Abstract
Presentation Description :
The ability to derive retinal ganglion cells (RGCs) from human pluripotent stem cells (hPSCs) provides an extraordinary opportunity to study the development of RGCs as well as cellular mechanisms underlying their degeneration in optic neuropathies. In the past several years, multiple approaches have been established that allow for the generation of RGCs from hPSCs, with these methods greatly improved in more recent studies to yield mature RGCs that more faithfully recapitulate phenotypes within the eye. Many approaches to date have focused upon the differentiation and analysis of RGCs with glaucoma-associated gene variants in a cell autonomous manner, although more recent studies have also begun to build complexity back into these simplified cellular models through the introduction of supporting cells such as glia in a non-cell autonomous manner. With the ultimate goal of generating hPSC-RGCs that most closely resemble those within the retina for proper studies of retinal development, disease modeling, as well as cellular replacement, this presentation will focus upon the current effective approaches for the differentiation of hPSC-RGCs, as well as how they have been applied for the investigation of RGC neurodegenerative diseases such as glaucoma. Furthermore, characteristics of RGCs necessary for their use as effective in vitro disease models will be discussed, how other cell types can be used to build complexity back into these models, and importantly, how these current systems should be improved to more accurately reflect disease states. The establishment of characteristics in differentiated hPSC-RGCs that more effectively mimic RGCs within the retina will not only enable their use as effective models of RGC development, but will also create a better disease model for the identification of mechanisms underlying the neurodegeneration of RGCs in disease states such as glaucoma, further facilitating the development of therapeutic approaches to rescue RGCs from degeneration in disease states.
This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.