In vivo mapping of foveal photoreceptors in retinal health and disease

尽管人类视网膜中总共有大约1亿个感光细胞，但只有位于中心凹中心的一小束视锥细胞用于大多数视觉任务。这些细胞的功能丧失会对视力造成严重后果，在许多视网膜疾病中，黄斑中心凹感光细胞的结构和功能受到永久性的干扰。尽管有来自世界领先中心的报道表明，光感受器的遗传治疗取得了成功，但视杆细胞和视锥细胞功能挽救的机制及其与视网膜成像中形态读数的相关性尚不清楚。自适应光学成像现在使观察视网膜的微观、细胞结构和功能变化成为可能，为直接显微评估受影响的组织及其对活眼视力的影响打开了大门。部分由于视觉分辨率不足和缺乏分析工具，功能上最相关的区域-中心凹的作用尚未被研究。为了缩小这一差距，该项目将首先开发人工智能辅助分析工具，用于AOSLO图像中中心凹感光细胞检测和量化的自动化管道。然后，我们将评估不同年龄段的一大群人的中心凹细胞地形图，以建立正常中心凹的地形图模型，该模型也将被用来建立标准中心凹锥体地形图数据的图谱。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，研究随时可用的图像数据是否可以作为细胞分辨分析的代理。

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.