CRCNS: Deep Learning to Discover Neurovascular Disruptions in Alzheimer's Disease

神经血管单位（NVU）由脑血管系统中的细胞（内皮细胞和周细胞）组成， 这些细胞与实质细胞（神经元和星形胶质细胞）协调以维持脑稳态和认知功能。 NVU细胞之间复杂的功能相互作用，称为神经血管偶联，逐渐受损 并且NVU组成在阿尔茨海默病中被严重破坏。然而，潜在的病理生理学 由于缺乏有关丰度较低但仍存在的分子水平信息，人们对机制知之甚少 脑血管内皮细胞和周细胞。功能磁共振成像，广泛应用于临床， 评估神经血管耦合，可能无法告知分子水平的变化。确定以下方面的变化具有挑战性： NVU组成使用标准组织病理学方法，因为它们缺乏定位的灵敏度和特异性 脑组织中的内皮细胞和周细胞来自死后阿尔茨海默氏症脑组织的批量RNA测序 可用于研究NVU组分，但它测量所有细胞的平均基因表达，因此使其 很难定义细胞特异性途径和NVU组分相互作用。单细胞方法和线性 目前使用去卷积技术来分析大量RNA测序数据以确定细胞类型特异性基因表达模式。然而，这些技术难以捕获低丰度细胞（如内皮细胞和周细胞）的分子特征。当前研究的目标是开发深度学习方法 准确预测NVU细胞的组成和转录组特征，并绘制它们之间的相互作用。 我们的中心假设是，数据驱动的深度学习模型，具有灵活性，可以捕捉潜在的 脑组织中的基因-基因和细胞-细胞相互作用，将预测 NVU细胞比传统方法更有效。在目标1中，我们将设计一种新型深度学习框架NUGENT，以识别细胞类型组成并预测细胞类型特异性基因表达模式。在Aim中 2，我们将使用从阿尔茨海默病中收获的NVU组成细胞的新scRNA-seq数据验证NUGENT 疾病转基因小鼠（APPswe/PSEN 1dE 9）和它们的非转基因同窝仔。使用在 目标2和公开可用的NUGENT框架上的患者和小鼠数据，在目标3中，我们将研究 分子通路调节认知正常和阿尔茨海默病患者的神经血管耦合。极有 这些研究可能有助于确定神经血管功能障碍的分子决定因素， 与年龄相关的认知衰退和阿尔茨海默氏痴呆症，并促进新的生物标志物的发现， 治疗目标

The Neurovascular unit (NVU) comprises of cells in the brain vasculature (endothelial cells and pericytes) working in coordination with parenchymal cells (neurons and astrocytes) to maintain brain homeostasis and cognitive function. Intricate functional interactions among NVU cells, referred to as neurovascular coupling, is progressively impaired and NVU composition is severely disrupted in Alzheimer’s disease. However, the underlying pathophysiological mechanisms are poorly understood due to paucity of molecular level information on the less abundant, yet functionally critical, cerebrovascular endothelial cells and pericytes. The fMRI imaging, widely used in the clinic to evaluate neurovascular coupling, may not inform molecular level changes. It is challenging to identify changes in NVU composition using standard histopathological methods, because they lack sensitivity and specificity to locate endothelial cells and pericytes in the brain tissue. Bulk RNA sequencing from postmortem Alzheimer's brain tissue can be used to investigate NVU components, but it measures gene expression averaged across all cells, thus making it difficult to define cell-specific pathways and NVU constituent interactions. Single-cell methods and linear deconvolution techniques are currently employed to analyze bulk RNA sequencing data to determine cell-type-specific gene expression patterns. However, these techniques struggle to capture the molecular signature of low-abundant cells like endothelial and pericytes. The objective of the current study is to develop deep-learning methods to accurately predict the composition and transcriptomic signature of NVU cells, and to map interactions among them. Our central hypothesis is that data-driven deep-learning models, which have the flexibility to capture underlying gene-gene and cell-cell interactions in the brain tissue, will predict the composition and transcriptomic signature of NVU cells more effectively than the conventional methods. In Aim 1, we will design NUGENT, a novel deep-learning framework, to identify cell-type composition and predict cell-type-specific gene expression patterns. In Aim 2, we will validate NUGENT using new scRNA-seq data of NVU constituent cells harvested from Alzheimer’s disease transgenic mice (APPswe/PSEN1dE9) and their non-transgenic littermates. Employing the data generated in Aim 2 and publicly available patient and mouse data on the NUGENT framework, in Aim 3 we will investigate molecular pathways regulating neurovascular coupling in cognitively normal and Alzheimer’s patients. It is highly likely that the proposed studies will help identify molecular determinants of neurovascular dysfunction underlying age-related cognitive decline and Alzheimer’s dementia and facilitate the discovery of novel biomarkers and therapeutic targets.