Abstract
Human islet transplantation is a promising therapy to restore normoglycemia for type 1 diabetes (T1D). Despite recent advances, human islet transplantation remains suboptimal due to significant islet graft loss after transplantation. Various immunological and nonimmunological factors contribute to this loss therefore signifying a need for strategies and approaches for visualizing and monitoring transplanted human islet grafts. One such imaging approach is magnetic particle imaging (MPI), an emerging imaging modality that detects the magnetization of iron oxide nanoparticles. MPI is known for its specificity due to its high image contrast and sensitivity. MPI through its noninvasive nature provides the means for monitoring transplanted human islets in real time. Here we summarize an approach to track transplanted human islets using MPI. We label human islet from donors with dextran-coated ferucarbotran iron oxide nanoparticles, transplant the labeled human islet into under the left kidney capsule, and image graft cells using an MPI scanner. We engineer a K-means++, clustering-based unsupervised machine learning algorithm for standardized image segmentation and iron quantification of the MPI, which solves problems with selection bias and indiscriminate signal boundary that accompanies this newer imaging modality. In this chapter, we summarize the methods of this emerging imaging modality of MPI in conjunction with unsupervised machine learning to monitor and visualize islets after transplantation.
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References
Lebastchi J, Herold KC (2012) Immunologic and metabolic biomarkers of beta-cell destruction in the diagnosis of type 1 diabetes. Cold Spring Harb Perspect Med 2(6):a007708. https://doi.org/10.1101/cshperspect.a007708
Azzi J, Geara AS, El-Sayegh S, Abdi R (2010) Immunological aspects of pancreatic islet cell transplantation. Expert Rev Clin Immunol 6(1):111–124. https://doi.org/10.1586/eci.09.67
Kleijwegt FS, Roep BO (2013) Infectious tolerance as candidate therapy for type 1 diabetes: transfer of immunoregulatory properties from human regulatory T cells to other T cells and proinflammatory dendritic cells. Crit Rev Immunol 33(5):415–434. https://doi.org/10.1615/critrevimmunol.2013006782
Narang AS, Mahato RI (2006) Biological and biomaterial approaches for improved islet transplantation. Pharmacol Rev 58(2):194–243. https://doi.org/10.1124/pr.58.2.6
Luo X, Herold KC, Miller SD (2010) Immunotherapy of type 1 diabetes: where are we and where should we be going? Immunity 32(4):488–499. https://doi.org/10.1016/j.immuni.2010.04.002
Niclauss N, Morel P, Berney T (2014) Has the gap between pancreas and islet transplantation closed? Transplantation 98(6):593–599. https://doi.org/10.1097/TP.0000000000000288
Talebloo N, Gudi M, Robertson N, Wang P (2020) Magnetic particle imaging: current applications in biomedical research. J Magn Reson Imaging 51(6):1659–1668. https://doi.org/10.1002/jmri.26875
Wang P, Goodwill PW, Pandit P, Gaudet J, Ross A, Wang J, Yu E, Hensley DW, Doyle TC, Contag CH, Conolly S, Moore A (2018) Magnetic particle imaging of islet transplantation in the liver and under the kidney capsule in mouse models. Quant Imaging Med Surg 8(2):114–122. https://doi.org/10.21037/qims.2018.02.06
Goodwill PW, Saritas EU, Croft LR, Kim TN, Krishnan KM, Schaffer DV, Conolly SM (2012) X-space MPI: magnetic nanoparticles for safe medical imaging. Adv Mater 24(28):3870–3877. https://doi.org/10.1002/adma.201200221
Saritas EU, Goodwill PW, Zhang GZ, Conolly SM (2013) Magnetostimulation limits in magnetic particle imaging. IEEE Trans Med Imaging 32(9):1600–1610. https://doi.org/10.1109/TMI.2013.2260764
Hayat H, Sun A, Hayat H, Liu S, Talebloo N, Pinger C, Bishop JO, Gudi M, Dwan BF, Ma X, Zhao Y, Moore A, Wang P (2021) Artificial intelligence analysis of magnetic particle imaging for islet transplantation in a mouse model. Mol Imaging Biol 23(1):18–29. https://doi.org/10.1007/s11307-020-01533-5
Szot GL, Koudria P, Bluestone JA (2007) Transplantation of pancreatic islets into the kidney capsule of diabetic mice. J Vis Exp 9:404. https://doi.org/10.3791/404
Wang P, Schuetz C, Ross A, Dai G, Markmann JF, Moore A (2013) Immune rejection after pancreatic islet cell transplantation: in vivo dual contrast-enhanced MR imaging in a mouse model. Radiology 266(3):822–830. https://doi.org/10.1148/radiol.12121129
Wang P, Schuetz C, Vallabhajosyula P, Medarova Z, Tena A, Wei L, Yamada K, Deng S, Markmann JF, Sachs DH, Moore A (2015) Monitoring of allogeneic islet grafts in nonhuman primates using MRI. Transplantation 99(8):1574–1581. https://doi.org/10.1097/TP.0000000000000682
Acknowledgments
Human pancreatic islets were provided by the NIDDK-funded Integrated Islet Distribution Program (IIDP) at City of Hope, NIH grant no. 2UC4DK098085 and the JDRF-funded IIDP Islet Award Initiative to P.W. The project was also partly funded by the 1R03EB028349 from NIH/NIBIB to P.W.
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Sun, A., Hayat, H., Sanchez, S.W., Moore, A., Wang, P. (2023). Magnetic Particle Imaging of Transplanted Human Islets Using a Machine Learning Algorithm. In: Moore, A., Wang, P. (eds) Type-1 Diabetes. Methods in Molecular Biology, vol 2592. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2807-2_13
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DOI: https://doi.org/10.1007/978-1-0716-2807-2_13
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