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* These authors contributed equally
This protocol describes the procedure for isolating the whole-mount mouse retina and performing immunostaining to label all retinal ganglion cells (RGCs). The process is followed by imaging and automatically counting RGCs using AI-based software, providing a simple, fast, and accurate method for quantifying RGCs in the entire mouse retina.
Glaucoma is a leading cause of blindness globally, characterized by a complex pathogenic mechanism that makes vision restoration challenging. Mice serve as valuable animal models for studying the pathogenesis and treatment of glaucoma due to their relatively homogenous genetic background and retinal ganglion cells (RGCs), which structurally resemble those in humans. Accurate assessment of RGC damage and treatment outcomes in mouse glaucoma models requires determining the RGC number across the entire retina. This protocol outlines a comprehensive method involving the isolation of the whole retina, labeling RGCs with specific antibodies, and rapid, accurate automatic counting of RGCs using an AI-based program. The streamlined approach allows for efficient and precise quantification of RGC numbers in mouse retinas, facilitating the evaluation of RGC degeneration and potential therapeutic interventions. By enabling researchers to assess the extent of RGC damage, this protocol contributes to a deeper understanding of glaucoma pathogenesis and aids in developing effective treatment strategies to manage and prevent vision loss.
Glaucoma is characterized byΒ the progressive death of ganglion cells, whichΒ poses a significant challenge for sightΒ 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 toΒ their homogeneous genetic background, highΒ reproductive capacity, and the similarity ofΒ 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 usedΒ for automatic counting minimizes bias andΒ 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.
The procedure complied with the guidelines of the Association for Research in Vision and Ophthalmology for using animals in research and was approved by the Institutional Animal Care and Use Committee (IACUC) of Sichuan Provincial People's Hospital. C57Bl/6J male mice (2 months old) were used in this study. Figure 1 illustrates the overall procedure described here. The details of the reagents and equipment used are listed in the Table of Materials.
1. Isolation of whole-mount retinas
2. Immunostaining
3. Image processing
NOTE: Import the image into the RGC automatic counting software and start counting. The number of BRN3A-positive cells for the entire retina can be obtained in minutes. Download the AutoCount software from GitHub (https://github.com/MOEMIL/Intelligent-quantifying-RGCs). Follow the detailed installation steps for the software below:
4. Automated cell counting
This protocol details the methodology for whole-mount immunostaining of mouse retinas, ensuring meticulous tissue preparation, precise antibody incubation, and reliable automated cell counting. The procedure facilitates robust labeling and quantification of retinal ganglion cells (RGCs), enabling accurate assessment of cellular populations in various experimental contexts. The method was used to count RGCs in wild-type and glaucoma-modeled mice, yielding consistent results. A representative result shows that the number o...
This protocol provides a method for determining all retinal ganglion cells (RGCs) in a mouse retina, which can be used to monitor the progression of RGC degeneration in mouse models for glaucoma studies. The mouse retina is a delicate nerve tissue5, and isolating whole-mount retinas from mouse eyes requires repeated practice. During experimentation, it was found that the fixation time significantly affects retina morphology. For mice around 2 weeks of age, a fixation time of 20 min is sufficient t...
The authors have no conflicts to disclose.
This research project was supported by the National Natural Science Foundation of China (82371059 (H.Z.)), the Department of Science and Technology of Sichuan Province, China (2023JDZH0002 (H.Z.)), the Chengdu Science and Technology Bureau (2022-YF05-01984-SN (H.Z.)), and Sichuan Provincial People's Hospital (30320230095 (J.Y.), 30420220062 (J.Y.)).Β
Name | Company | Catalog Number | Comments |
1Γ PBS | Servicebio | G4202 | |
Alexa594-conjugated Goat Anti-Rabbit IgGΒ (H+L) | ThermoFisher | A-11012 | |
Anti-BRN3A antibody [EPR23257-285] | Abcam | ab245230 | |
AutoCount software | https://github.com/MOEMIL/Intelligent-quantifying-RGCs | ||
Cloud disk | Google drive link | https://drive.google.com/file/d/1yOEsBvil6KEdZFa5ENQxB6Β | |
Cloud disk | Baidu link | Extraction code: g44k | https://pan.baidu.com/s/1lccg1OVbeudsp2VtnqxWZgΒ |
Marker | Sharpie | ||
Normal Donkey Serum | Biosharp, Labgic | 25030081 | |
Paraformaldehyde | Macklin | P804536 | |
ProClean 300 | Beyotime | ST853 | |
Sucrose | BBI, Sangon | A610498 | |
Triton X-100 | BioFroxx, neoFroxx | 1139ML100 |
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