Automated high-throughput, high-content 3D imaging of intact pancreatic islets

Abstract

Diabetes poses a global health crisis affecting individuals across age groups and backgrounds, with a prevalence estimate of 700 million people worldwide by 2045. Current therapeutic strategies primarily rely on insulin therapy or hypoglycemic agents, which fail to address the root cause of the disease - the loss of pancreatic insulin-producing beta-cells. Therefore, bioassays that recapitulate intact islets are needed to enable drug discovery for beta-cell replenishment, protection from beta-cell loss, and islet-cell interactions. Standard cancer insulinoma beta-cell lines MIN6 and INS-1 have been used to interrogate beta-cell metabolic pathways and function but are not suitable for studying proliferative effects. Screening using primary human/rodent intact islets offers a higher level of physiological relevance to enhance diabetes drug discovery and development. However, the 3-dimensionality of intact islets have presented challenges in developing robust, high-throughput assays to detect beta-cell proliferative effects. Established methods rely on either dissociated islet cells plated in 2D monolayer cultures for imaging or reconstituted pseudo-islets formed in round bottom plates to achieve homogeneity. These approaches have significant limitations due to the islet cell dispersion process. To address these limitations, we have developed a robust, intact ex vivo pancreatic islet bioassay in 384-well format that is capable of detecting diabetes-relevant endpoints including beta-cell proliferation, chemoprotection, and islet spatial morphometrics.

Keywords

• Diabetes
• Pancreatic islets
• High-content imaging
• Beta-cell proliferation
• Machine Learning
• 3D Imaging