Single-cell multi-region transcriptional and epigenomic dissection of VCID.

摘要 阿尔茨海默病（AD）和AD相关性痴呆（ADRD）是死亡率、发病率和死亡率的主要驱动因素。 由于人口老龄化，缺乏预测性诊断， 缺乏有效的治疗或预防。血管对认知障碍和痴呆的影响 （VCID）是AD和ADRD的关键因素，并通过各种脑血管病变表现出来， 包括动脉粥样硬化、微梗塞和小血管中风。VCID包括脑小血管疾病 脑淀粉样血管病（CAA）和CSVD的单基因家族形式（CADASIL，脑淀粉样血管病）。 常染色体显性遗传性动脉病伴皮质下白质脑病）。了解 VCID的机制和驱动因素将使新的生物标志物和治疗方法成为可能，类似于 解决心血管疾病和心脏病中的高血压。为了了解细胞机制 在大脑区域、细胞类型、病理学和分子通路的基础VCID中，我们执行高- 在死后的CNS样本中， 散发性和遗传性VCID患者。目的1：我们分析了单核RNA测序（snRNA-seq）和DNA 可访问性（snATAC-seq），以创建跨诊断、脑 区域、细胞类型、性别和个体。目标2：我们创建了一个SVD相关基因变化的图谱， 模块、通路和细胞间相互作用。目标3：我们预测候选驱动基因，调节基因， 使用跨时间和遗传模型的因果关系分析，我们验证了我们的结果 实验性地使用成像研究。我们的研究的成功执行将描述临床相关的 VCID生物标志物和治疗方法跨越散发性和遗传性VCID，使我们能够剖析他们的共同点， 和不同的分子回路，跨越四个受影响的CNS区域和其中的所有主要细胞类型， 捕获前所未有的复杂性水平，并实现丰富的计算比较。数据集 生成和计算分析将为解决紧迫的医疗问题提供宝贵的见解。 需要VCID，它们的时间，区域特异性和细胞类型特异性变化，这可以帮助指导新的 治疗学

Abstract Alzheimer’s disease (AD) and AD-related dementias (ADRDs) are major drivers of mortality, morbidity, and health care costs for patients and their loved ones, due to the aging population, lack of predictive diagnosis, and lack of effective treatments or prevention. Vascular contributions to cognitive impairment and dementia (VCIDs) are key contributors to AD and ADRDs and manifest through diverse cerebrovascular lesions, including atherosclerosis, microinfarcts, and small vessel strokes. VCIDs include cerebral small vessel disease (CSVD), cerebral amyloid angiopathy (CAA), and a monogenic familial form of CSVD (CADASIL, Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy). Understanding the mechanisms and drivers of VCID will enable new biomarkers and therapeutics, similar to the success of addressing cardiovascular disease and hypertension in heart disease. To understand the cellular mechanisms underlying VCID across brain regions, cell types, pathology, and molecular pathways, we perform high- resolution profiling of epigenomic and transcriptional alterations in post-mortem CNS samples from both sporadic and genetic VCID patients. Aim 1: We profile single-nucleus RNA-sequencing (snRNA-seq) and DNA accessibility (snATAC-seq) to create a transcriptional and epigenomic atlas of VCID across diagnoses, brain regions, cell types, sexes, and individuals. Aim 2: We create an atlas of SVD-associated changes in genes, modules, pathways, and cell-cell interactions. Aim 3: We predict candidate driver genes, regulators, and pathways using causality analyses across temporal and genetic models, and we validate our results experimentally using imaging studies. The successful execution of our studies will delineate clinically-relevant VCID biomarkers and therapeutics across sporadic and genetic VCID, enabling us to dissect their common and distinct molecular circuits, across four affected CNS region and all major cell types within them, and capturing an unprecedented level of complexity and enabling rich computational comparisons. The datasets generated and the computational analyses will provide invaluable insights for addressing the pressing medical need of VCIDs, their temporal, region-specific, and cell-type-specific changes, which can help guide new therapeutics.