Blink, Lacrimation, and Nociception: Precision Mapping and Integrated Atlas Generation of Corneal Trigeminal Afferents

摘要 调节疼痛、眨眼反射和泪液产生的角膜神经在正常情况下是不可或缺的 维持眼表的动态平衡。然而，这些神经元的复杂性，无论是在轴突水平上 在角膜中，以及三叉神经节中的细胞体，已经越来越难以完全掌握它们 自然，造成了该领域的关键知识空白。与当前U01的号召一致，我们将 通过对角膜神经的形态、分子和结构的全面分析来解决这一障碍 职能层面。我们组建了一支专业互补的多学科团队，使 对小鼠的空间、电生理、基因组和行为特征的综合分析；以及人工智能辅助 在人体受试者中进行结构-功能研究，在该领域开辟新的进展。在目标1中，我们将从解剖学上 并对小鼠的角膜投射三叉神经传入进行功能定位。遗传方法将被应用于 使用七种方法严格描述角膜和三叉神经节中伤害性感受器的空间分布 彩色免疫标记。这种空间信息将直接与人体的电生理特征相联系 这些各自的种群。在目标2中，将对角膜投射的三叉神经传入进行基因组分析 在细胞分辨率下应用了新的空间RNA-SEQ平台。我们将使用老鼠品系来 在基因水平上解析出转录本和行为输出。此外，我们还应用了两部小说 与眨眼相比，选择性靶向参与泪液产生的角膜神经的方法。在《目标3》中，我们将 发现角膜神经的形态模式，预测人类的眨眼、流泪和伤害性感受。 我们将用活体共聚焦显微镜对患有不同的眨眼、泪水和 伤害性行为输出，从而捕获AIMS 1和AIMS中的小鼠实验的功能类似 2.利用我们正在开发的基于人工智能的自动分割算法，我们将能够在机器上应用 学习神经模式的多维描述，然后将这些与各自的行为进行比较 产出。这些人工智能引导的努力将为理解角膜传入结构-功能提供关键线索 在人类身上。总而言之，我们的集体研究将导致史无前例的角膜传入地图绘制 眨眼、流泪和伤害。这项工作的进展将有助于更深层次的 了解相关的病理生物学，包括神经性眼痛和干眼病 为未来的翻译和临床研究奠定基础。所有基因组、成像和电生理数据集 生产的产品将公开上市，所有用于角膜神经图像分割的软件产品将 以开放源码和易于使用的软件包形式在网上免费提供。

ABSTRACT Corneal nerves that mediate pain, blink reflexes and tear production are indispensable in the proper maintenance of ocular surface homeostasis. However, the complexity of these neurons, both at the axon level in the cornea, and cell bodies in the trigeminal ganglion, has made it increasingly difficult to grasp their full nature, resulting in key knowledge gaps in the field. Consistent with the call for the current U01, we will address this barrier via comprehensive analyses of corneal nerves at the morphologic, molecular, and functional level. We have assembled a multidisciplinary team with complementary expertise, enabling integrated analyses of spatial, electrophysiologic, genomic and behavioral profiles in mice; and AI-assisted structure-function studies in human subjects, to open new inroads in the field. In Aim 1, we will anatomically and functionally map corneal-projecting trigeminal afferents in mice. Genetic approaches will be applied to rigorously profile the spatial arrangement of nociceptors in the cornea and trigeminal ganglion using seven- color immunolabeling. This spatial information will be directly linked with the electrophysiological profiling of these respective populations. In Aim 2, genomic analyses of corneal-projecting trigeminal afferents will be conducted applying a new platform for spatial RNA-seq at cellular resolution. We will use mouse strains to parse out the transcriptomes and behavioral outputs at the genetic level. Moreover, we apply two novel approaches to selectively target corneal nerves involved in tear production versus blinking. In Aim 3, we will discover morphological patterns of corneal nerves that predict blinking, lacrimation, and nociception in humans. We will image corneal nerves with in vivo confocal microscopy of subjects with differential blink, tear, and nociceptive behavioral outputs, thereby capturing functional analogs of the mouse experiments in Aims 1 and 2. With the AI-based auto-segmentation algorithm that we are developing, we will be able to apply machine learning for multidimensional profiling of nerve patterns, and then compare these with respective behavioral outputs. These AI-guided efforts will provide critical clues for understanding corneal afferent structure-function in humans. In summary, our collective studies will lead to an unprecedented cartography of corneal afferents in blink, lacrimation, and nociception. The advancements from this work will be poised to facilitate a deeper understanding of related pathobiology including neuropathic ocular pain and dry eye disease that will lay the foundation for future translational and clinical research. All genomic, imaging and electrophysiologic datasets produced will be made publicly available, and all software products for corneal nerve image segmentation will be made freely available online as open-source and easy-to-use software packages.