immunostaining and automated counting of mouse retinal ganglion cells using an ai-based program

<meta charset="utf8"></head><body>小鼠全支架视网膜中 RGC 的免疫染色和基于 AI 的计数

Glaucoma is characterized by&#160;the progressive death of ganglion cells, which&#160;poses a significant challenge for sight&#160;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&#160;their homogeneous genetic background, high&#160;reproductive capacity, and the similarity of&#160;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&#160;for automatic counting minimizes bias and&#160;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&#39;s straightforward design and the availability of user-friendly software make it accessible to researchers with varying levels of expertise in retinal analysis.

<meta charset="utf8"></head><body>作者聚焦：青光眼小鼠模型中的高效视网膜神经节细胞计数用于治疗评估

小鼠视网膜神经节细胞的全支架免疫染色和自动计数

使用基于 AI 的程序对小鼠视网膜神经节细胞进行免疫染色和自动计数

immunostaining-automated-counting-mouse-retinal-ganglion-cells-using

Research

JoVE Journal

Neuroscience

Immunostaining and Automated Counting of Mouse Retinal Ganglion Cells Using an AI-Based Program

74 次观看. 将整个支架视网膜与鼠标分离后，在 PBS 中轻轻搅拌清洗视网膜 5 分钟。将洗涤后的视网膜浸入封闭缓冲液中 30 分钟。孵育后，用一抗 BRN3A 替换封闭缓冲液。在 4 摄氏度下孵育视网膜过夜，以允许特异性抗原抗体结合。第二天，用 PBS 洗涤视网膜 3 次，每次 5 分钟，然后与 Alexa 594 或 Alexa 488 偶联的抗兔二抗孵育 2 小时，以观察靶抗原。用 PBS 洗...

视频: 使用基于 AI 的程序对小鼠视网膜神经节细胞进行免疫染色和自动计数

Whole-Mount Immunostaining and Automatic Counting of Mouse Retinal Ganglion Cells

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.

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 ...