The demonstration by the Edmonton group that human islet transplantation can be successfully used to manage adult type 1 diabetes patients with refractory hypoglycemia has led to increased funding of clinical trials and further research to extend the scope of this therapy by using porcine islets in place of human islets. Significant advances have been made in improving immunosuppression treatment regimens so that results obtained from treating adult diabetic patients with human islet transplants are similar to those obtained after pancreas transplantation. The major hurdle to move this therapy from clinical research to routine clinical practice is to improve the yield and quality of islets recovered from human or porcine pancreas. Presently, there are no standardized methods that can accurately assess the number or quality of islets that are used in the islet transplantation procedures so that results between laboratories can be objectively evaluated. This grant is focused on developing a robust, islet image analysis software to objectively analyze the number and quality of porcine islets recovered from the pancreas. The two major aims of the project are first to develop an improved image analysis software program that will provide a standardized measurement of the number and mass of porcine islets in a cell preparation. And second, enhance the capabilities of the software program by correlating the image signatures of each porcine islet to an artificial category. Porcine islets of similar size will be handpicked and sorted into three categories based on the shape, border, integrity, or uniformity of dithizone staining. The first software enhancement will find those features in the images that can be used to distinguish the different categories of islets. The second enhancement will assess the feasibility of using machine learning methods to correlate these features with data recovered from the images but also other discrete or continuous variables that are used to characterize the porcine islet preparations. If successful, the ability to use a rapid and objective image analysis methodology will improve the assessment of the number and quality of islets within and between laboratories;correlate image features with success of transplantation as measured by graft survival and insulin independence;and improve the islet isolation methods to achieve favorable islet image scores that are determined by retrospective analysis. The ability of a commercial firm focused on improving islet yields by focusing on tissue dissociation with a leading academic laboratory that has sophisticated expertise in developing software algorithms from microscopic images provides a fresh approach to a difficult medical that needs to be resolved to realize the full potential of islet transplantation to treat adult type 1 diabetic patients.
An objective, reliable and accurate method for the assessment of islet quantity and quality is paramount to the standardization and subsequent success of islet transplantation as a treatment for type 1 diabetes. Conventional manual methods for determining islet yields using an optical microscope with a calibrated eyepiece reticule are subjective, time consuming and often overestimate islet mass due to sampling errors and erroneous assumptions in the conversion of islet numbers to islet equivalents. The research proposed will utilize recent advances in digital image analysis, including machine learning and pattern recognition, to develop a software algorithm for the rapid characterization of islets destined for transplantation procedures.
