Back

Developmental wave of programmed ganglion cell death in human retinal organoids

Brooks, T.; Park, Y. K.; Vielle, A.; Ha, M.; Del Rio-Tsonis, K.; Robinson, M. L.; Vergara, M. N.

2025-07-30 developmental biology
10.1101/2025.07.25.666895 bioRxiv
Show abstract

The delicate and complex structure of the neural retina that enables proper visual function is achieved during embryonic development through a precise balance of proliferation, differentiation, and cell death. Retinal ganglion cells (RGC), the only output neurons of the retina, show a steady increase in numbers during development except for two waves of cell death that are highly conserved in vertebrates. However, the mechanisms responsible for these phenomena and their conservation in the human retina are incompletely understood. In this work we took advantage of human induced pluripotent stem cell (hiPSC)-derived retinal organoids to explore these questions. Using different markers and quantitative techniques in three different hiPSC lines, we found a consistent decrease in RGC numbers at week 8 of differentiation, a developmental stage that is equivalent to that of the first wave of RGC death in other species. This decrease coincided with a peak in caspase 3 activation and TUNEL(+) staining, suggesting an apoptotic mechanism. Notably, this was accompanied by a decrease in the BAX/BCL2 ratio and a lack of caspase 9 activation. However, we observed a marked increase in caspase 8 activation at this stage, suggesting the involvement of the extrinsic apoptotic pathway. Together, these results show for the first time the intrinsic ability of the human retina to regulate RGC numbers through programmed cell death mechanisms, which could lead to new insights regarding congenital retinal abnormalities. Moreover, this work has implications for experimental design in basic and translational research using human stem cell-derived retinal organoid models.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.