LAUSR

Islet transplantation has made major progress to treat patients with type 1 diabetes. Islet mass and quality are critically important to ensure successful transplantation. Currently, islet status is evaluated using insulin secretion, oxygen consumption rate, or adenosine triphosphate (ATP) measurement. These parameters are evaluated independently and do not effectively predict islet status post-transplant. Therefore, assessing human pancreatic islets by encompassing ATP, DNA, insulin, and protein content from a single tissue sample would serve as a better predictor for islet status. In this study, a single step procedure for extracting ATP, DNA, insulin, and protein content from human pancreatic islets was described and the biomolecule contents were quantified. Additionally, different mathematical calculations integrating total ATP, DNA, insulin, and protein content were randomly tested under various conditions to predict islet status. The results demonstrated that the ATP assay was efficient and the biomolecules were effectively quantified. Furthermore, the mathematical formula we developed could be optimized to predict islet status. In conclusion, our results indicate a proof-of-concept that a simple logarithmic formula can predict overall islet status for various conditions when total islet ATP, DNA, insulin, and protein content are simultaneously assessed from a single tissue sample.