Uncovering cell-type-specific driver genes of Alzheimer's Disease by pathology-indexing scRNA-seq, spatial transcriptomics, and CRISPR screens

项目总结 虽然淀粉样蛋白-β斑块和神经原纤维缠结是目前病理诊断的标准 阿尔茨海默病(AD)，临床诊断的AD患者中只有9%的人有纯粹的AD病理，大多数AD 病例有混合病理，这显著增加了患痴呆症的几率。因为不同的内部和 细胞外病理和应激源导致AD进展，跟踪它们如何影响AD进展是至关重要的 通过对细胞类型特异的转录反应进行分类来研究大脑中不同类型的细胞 阿尔茨海默病发病中的细胞外病理。因此，这项提议旨在衡量多个 每种细胞类型在其自然环境中的病理，然后通过以下方式使该信息可操作 通过计算确定这些效应的驱动因素，并在人体细胞模型中进行测试。为此，我们 提出两种同时测量细胞转录本和多种病理变化的方法 数以百万计的单个细胞在它们的自然环境中。第一种方法是“病理索引scRNA-seq”， 专为细胞内病理而设计。它将单细胞rna-seq(scRNA-seq)与一组寡核苷酸结合在一起。 针对细胞内病理的条形码抗体。这种方法使我们能够同时测量 基因表达和多种细胞内病变都在同一细胞内。第二种方法，“病理学” 空间转录学“是为细胞外病理而设计的。获得了1~10个细胞的基因表达(55- μM分辨率)与细胞外病理进行空间配准。这使我们能够量化 细胞外病理和微环境对细胞疾病状态的影响。我们将应用这两项创新 两个脑区，背外侧前额叶皮质和海马区的测序技术 表型较深的ROSMAP参与者的死后脑。使用单变量、系统生物学和深度 学习计算方法，我们将确定驱动特定细胞类型疾病状态的候选基因。 为了测试预测的早期驱动基因并提供治疗靶点，我们将在 人脑皮质细胞模型来源于对照和AD等基因IPSC系。总而言之，我们的研究将摆脱 AD发病机制中细胞类型特异性驱动基因的重要研究，定义了导致AD的分子通路 细胞疾病状态，并提供实验验证的目标，以防止疾病状态转换 在AD早期发展过程中。

PROJECT SUMMARY Although amyloid-β plaques and neurofibrillary tangles are the current criteria for pathologic diagnosis of Alzheimer’s Diseases (AD), only 9% of clinically diagnosed AD patients have "pure" AD pathology and most AD cases have mixed pathologies, which significantly increase the odds of dementia . Because diverse intra- and extracellular pathologies and stressors contribute to AD progression, it is essential to track how they affect the various cell types of the brain by cataloging cell-type-specific transcriptomic responses to both intra- and extracellular pathologies in AD pathogenesis. Therefore, this proposal aims to measure the effects of multiple pathologies on each cell type in their native environment, then make this information actionable by computationally identifying the drivers of these effects and testing them in human cell models. To this end, we propose two approaches to simultaneously measure the cell transcriptomes and multiple pathologies in millions of individual cells in their native context. The first approach, “pathology-indexing scRNA-seq,” is designed for intracellular pathologies. It combines single-cell RNA-seq (scRNA-seq) with a set of oligo- barcoded antibodies against intracellular pathologies. This approach enables us to simultaneously measure gene expression and multiple intracellular pathologies all in the same cell. The second approach, “pathology spatial transcriptomics,” is designed for extracellular pathologies. It obtains gene expression of 1~10 cells (55- μM resolution) in spatial registration with extracellular pathology. This enables us to quantify the effects of extracellular pathologies and microenvironment on cell disease states. We will apply these two innovative sequencing technologies to two brain regions, the dorsal lateral prefrontal cortex and hippocampus of postmortem brains of deeply-phenotyped ROSMAP participants. Using univariate, systems biology, and deep learning computational methods, we will identify candidate genes that drive cell-type-specific disease states. To test predicted early driver genes and provide therapeutic targets, we will conduct CRISPR screens in human cortical cell models derived from control and AD isogenic iPSC lines. Collectively, our study will shed important light on the cell-type-specific driver genes in AD pathogenesis, define molecular pathways leading to cell disease-states, and provide experimentally validated targets for preventing the disease-state transition during early AD development.