Glaucoma is characterized by the progressive death of ganglion cells, which poses a significant challenge for sight restoration1,2. This disease is a major focus of ophthalmic research due to its prevalence and impact on vision3. Mouse models are indispensable in glaucoma
research due to their homogeneous genetic background, high reproductive capacity, and the similarity of their ganglion cell properties to humans4. The primary goal of this method is to accurately quantify retinal ganglion cells (RGCs) in mouse models, which is essential for understanding the pathogenesis of glaucoma and developing relevant treatments.
The rationale behind developing this technique stems from the need for a reliable and efficient method to assess RGC degeneration in mouse models. Traditional methods, such as labeling RGCs in retinal sections, often provide unreliable results due to the non-uniform distribution of RGCs in the retina5. Quantifying RGCs across the entire retina better reflects changes in their numbers and is crucial for evaluating disease progression and therapeutic interventions.
This method offers several advantages over alternative techniques. For instance, manual counting of retinal ganglion cells (RGCs) in a normal adult mouse retina, which contains 40,000 to 60,000 RGCs, can be time-consuming and error-prone6,7,8. The software for automatic RGC counting that we developed allows for accurate counting in less than 3 min, potentially saving researchers a significant amount of time. Additionally, the AI-based software used for automatic counting minimizes bias and enhances reproducibility.
Furthermore, this technique provides a standardized approach to assess RGC degeneration across different mouse models and experimental conditions, contributing valuable data to the field of glaucoma research. The method aligns with other studies that emphasize the importance of whole-mount retina analysis for understanding retinal changes in diseases9.
To help readers determine whether this method is suitable for their application, it is important to note that this technique is particularly advantageous for researchers studying retinal ganglion cell (RGC) degeneration in mouse models of glaucoma or other retinal diseases. The method is adaptable to various experimental setups and offers a high degree of accuracy and efficiency in RGC counting, making it ideal for both small-scale and large-scale studies. Additionally, the protocol's straightforward design and the availability of user-friendly software make it accessible to researchers with varying levels of expertise in retinal analysis.