Cell-type-specific neural circuit connectomes in the mouse models of aging and Alzheimer's disease

项目摘要 阿尔茨海默病(AD)是老年人进行性痴呆的最常见原因，但没有 治愈这种令人衰弱的疾病。我们假设衰老和阿尔茨海默病相关的病理导致适应不良 作为大量关键脑的连接组枢纽的海马体结构回路的变化 地区，最终导致与年龄和AD相关的认知缺陷。为了回应RFA-AG-22-008，我们已经 组建了一支强大的多人研究团队，跨多个机构，在神经学方面具有互补的专业知识 电路图谱、新一代AD小鼠模型开发、单细胞转录和表观基因组学 分析，和小鼠脑共同坐标框架/图谱的开发。我们将利用卓越的 由UCI神经电路映射中心、Model-AD联盟和Allen提供的资源 脑科学研究所。我们建议对海马区进行大规模、特定细胞类型的标测 用于生成结合了分子和解剖学的细胞分辨率连接体数据的形成电路 注释。为了更准确地捕捉人类AD特征的组合，我们将使用三个互补的 广告鼠标模型包括两个下一代AD鼠标模型。其中包括1)5xFAD小鼠模型 有家族性突变，2)从内源性表达人类野生型Aβ序列的哈伯基小鼠 小鼠App基因座用于模拟晚发性AD特征，以及3)TREM2 R47H敲入小鼠以模拟增加的AD R47H编码变异对晚发性AD的风险。我们将全面绘制和刻画海马区 形成脑回路，包括CA1、下丘脑(亚)和内嗅皮层(EC)，显示最早 阿尔茨海默病小鼠模型和人类患者的神经变性。这些子回路充当神经的中枢。 处理来自许多其他皮质和皮质下大脑区域的信息。我们将使用转基因技术 我们团队开发的跨突触嗜神经性病毒用于绘制全脑顺行和逆行神经 网络。病毒追踪实验产生的大脑连接将在空间上得到增强 基于MERFISH(多路传输错误鲁棒性)的基于单细胞转录的可分辨分子注释 荧光原位杂交)。我们将确定与疾病易感性相关的候选分子。 当我们将空间转录数据叠加到AD调制的电路连接上时，我们会产生抗性。整个 数据集将使用Allen Mouse Brain公共坐标框架进行注释，以促进资源 和数据共享。我们的工作将提高我们对易受衰老和阿尔茨海默病影响的大脑回路的理解 开发更好的AD早期诊断工具和新的治疗策略。

Project Summary Alzheimer’s disease (AD) is the most common cause of progressive dementia in older adults, but there is no cure for this debilitating condition. We hypothesize that aging and AD-related pathologies cause maladaptive changes within hippocampal formation circuits that serve as connectome hubs for large numbers of critical brain regions, ultimately leading to age- and AD-related cognitive deficits. In response to RFA-AG-22-008, we have assembled a strong multi-investigator team across multiple institutions with complementary expertise in neural circuit mapping, next-generation AD mouse model development, single-cell transcriptomics and epigenomics analysis, and mouse brain common coordinate framework / atlas development. We will leverage the exceptional resources offered by the UCI Center for Neural Circuit Mapping, the MODEL-AD Consortium and the Allen Institute for Brain Science. We propose to perform large-scale, cell-type-specific mapping of hippocampal formation circuits to generate cellular resolution connectome data that combines molecular and anatomical annotations. To capture a more accurate composite of human AD features, we will use three complementary AD mouse models including two next-generation AD mouse models. These include 1) the 5xFAD mouse model with familial mutations, 2) the hAß-KI mouse that expresses human wild-type Aβ sequence from the endogenous mouse App locus to model late-onset AD features, and 3) Trem2 R47H knock-in mice that model the increased risk of the R47H coding variant for late onset AD. We will comprehensively map and characterize hippocampal formation brain circuits, including CA1, the subiculum (SUB) and the entorhinal cortex (EC) that show earliest neurodegeneration across AD mouse models and in human patients. These sub-circuits serve as hubs for neural processing from many other cortical and sub-cortical brain regions. We will use genetically modified transsynaptic neurotropic viruses developed by our team to map brain-wide anterograde and retrograde neural networks. The brain connectomes generated from viral tracing experiments will be enhanced with spatially resolved, single-cell transcriptomics-based molecular annotation using MERFISH (multiplexed error-robust fluorescence in situ hybridization). We will identify molecular candidates that confer vulnerability versus disease resistance as we superimpose spatial transcriptomic data on AD-modulated circuit connectomes. The entire data set will be annotated using the Allen Mouse Brain Common Coordinate Framework to facilitate resource and data sharing. Our work will improve our understanding of brain circuits susceptible to aging and AD towards developing better early diagnostic tools and new treatment strategies for AD.