A novel AI-driven approach to image and interrogate 3D histology of gastric tissues diabetic gastroparesis patients
Gastroparesis, a condition that causes significant slowing of gastric motility, nausea, and abdominal pain, is a significant complication in diabetic patients. While gastrointestinal motility is regulated by the Enteric Nervous System (ENS) in conjunction with associated cells, a clear understanding of how the ENS is impacted in diabetic gastroparesis patients is lacking. While prior data suggests significant reduction in nerve fiber staining in the gastric tissue from patients, there is no clarity on whether neuronal numbers, their networks, or their proximity to associated cells such as ICC and macrophages are pathologically impacted in disease. This gap, which exists since neuronal networks exist in three-dimensional (3D) space which 2 dimensional cross-sections of tissue cannot capture accurately, has stymied our efforts in understanding whether and how structural changes to the ENS should be quantified for assessing pathology, and whether future therapeutic interventions should target the ENS for normalizing gastric function. It is with this rationale that in this proposal, we aim to create a 3D high-resolution and high-information map of the full thickness gastric tissues from control and diabetic gastroparesis patients to tease out how the ENS and their associated cells significant for gastric motility are altered with disease. Using our novel software algorithms CODA and VAMPIRE, we will construct 3D images of neurons, macrophages, ICC, and smooth muscle from 2D immunostained tissue sections, and assess disease associated changes to neuronal structure, cell-cell proximity, and cell health. Further, these images can be analyzed by other artificial intelligence-driven approaches in the future to learn how diabetes impacts gastric tissue structure.

Curation Flag Information