Assessing how ocular surface nerves, immune cells, and epithelial cells communicate to encourage neuro-immune homeostasis

项目总结 眼表神经的性质和功能在维持眼表健康方面起着至关重要的作用 预防疾病。眼表感觉神经受损可导致失明角膜炎、移植排斥反应、 干眼症，眼表疼痛。-组学研究的出现提供了一个平台， 了解眼表神经如何参与眼部健康和疾病的更广泛的背景。这里, 我们概述了一项提案，该提案将创建详细描述眼表神经支配性质的大型数据集。我们会 采用系统级方法使用生物信息学和机器学习来分析我们的数据 平台，使我们能够对健康和健康期间的眼表环境有一个实际的了解 疾病。具体地说，这项建议将解决眼表神经、上皮细胞和免疫细胞如何 互动。我们招募了一个由眼科专家组成的多学科研究团队， 神经免疫学、神经学、蛋白质组学、系统免疫学和生物信息学。有了这个团队，我们计划 全面分析RFA中概述的所有三个层次的研究，包括解剖学和 确定神经元和非神经元细胞的细胞和分子特性的形态特征 并评估神经元和非神经元细胞的功能特性。因为神经支配模式 和功能随疾病而变化，我们将使用多种眼表疾病模型(病毒性角膜炎和 房水干眼症)，以更好地了解神经元、上皮细胞和免疫细胞如何影响神经元 功能和随后的疾病。我们将通过追求四个具体目标来实现我们的目标：1)我们将 对支配角膜和眼表的神经元和非神经元细胞进行单细胞(Sc)组学 神经节。这些数据将描述神经元的特性，并描述它们在疾病期间的功能。 此外，我们将能够从分子上确定免疫和上皮细胞的特性，这些细胞会影响 疾病期间眼表传入神经的功能。2)我们将使用质谱仪来分析 健康和疾病期间角膜的蛋白质组特征。一支不偏不倚的猎枪走近，一支 将使用专注于神经营养因子的有针对性的方法来识别影响眼睛的蛋白质 体表神经功能。3)我们将利用我们的成像技术和组织细胞计数法来全面 分析角膜内神经细胞和非神经细胞的位置和形态，以便我们可以 能够对神经、上皮和免疫细胞如何在近距离相互作用做出结论 眼表。4)我们将使用DREADD(由设计师药物独家激活的设计师受体)技术 来扰乱神经元的敏感性，这样我们就可以评估如何改变神经元对刺激的反应 影响眼表动态平衡和病情进展。对我们的观察结果进行系统级别的分析 使用机器学习和生物信息学将导致预测新的相互作用网络 在未来的研究中被询问。所有数据将被存放到我们将开发的门户网站。

PROJECT SUMMARY The nature and function of ocular surface nerves play a critical role in maintaining ocular surface health while preventing disease. Disruption of ocular surface sensory nerves can lead to blinding keratitis, graft rejection, dry eye disease, and ocular surface pain. The advent of “-omics” studies have provided a platform to understand how ocular surface nerves participate in the broader context of ocular health and disease. Here, we outline a proposal that will create large datasets detailing the nature of ocular surface innervation. We will take a systems-level approach towards analyzing our data using bioinformatics and machine learning platforms, so that we can gain a practical understanding of the ocular surface environment during health and disease. Specifically, this proposal will address how ocular surface nerves, epithelial cells, and immune cells interact. We have recruited a multidisciplinary research team that consists experts in ophthalmology, neuroimmunology, neurology, proteomics, systems immunology, and bioinformatics. With this team, we plan to comprehensively analyze all three levels of research outlined in the RFA, which includes anatomical and morphological characterization, defining cellular and molecular properties of neuronal and non-neuronal cell types, and assessing functional properties of neuronal and non-neuronal cells. Because innervation patterns and functionality changes with disease, we will use multiple ocular surface disease models (viral keratitis and aqueous dry eye disease) to better understand how neurons, epithelial cells, and immune cells affect neuronal functionality and subsequent disease. We will achieve our goal by pursuing four specific aims: 1) We will perform single-cell (sc)-omics on neuonrs and non-neuronal cells of the cornea and ocular surface innervating ganglia. These data will characterize neuronal identities and describe their functionality during disease. Additionally, we will be able to molecularly characterize immune and epithelial cells that influence the functionality of ocular surface afferents during disease. 2) We will use mass spectrometry to analyze the proteomic signatures of the cornea during health and disease. An unbiased shotgun approaches and a targeted approach focusing on neurotrophic factors will be used to identify proteins that influence ocular surface nerve functionality. 3) We will use our imaging techniques and histocytometry to comprehensively analyze the location and morphology of neuronal and non-neuronal cells within the cornea, so that we may be able to make conclusions about how nerves, epithelial, and immune cells interact in close proximity at the ocular surface. 4) We will use DREADD (designer receptor exclusively activated by designer drugs) technology to perturb neuronal sensitivity, so that we can assess how altering neuronal responsiveness to stimuli may affect the ocular surface homeostasis and progress of disease. A systems-level analysis of our observations using machine-learning and bioinformatics will lead to the prediction of novel interaction networks that can be interrogated in future studies. All data will be deposited into a web portal that we will develop.