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

通过病理索引 scRNA-seq、空间转录组学和 CRISPR 筛选发现阿尔茨海默病的细胞类型特异性驱动基因

• 批准号: 10605280
• 负责人: Yanling Wang
• 金额: $ 74.42万
• 依托单位: RUSH UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-15 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10605280
• 关键词: Acceleration; Affect; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease diagnosis; Alzheimer' s disease pathology; Alzheimer' s disease patient; Antibodies; Astrocytes; Autopsy; Bar Codes; Biotechnology; Brain; Brain region; CRISPR screen; Calibration; Candidate Disease Gene; Cataloging; Cell Shape; Cell model; Cells; Cellular Stress; Chronic; Clustered Regularly Interspaced Short Palindromic Repeats; Computer Analysis; Computing Methodologies; Data; Dementia; Development; Disease; Disease Progression; Dorsal; Environment; Gene Expression; Genes; Genetic Screening; Genotype; Guide RNA; Hippocampus; Human; In Vitro; Individual; Inflammation; Lateral; Measurement; Measures; Messenger RNA; Methods; Microglia; Molecular; Mus; Neurofibrillary Tangles; Neurons; Oligonucleotides; Participant; Pathogenesis; Pathologic; Pathology; Pathway interactions; Phenotype; Prefrontal Cortex; Research; Resolution; Senile Plaques; Systems Biology; Technology; Testing; apolipoprotein E-3; apolipoprotein E-4; beta amyloid pathology; brain tissue; candidate identification; cell cortex; cell type; cellular pathology; clinical diagnosis; combinatorial; deep learning; design; disease phenotype; extracellular; gene network; human model; indexing; induced pluripotent stem cell; innovation; mouse model; novel; prevent; protein TDP-43; response; single nucleus RNA-sequencing; single-cell RNA sequencing; stressor; tau-1; therapeutic target; transcriptome; transcriptomics

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.

项目摘要 虽然β淀粉样蛋白斑块和神经纤维缠结是目前病理诊断的标准， 阿尔茨海默病（AD），只有9%的临床诊断AD患者具有“纯粹”AD病理，而大多数AD 病例具有混合病理，这显著增加了痴呆症的几率。因为不同的内部和 细胞外病理和应激源有助于AD进展，因此必须跟踪它们如何影响AD进展。 通过将细胞类型特异性的转录组学反应分类为脑内和脑内的各种细胞类型， AD发病机制中的细胞外病理学。因此，本建议旨在衡量多个 在其原生环境中每种细胞类型的病理学，然后通过以下方式使此信息可操作： 通过计算确定这些影响的驱动因素，并在人类细胞模型中进行测试。为此我们 提出了两种方法来同时测量细胞转录组和多种病理， 数以百万计的单个细胞。第一种方法，“病理索引scRNA-seq”， 专为细胞内病变设计。它结合了单细胞RNA-seq（scRNA-seq）和一组寡核苷酸， 针对细胞内病变的条形码抗体。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个细胞中。第二种方法，“病理学 空间转录组学”是为细胞外病理学设计的。获得1~10个细胞（55- μM分辨率）与细胞外病理学的空间配准。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。我们将运用这两个创新的 两个大脑区域的测序技术，背外侧前额叶皮层和海马， ROSMAP参与者的死后大脑。使用单变量、系统生物学和深度 通过学习计算方法，我们将确定驱动细胞类型特异性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2020年进行CRISPR筛选。 来源于对照和AD同基因iPSC系的人皮质细胞模型。总的来说，我们的研究将 AD发病机制中细胞类型特异性驱动基因的重要光，定义导致AD发病的分子途径， 细胞疾病状态，并提供实验验证的目标，以防止疾病状态的转变 早期AD的发展。