3D in vivo Magnetic Particle Imaging of Human Stem Cell-Derived Islet Organoid Transplantation Using a Machine Learning Algorithm

Frontiers in Cell and Developmental Biology

Aitor Aguirre²  Wen Li²  Jeffery Gaudet³  Adam Alessio⁴  Bennett Francis Dwan⁵  Nazanin Talebloo⁶  Jack Owen Bishop⁷  Sihai Liu⁸  Aixia Sun⁹  Ping Wang⁹  James Raynard Dizon^1,10  Hasaan Hayat^1,11  Mithil Gudi^1,11  Eliah Tull^1,12 

[1] 0Department of Electrical and Computer Engineering, College of Engineering, Michigan State University, East Lansing, MI, United States;1Institute for Quantitative Health Science and Engineering (IQ), Department of Biomedical Engineering, Michigan State University, East Lansing, MI, United States;2Magnetic Insight Inc., Alameda, CA, United States;3Department of Computational Mathematics, Science and Engineering, College of Engineering, Michigan State University, East Lansing, MI, United States;College of Natural Science, Michigan State University, East Lansing, MI, United States;Department of Chemistry, College of Natural Science, Michigan State University, East Lansing, MI, United States;Department of Neuroscience, College of Natural Science, Michigan State University, East Lansing, MI, United States;Department of Orthopedics, Beijing Charity Hospital, Capital Medical University, Beijing, China;Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, United States;Department of Radiology, UT Southwestern Medical Center, Dallas, TX, United States;Lyman Briggs College, Michigan State University, East Lansing, MI, United States;Medgar Evers College, City University of New York, Brooklyn, NY, United States;Precision Health Program, Michigan State University, East Lansing, MI, United States;

关键词: artificial intelligence;    unsupervised machine learning;    magnetic particle imaging;    stem cell tracking;    diabetes;   

DOI  :  10.3389/fcell.2021.704483

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown