Summary

Glaucoma is the second leading cause of blindness worldwide. It affects over 90 million people, and its incidence is predicted to rise 2-fold over the next two decades. Glaucoma-induced vision loss and blindness result from the slow degeneration and death of retinal ganglion cells (RGCs). In humans, the large population of RGCs can be subdivided into at least 30 subtypes. The susceptibility of RGC subtypes to glaucoma-induced cell degeneration differs significantly. But it has been surprisingly difficult to identifying both RGC subtypes and their susceptibility to glaucoma. For the past three years, we have been developing single-cell technologies to study both RGC type and the early signature of glaucoma-associated cellular stress. We will develop artificial intelligence (AI) approaches to identify RGC subtypes that are more susceptible to glaucoma-induced insult. Our results could advance our understanding of the genetic bases for glaucoma-induced RGC cell death and possible therapeutic interventions.

Project Details

Our long-term goal is to identify the impact of glaucoma on retinal ganglion cell (RGC) subtypes using transcriptome analysis of single retinal cells generated using powerful mouse models of glaucoma. The objective of our study is to develop and refine novel artificial intelligence (AI) approaches to study single-cell RNA-sequencing (scRNA-seq) data and to identify RGC subtypes that are more impacted by glaucoma. We hypothesize that state-of-the-art AI algorithms can delineate RGC subtypes from scRNA-seq data and subsequently improve the identification of the subtypes affected in the early stages of glaucoma. Such findings may advance our understanding of the genetic bases for glaucoma-induced RGC cell death leading to possible therapeutic interventions, such as nicotinamide and pregabalin.