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3D in vivo Magnetic Particle Imaging of Human Stem Cell-Derived Islet Organoid Transplantation Using a Machine Learning Algorithm
Stem cell-derived islet organoids constitute a promising treatment of type 1 diabetes. A major hurdle in the field is the lack of appropriate in vivo method to determine graft outcome. Here, we investigate the feasibility of in vivo tracking of transplanted stem cell-derived islet organoids using ma...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8397508/ https://www.ncbi.nlm.nih.gov/pubmed/34458264 http://dx.doi.org/10.3389/fcell.2021.704483 |
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author | Sun, Aixia Hayat, Hasaan Liu, Sihai Tull, Eliah Bishop, Jack Owen Dwan, Bennett Francis Gudi, Mithil Talebloo, Nazanin Dizon, James Raynard Li, Wen Gaudet, Jeffery Alessio, Adam Aguirre, Aitor Wang, Ping |
author_facet | Sun, Aixia Hayat, Hasaan Liu, Sihai Tull, Eliah Bishop, Jack Owen Dwan, Bennett Francis Gudi, Mithil Talebloo, Nazanin Dizon, James Raynard Li, Wen Gaudet, Jeffery Alessio, Adam Aguirre, Aitor Wang, Ping |
author_sort | Sun, Aixia |
collection | PubMed |
description | Stem cell-derived islet organoids constitute a promising treatment of type 1 diabetes. A major hurdle in the field is the lack of appropriate in vivo method to determine graft outcome. Here, we investigate the feasibility of in vivo tracking of transplanted stem cell-derived islet organoids using magnetic particle imaging (MPI) in a mouse model. Human induced pluripotent stem cells-L1 were differentiated to islet organoids and labeled with superparamagnetic iron oxide nanoparticles. The phantoms comprising of different numbers of labeled islet organoids were imaged using an MPI system. Labeled islet organoids were transplanted into NOD/scid mice under the left kidney capsule and were then scanned using 3D MPI at 1, 7, and 28 days post transplantation. Quantitative assessment of the islet organoids was performed using the K-means++ algorithm analysis of 3D MPI. The left kidney was collected and processed for immunofluorescence staining of C-peptide and dextran. Islet organoids expressed islet cell markers including insulin and glucagon. Image analysis of labeled islet organoids phantoms revealed a direct linear correlation between the iron content and the number of islet organoids. The K-means++ algorithm showed that during the course of the study the signal from labeled islet organoids under the left kidney capsule decreased. Immunofluorescence staining of the kidney sections showed the presence of islet organoid grafts as confirmed by double staining for dextran and C-peptide. This study demonstrates that MPI with machine learning algorithm analysis can monitor islet organoids grafts labeled with super-paramagnetic iron oxide nanoparticles and provide quantitative information of their presence in vivo. |
format | Online Article Text |
id | pubmed-8397508 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83975082021-08-28 3D in vivo Magnetic Particle Imaging of Human Stem Cell-Derived Islet Organoid Transplantation Using a Machine Learning Algorithm Sun, Aixia Hayat, Hasaan Liu, Sihai Tull, Eliah Bishop, Jack Owen Dwan, Bennett Francis Gudi, Mithil Talebloo, Nazanin Dizon, James Raynard Li, Wen Gaudet, Jeffery Alessio, Adam Aguirre, Aitor Wang, Ping Front Cell Dev Biol Cell and Developmental Biology Stem cell-derived islet organoids constitute a promising treatment of type 1 diabetes. A major hurdle in the field is the lack of appropriate in vivo method to determine graft outcome. Here, we investigate the feasibility of in vivo tracking of transplanted stem cell-derived islet organoids using magnetic particle imaging (MPI) in a mouse model. Human induced pluripotent stem cells-L1 were differentiated to islet organoids and labeled with superparamagnetic iron oxide nanoparticles. The phantoms comprising of different numbers of labeled islet organoids were imaged using an MPI system. Labeled islet organoids were transplanted into NOD/scid mice under the left kidney capsule and were then scanned using 3D MPI at 1, 7, and 28 days post transplantation. Quantitative assessment of the islet organoids was performed using the K-means++ algorithm analysis of 3D MPI. The left kidney was collected and processed for immunofluorescence staining of C-peptide and dextran. Islet organoids expressed islet cell markers including insulin and glucagon. Image analysis of labeled islet organoids phantoms revealed a direct linear correlation between the iron content and the number of islet organoids. The K-means++ algorithm showed that during the course of the study the signal from labeled islet organoids under the left kidney capsule decreased. Immunofluorescence staining of the kidney sections showed the presence of islet organoid grafts as confirmed by double staining for dextran and C-peptide. This study demonstrates that MPI with machine learning algorithm analysis can monitor islet organoids grafts labeled with super-paramagnetic iron oxide nanoparticles and provide quantitative information of their presence in vivo. Frontiers Media S.A. 2021-08-12 /pmc/articles/PMC8397508/ /pubmed/34458264 http://dx.doi.org/10.3389/fcell.2021.704483 Text en Copyright © 2021 Sun, Hayat, Liu, Tull, Bishop, Dwan, Gudi, Talebloo, Dizon, Li, Gaudet, Alessio, Aguirre and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cell and Developmental Biology Sun, Aixia Hayat, Hasaan Liu, Sihai Tull, Eliah Bishop, Jack Owen Dwan, Bennett Francis Gudi, Mithil Talebloo, Nazanin Dizon, James Raynard Li, Wen Gaudet, Jeffery Alessio, Adam Aguirre, Aitor Wang, Ping 3D in vivo Magnetic Particle Imaging of Human Stem Cell-Derived Islet Organoid Transplantation Using a Machine Learning Algorithm |
title | 3D in vivo Magnetic Particle Imaging of Human Stem Cell-Derived Islet Organoid Transplantation Using a Machine Learning Algorithm |
title_full | 3D in vivo Magnetic Particle Imaging of Human Stem Cell-Derived Islet Organoid Transplantation Using a Machine Learning Algorithm |
title_fullStr | 3D in vivo Magnetic Particle Imaging of Human Stem Cell-Derived Islet Organoid Transplantation Using a Machine Learning Algorithm |
title_full_unstemmed | 3D in vivo Magnetic Particle Imaging of Human Stem Cell-Derived Islet Organoid Transplantation Using a Machine Learning Algorithm |
title_short | 3D in vivo Magnetic Particle Imaging of Human Stem Cell-Derived Islet Organoid Transplantation Using a Machine Learning Algorithm |
title_sort | 3d in vivo magnetic particle imaging of human stem cell-derived islet organoid transplantation using a machine learning algorithm |
topic | Cell and Developmental Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8397508/ https://www.ncbi.nlm.nih.gov/pubmed/34458264 http://dx.doi.org/10.3389/fcell.2021.704483 |
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