In vivo mapping of foveal photoreceptors in retinal health and disease

视网膜健康和疾病中黄斑中心凹光感受器的体内绘图

• 批准号: 530371629
• 负责人: Dr. Wolf Harmening
• 金额: --
• 依托单位: Universitäts-Augenklinik Bonn
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/530371629?language=en
• 关键词: vivo; mapping; foveal; photoreceptors; retinal

Despite a total of about 100 million light sensitive photoreceptor cells in the human retina, only a small bouquet of cones at the foveal center is used for most visual tasks. Losing function in these cells has dramatic consequences for vision and in many retinal diseases, foveal photoreceptor structure and function is disturbed permanently. Although there are reports from the leading centers worldwide indicating therapy success in genetic therapies of photoreceptors the mechanism of the functional rescue of rods and cones and its correlation with morphological readouts in retinal imaging is not well understood. Adaptive optics imaging now makes it possible to observe microscopic, cellular structural and functional changes in the retina, opening the door for direct microscopic evaluation of the affected tissue and its impact for vision in the living eye. Partially because of insufficient visual resolution and missing analytical tools, the role of the functionally most relevant area, the foveal center, has not been studied yet. To close the gap, this project will first develop AI-assisted analytical tools for an automated pipeline for foveal photoreceptor detection and quantification in AOSLO imagery. We will then assess foveolar cellular topography in a large cohort of different age groups to build a topographical model of the normal fovea, which will also be used to build an atlas of normative foveolar cone topography data. Finally, to foster widespread clinical application, we will correlate our high-resolution topography data with clinical grade lower-resolution image data, studying whether readily available image data can be used as proxy for cell-resolved analysis.

尽管在人类视网膜中总共有大约1亿个光敏感光细胞，但是在中央凹中心只有一小束视锥用于大多数视觉任务。这些细胞的功能丧失对视力具有显著的后果，并且在许多视网膜疾病中，中央凹光感受器结构和功能永久性地受到干扰。尽管有来自世界领先中心的报告表明光感受器的基因疗法的治疗成功，但是对视杆细胞和视锥细胞的功能拯救的机制及其与视网膜成像中的形态学读数的相关性还没有很好地理解。自适应光学成像现在可以观察视网膜的微观，细胞结构和功能变化，为直接显微镜评估受影响的组织及其对活体眼睛视力的影响打开了大门。部分原因是视觉分辨率不足和缺少分析工具，功能上最相关的区域，中央凹中心的作用尚未研究。为了缩小差距，该项目将首先开发人工智能辅助分析工具，用于AOSLO图像中中央凹感光器检测和量化的自动化管道。然后，我们将评估不同年龄组的大队列中的小凹细胞地形，以建立正常小凹的地形模型，该模型也将用于建立标准小凹圆锥地形数据的图谱。最后，为了促进广泛的临床应用，我们将把我们的高分辨率地形图数据与临床级别的低分辨率图像数据相关联，研究是否可以使用现成的图像数据作为细胞分辨分析的替代。