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

Abstract

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.

Keywords: artificial intelligence; diabetes; magnetic particle imaging; stem cell tracking; unsupervised machine learning.