Back

Photostimulation Improves Maturation of Human Photoreceptors

Celiker, C.; Hruba, E.; Kyriakou, K.; Dorgau, B.; Gambin, F. M.; Weissova, K.; Englmaier, L.; Vaskovicova, N.; Savage, M. A.; Hilgen, G.; Sernagor, E.; Garanto, A.; Lako, M.; Barta, T.

2025-07-17 cell biology
10.1101/2025.07.17.665282 bioRxiv
Show abstract

The human retina contains photoreceptor cells that detect light and enable vision. The development of these cells involves a tightly regulated cascade of structural and molecular events, and their dysfunction leads to irreversible blindness in many retinal diseases. Human retinal organoids derived from stem cells have become powerful tools to model retinal development and disease, but they often remain immature and lack key features required for full function. Light is not only the sensory target of photoreceptors but also an important developmental signal in vivo. However, light has rarely been used as a deliberate stimulus during in vitro differentiation. Here we show that exposing retinal organoids to rhythmic light flicker at a specific frequency enhances photoreceptor maturation across multiple levels. This stimulation improves the development of outer segments, accelerates the transcriptional transition from precursor to mature photoreceptors, and strengthens functional connectivity with downstream neurons. These findings identify patterned light as a potent and physiologically relevant signal for driving retinal development in vitro. This approach represents a non-invasive and easily scalable method for improving the quality of retinal organoids, with implications for disease modelling, drug discovery and the preparation of photoreceptors for cell-based therapies.

Published in Nature Communications (predicted rank #1) · training set

Matching journals

The top 6 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.