Developing a Rapid Algorithm to Enable Rapid Characterization of Alginate Microcapsules

Abstract

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.