Separation of empty microcapsules after microencapsulation of porcine neonatal islets

Two-step sedimentation process for selection of microcapsules containing cell aggregates

Microencapsulation technologies are being developed to protect transplanted islets from immune rejection, to reduce or even eliminate the need for immunosuppression. However, unencapsulated cells increase the chances of rejection and empty beads increase transplant volumes. Thus, separation processes were investigated to remove these byproducts based on density differences. The densities of islet-sized mouse insulinoma 6 (MIN6) cell aggregates and acellular 5% alginate beads generated via emulsification and internal gelation were ~ 1.065 and 1.042 g/ml, respectively. The separation of empty beads from those containing aggregates was performed by sedimentation under unit gravity in continuous gradients of polysucrose and sodium diatrizoate with density ranges of 1.032-1.045, 1.035-1.044, or 1.039-1.042 g/ml. The 1.039-1.042 g/ml gradient enabled recoveries of ~ 80% of the aggregate-containing beads while the other gradients recovered only ~ 60%. The bottom fraction of the 1.039-1.042 g/ml gradient contained beads with ~ 6% of their volume occupied by cell aggregates. Separation of unencapsulated aggregates from the aggregate-containing beads was then achieved by centrifugation of this purified fraction in a 1.055 g/ml density solution. Thus, these sedimentation-based approaches can effectively remove the byproducts of cell encapsulation.

Nanotechnology Approaches to Modulate Immune Responses to Cell-based Therapies for Type 1 Diabetes

Islet transplantation is a promising curative treatment option for type 1 diabetes (T1D) as it can provide physiological blood glucose control. The widespread utilization of islet transplantation is limited due to systemic immunosuppression requirements, persisting graft immunodestruction, and poor islet engraftment. Traditional macro- and micropolymeric encapsulation strategies can alleviate the need for antirejection immunosuppression, yet the increased graft volume and diffusional distances imparted by these coatings can be detrimental to graft viability and glucose control. Additionally, systemic administration of pro-engraftment and antirejection therapeutics leaves patients vulnerable to adverse off-target side effects. Nanoscale engineering techniques can be used to immunocamouflage islets, modulate the transplant microenvironment, and provide localized pro-engraftment cues. In this review, we discuss the applications of nanotechnology to advance the clinical potential of islet transplantation, with a focus on cell surface engineering, bioactive functionalization, and use of nanoparticles in T1D cell-based treatments.

Developing a Rapid Algorithm to Enable Rapid Characterization of Alginate Microcapsules

The islets of Langerhans are endocrine tissue clusters that secrete hormones that regulate the body's glucose, carbohydrate, and fat metabolism, the most important of which is insulin, a hormone secreted by β-cells within the islets. In certain instances, a person's own immune system attacks and destroys them, leading to the development of type 1 diabetes (T1D), a life-long condition that needs daily insulin administration to maintain health and prolong survival. Islet transplantation is a surgical procedure that has demonstrated the ability to normalize blood sugar levels for up to a few years, but the need for chronic immunosuppression relegates it to a last resort that is often only used sparingly and in seriously ill patients. Islet microencapsulation is a biomedical innovation designed to protect islets from the immune system by coating them with a biocompatible polymer, and this new technology has demonstrated various degrees of success in small- and large-animal studies. This success is significantly impacted by microcapsule morphology and encapsulation efficiency. Since hundreds of thousands of microcapsules are generated during the process, characterization of encapsulated islets without the help of some degree of automation would be difficult, time-consuming, and error prone due to inherent observer bias. We have developed an image analysis algorithm that can analyze hundreds of microencapsulated islets and characterize their size, shape, circularity, and distortion with minimal observer bias. This algorithm can be easily adapted to similar nano- or microencapsulation technologies to implement stricter quality control and improve biomaterial device design and success.