3D in vivo Magnetic Particle Imaging of Human Stem Cell-Derived Islet Organoid Transplantation Using a Machine Learning Algorithm.
3D in vivo Magnetic Particle Imaging of Human Stem Cell-Derived Islet Organoid Transplantation Using a Machine Learning Algorithm.
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DOI：
10.3389/fcell.2021.704483
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发表时间：
2021
影响因子：
5.5
通讯作者：
Wang P
中科院分区：
文献类型：
--
作者：
Sun A;Hayat H;Liu S;Tull E;Bishop JO;Dwan BF;Gudi M;Talebloo N;Dizon JR;Li W;Gaudet J;Alessio A;Aguirre A;Wang P
干细胞衍生的胰岛类器官是治疗 1 型糖尿病的一种有前途的方法。该领域的一个主要障碍是缺乏适当的体内方法来确定移植结果。在这里，我们研究了在小鼠模型中使用磁粒子成像（MPI）对移植的干细胞衍生的胰岛类器官进行体内追踪的可行性。人诱导多能干细胞-L1 分化为胰岛类器官，并用超顺磁性氧化铁纳米颗粒进行标记。使用 MPI 系统对由不同数量的标记胰岛类器官组成的模型进行成像。将标记的胰岛类器官移植到 NOD/scid 小鼠的左肾被膜下，然后在移植后 1、7 和 28 天使用 3D MPI 进行扫描。使用 3D MPI 的 K-means++ 算法分析对胰岛类器官进行定量评估。收集左肾并进行C肽和葡聚糖免疫荧光染色。胰岛类器官表达胰岛细胞标记物，包括胰岛素和胰高血糖素。标记的胰岛类器官模型的图像分析揭示了铁含量和胰岛类器官数量之间的直接线性相关性。 K-means++ 算法显示，在研究过程中，左肾被膜下标记的胰岛类器官的信号减少。肾切片的免疫荧光染色显示存在胰岛类器官移植物，这通过右旋糖酐和 C 肽双染色证实。这项研究表明，采用机器学习算法分析的 MPI 可以监测用超顺磁性氧化铁纳米颗粒标记的胰岛类器官移植物，并提供它们在体内存在的定量信息。
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.
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影响因子：
7.7
作者：
Pomposelli T;Wang P;Takeuchi K;Miyake K;Ariyoshi Y;Watanabe H;Chen X;Shimizu A;Robertson N;Yamada K;Moore A
通讯作者：
Moore A
影响因子：
7.7
作者：
Davalli, AM;Scaglia, L;Weir, GC
通讯作者：
Weir, GC
影响因子：
6.7
作者：
Khandhar AP;Keselman P;Kemp SJ;Ferguson RM;Goodwill PW;Conolly SM;Krishnan KM
通讯作者：
Krishnan KM
影响因子：
28.5
作者：
Li J;Song W;Pan G;Zhou J
通讯作者：
Zhou J
影响因子：
10.8
作者：
Yu EY;Bishop M;Zheng B;Ferguson RM;Khandhar AP;Kemp SJ;Krishnan KM;Goodwill PW;Conolly SM
通讯作者：
Conolly SM