In Vivo Bioluminescence for the Detection of the Fate of Pancreatic Islet Organoids Post-transplantation.

Pancreatic islet transplantation is a promising cell replacement treatment for patients afflicted with type 1 diabetes (T1D), which is an autoimmune disease resulting in the destruction of insulin-producing islet ß-cells. However, the shortage of donor pancreatic islets significantly hampers the widespread application of this strategy as routine therapy. Pluripotent stem cell-derived insulin-producing islet organoids constitute a promising alternative ß-cell source for T1D patients. Early after transplantation, it is critical to know the fate of transplanted islet organoids, but determining their survival remains a significant technical challenge. Bioluminescence imaging (BLI) is an optical molecular imaging technique that detects the survival of living cells using light emitted from luciferase-expressing bioreporter cells. Through BLI, the post-transplantation fate of islet organoids can be evaluated over time in a noninvasive fashion with minimal intervention, thus making BLI an ideal tool to determine the success of the transplant and improving cell replacement therapy approaches for T1D.

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 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.

A Concise Review: The Role of Stem Cells in Cancer Progression and Therapy.

The properties of cancer stem cells (CSCs) have recently gained attention as an avenue of intervention for cancer therapy. In this review, we highlight some of the key roles of CSCs in altering the cellular microenvironment in favor of cancer progression. We also report on various studies in this field which focus on transformative properties of CSCs and their influence on surrounding cells or targets through the release of cellular cargo in the form of extracellular vesicles. The findings from these studies encourage the development of novel interventional therapies that can target and prevent cancer through efficient, more effective methods. These methods include targeting immunosuppressive proteins and biomarkers, promoting immunization against tumors, exosome-mediated CSC conversion, and a focus on the quiescent properties of CSCs and their role in cancer progression. The resulting therapeutic benefit and transformative potential of these novel approaches to stem cell-based cancer therapy provide a new direction in cancer treatment, which can focus on nanoscale, molecular properties of the cellular microenvironment and establish a more precision medicine-oriented paradigm of treatment.

Artificial Intelligence Analysis of Magnetic Particle Imaging for Islet Transplantation in a Mouse Model.

PURPOSE: Current approaches to quantification of magnetic particle imaging (MPI) for cell-based therapy are thwarted by the lack of reliable, standardized methods of segmenting the signal from background in images. This calls for the development of artificial intelligence (AI) systems for MPI analysis. PROCEDURES: We utilize a canonical algorithm in the domain of unsupervised machine learning, known as K-means++, to segment the regions of interest (ROI) of images and perform iron quantification analysis using a standard curve model. We generated in vitro, in vivo, and ex vivo data using islets and mouse models and applied the AI algorithm to gain insight into segmentation and iron prediction on these MPI data. In vitro models included imaging the VivoTrax-labeled islets in varying numbers. In vivo mouse models were generated through transplantation of increasing numbers of the labeled islets under the kidney capsule of mice. Ex vivo data were obtained from the MPI images of excised kidney grafts. RESULTS: The K-means++ algorithms segmented the ROI of in vitro phantoms with minimal noise. A linear correlation between the islet numbers and the increasing prediction of total iron value (TIV) in the islets was observed. Segmentation results of the ROI of the in vivo MPI scans showed that with increasing number of transplanted islets, the signal intensity increased with linear trend. Upon segmenting the ROI of ex vivo data, a linear trend was observed in which increasing intensity of the ROI yielded increasing TIV of the islets. Through statistical evaluation of the algorithm performance via intraclass correlation coefficient validation, we observed excellent performance of K-means++-based model on segmentation and quantification analysis of MPI data. CONCLUSIONS: We have demonstrated the ability of the K-means++-based model to provide a standardized method of segmentation and quantification of MPI scans in an islet transplantation mouse model.

Magnetic Particle Imaging: Current Applications in Biomedical Research.

Magnetic particle imaging (MPI) is a new imaging modality with the potential for high-resolution imaging while retaining the noninvasive nature of other current modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET). It is able to track location and quantities of special superparamagnetic iron oxide nanoparticles without tracing any background signal. MPI utilizes the unique, intrinsic aspects of the nanoparticles: how they react in the presence of the magnetic field, and the subsequent turning off of the field. The current group of nanoparticles that are used in MPI are usually commercially available for MRI. Special MPI tracers are in development by many groups that utilize an iron-oxide core encompassed by various coatings. These tracers would solve the current obstacles by altering the size and material of the nanoparticles to what is required by MPI. In this review, the theory behind and the development of these tracers are discussed. In addition, applications such as cell tracking, oncology imaging, neuroimaging, and vascular imaging, among others, stemming from the implementation of MPI into the standard are discussed. Level of Evidence: 5 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2020;51:1659-1668.