Pichia pastoris. Biochem. J. 394, 563–573 (2006).

    Article  Google Scholar 

  50. 50.

    Mori, D. N., Kreisel, D., Fullerton, J. N., Gilroy, D. W. & Goldstein, D. R. Inflammatory triggers of acute rejection of organ allografts. Immunol. Rev. 258, 132–144 (2014).

    Article  Google Scholar 

  51. 51.

    LaRosa, D. F., Rahman, A. H. & Turka, L. A. The innate immune system in allograft rejection and tolerance. J. Immunol. 178, 7503–7509 (2007).

    CAS  Article  Google Scholar 

  52. 52.

    Haas, M. et al. The Banff 2017 Kidney Meeting Report: revised diagnostic criteria for chronic active T cell–mediated rejection, antibody-mediated rejection, and prospects for integrative endpoints for next-generation clinical trials. Am. J. Transplant. 18, 293–307 (2018).

    CAS  Article  Google Scholar 

  53. 53.

    Maeda, H., Wu, J., Sawa, T., Matsumura, Y. & Hori, K. Tumor vascular permeability and the EPR effect in macromolecular therapeutics: a review. J. Control. Release 65, 271–284 (2000).

    CAS  Article  Google Scholar 

  54. 54.

    Fredman, G. et al. Targeted nanoparticles containing the proresolving peptide Ac2-26 protect against advanced atherosclerosis in hypercholesterolemic mice. Sci. Transl. Med. 7, 275ra20 (2015).

    CAS  Article  Google Scholar 

  55. 55.

    Wilhelm, S. et al. Analysis of nanoparticle delivery to tumours. Nat. Rev. Mater. 1, 16014 (2016).

    CAS  Article  Google Scholar 

  56. 56.

    Choi, H. S. et al. Renal clearance of nanoparticles. Nat. Biotechnol. 25, 1165–1170 (2007).

    CAS  Article  Google Scholar 

  57. 57.

    Loupy, A. et al. The Banff 2015 Kidney Meeting Report: current challenges in rejection classification and prospects for adopting molecular pathology. Am. J. Transplant. 17, 28–41 (2017).

    CAS  Article  Google Scholar 

  58. 58.

    Vincenti, F., Jensik, S. C., Filo, R. S., Miller, J. & Pirsch, J. A long-term comparison of tacrolimus (FK506) and cyclosporine in kidney transplantation: evidence for improved allograft survival at five years. Transplantation 73, 775–782 (2002).

    CAS  Article  Google Scholar 

  59. 59.

    Vincenti, F. et al. Belatacept and long-term outcomes in kidney transplantation. N. Engl. J. Med. 374, 333–343 (2016).

    CAS  Article  Google Scholar 

  60. 60.

    Jeansson, M. & Haraldsson, B. Glomerular size and charge selectivity in the mouse after exposure to glucosaminoglycan-degrading enzymes. J. Am. Soc. Nephrol. 14, 1756–1765 (2003).

    CAS  Article  Google Scholar 

  61. 61.

    Hsauyry, P. et al. The inflammatory mechanisms of allograft rejection.Immunol. Rev. 77, 85–142 (1984).

    Article  Google Scholar 

  62. 62.

    Wood, K. J. & Goto, R. Mechanisms of rejection. Curr. Persp. Transplant. 93, 1–10 (2012).

    Google Scholar 

  63. 63.

    Staquicini, F. I. et al. Vascular ligand-receptor mapping by direct combinatorial selection in cancer patients. Proc. Natl Acad. Sci. USA 108, 18637–18642 (2011).

    CAS  Article  Google Scholar 

  64. 64.

    Hua, S. Targeting sites of inflammation: intercellular adhesion molecule-1 as a target for novel inflammatory therapies. Front. Pharmacol. 4, 127 (2013).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Kwon, E. J., Dudani, J. S. & Bhatia, S. N. Ultrasensitive tumour-penetrating nanosensors of protease activity. Nat. Biomed. Eng. 1, 0054 (2017).

    Article  Google Scholar 

  66. 66.

    Han, D. et al. Assessment of cytotoxic lymphocyte gene expression in the peripheral blood of human islet allograft recipients: elevation precedes clinical evidence of rejection. Diabetes 53, 2281–2290 (2004).

    CAS  Article  Google Scholar 

  67. 67.

    Jaffer, F. A. & Weissleder, R. Molecular imaging in the clinical arena. JAMA 293, 855–862 (2005).

    CAS  Article  Google Scholar 

  68. 68.

    Weissleder, R., Tung, C.-H., Mahmood, U. & Bogdanov, J. In vivo imaging of tumors with protease-activated near-infrared fluorescent probes. Nat. Biotechnol. 17, 375–378 (1999).

    CAS  Article  Google Scholar 

  69. 69.

    Olson, E. S. et al. Activatable cell penetrating peptides linked to nanoparticles as dual probes for in vivo fluorescence and MR imaging of proteases. Proc. Natl Acad. Sci. USA 107, 4311–4316 (2010).

    CAS  Article  Google Scholar 

  70. 70.

    Sugahara, K. N. et al. Tissue-penetrating delivery of compounds and nanoparticles into tumors. Cancer Cell. 16, 510–520 (2009).

    CAS  Article  Google Scholar 

  71. 71.

    Hori, S. S. & Gambhir, S. S. Mathematical model identifies blood biomarker–based early cancer detection strategies and limitations. Sci. Transl. Med. 3, 109ra116–109ra116 (2011).

    Article  Google Scholar 

  72. 72.

    Lutz, A. M., Willmann, J. K., Cochran, F. V., Ray, P. & Gambhir, S. S. Cancer screening: a mathematical model relating secreted blood biomarker levels to tumor sizes. PLoS. Med. 5, e170 (2008).

    Article  Google Scholar 

  73. 73.

    Dharnidharka, V. R., Kwon, C. & Stevens, G. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am. J. Kidney. Dis. 40, 221–226 (2002).

    CAS  Article  Google Scholar 

  74. 74.

    Kaplan, B., Schold, J. & Meier-Kriesche, H.-U. Poor predictive value of serum creatinine for renal allograft loss. Am. J. Transplant. 3, 1560–1565 (2003).

    CAS  Article  Google Scholar 

  75. 75.

    Slocum, J. L., Heung, M. & Pennathur, S. Marking renal injury: can we move beyond serum creatinine? Transl. Res. 159, 277–289 (2012).

    CAS  Article  Google Scholar 

  76. 76.

    Haase, M. et al. Accuracy of neutrophil gelatinase-associated lipocalin (NGAL) in diagnosis and prognosis in acute kidney injury: a systematic review and meta-analysis. Am. J. Kidney. Dis. 54, 1012–1024 (2009).

    CAS  Article  Google Scholar 

  77. 77.

    Mischak, H. et al. Recommendations for biomarker identification and qualification in clinical proteomics. Sci. Transl. Med. 2, 46ps42–46ps42 (2010).

    Article  Google Scholar 

  78. 78.

    Prendergast, M. B. & Gaston, R. S. Optimizing medication adherence: an ongoing opportunity to improve outcomes after kidney tansplantation. Clin. J. Am. Soc. Nephrol. 5, 1305–1311 (2010).

    Article  Google Scholar 

  79. 79.

    Alangaden, G. J. et al. Infectious complications after kidney transplantation: current epidemiology and associated risk factors. Clin. Transplant. 20, 401–409 (2006).

    Article  Google Scholar 

  80. 80.

    Sellarés, J. et al. Understanding the causes of kidney transplant failure: the dominant role of antibody-mediated rejection and nonadherence. Am. J. Transplant. 12, 388–399 (2012).

    Article  Google Scholar 

  81. 81.

    Palmacci, S. Synthesis of polysaccharide covered superparamagnetic oxide colloids. US patent 5,262,176 (1993).

  82. 82.

    Presolski Stanislav, I., Hong Vu, Phong. & Finn, M. G. Copper‐catalyzed azide–alkyne click chemistry for bioconjugation. Curr. Protoc. Chem. Biol. 3, 153–162 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

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This work was funded by an NIH Director’s New Innovator Award DP2HD091793 awarded to G.A.K. and National Institutes of Health U01 AI132904 awarded to A.B.A. Q.D.M. is supported by the NSF Graduate Research Fellowships Program (Grant No. DGE-1650044). D.V.M. is supported by National Institutes of Health F30 award number DK109665. B.A.H is supported by the National Institutes of Health GT BioMAT Training Grant under Award Number 5T32EB006343. G.A.K. holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Q.D.M., D.V.M., A.B.A. and G.A.K. conceived of the idea, designed experiments, and interpreted results. Q.D.M., D.V.M., J.A.K., C.M.S., O.M.D. and B.A.H. carried out the experiments. Q.D.M., D.V.M., B.A.H., A.B.A. and G.A.K. wrote the manuscript.

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Correspondence to Andrew B. Adams or Gabriel A. Kwong.

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Q.D.M., D.V.M., A.B.A. and G.A.K. are listed as inventors on a patent application pertaining to the results of the paper. G.A.K. is co-founder of and serves as consultant to Glympse Bio, which is developing products related to the research described in this paper. This study could affect his personal financial status. The terms of this arrangement have been reviewed and approved by Georgia Tech in accordance with its conflict of interest policies.

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Mac, Q.D., Mathews, D.V., Kahla, J.A. et al. Non-invasive early detection of acute transplant rejection via nanosensors of granzyme B activity. Nat Biomed Eng 3, 281–291 (2019).

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