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 治愈这种虚弱的状况。我们假设衰老和AD相关的病理导致适应不良 海马结构回路内的变化，海马结构回路作为大量关键大脑的连接体枢纽， 区域，最终导致年龄和AD相关的认知缺陷。为了响应RFA-AG-22-008，我们 在多个机构组建了一支强大的多研究者团队，在神经病学方面具有互补的专业知识。 电路映射，下一代AD小鼠模型开发，单细胞转录组学和表观基因组学 分析和小鼠脑共同坐标框架/图谱开发。我们将利用特殊的 由UCI神经回路映射中心、MODEL-AD联盟和艾伦提供的资源 脑科学研究所。我们建议进行大规模的，细胞类型特异性的海马映射， 形成电路，以生成结合分子和解剖学的细胞分辨率连接体数据， 注释。为了捕捉更准确的人类AD特征组合，我们将使用三个互补的 AD小鼠模型，包括两种下一代AD小鼠模型。这些包括1）5xFAD小鼠模型 具有家族性突变的hA β-KI小鼠，2）从内源性A β基因表达人野生型Aβ序列的hA β-KI小鼠， 小鼠App基因座以模拟迟发性AD特征，和3）Trem 2 R47 H敲入小鼠，其模拟增加的 R47 H编码变异体对迟发性AD的风险。我们将全面绘制和表征海马 形成脑回路，包括CA 1，下托（ESTIUM）和内嗅皮层（EC），显示最早的 在AD小鼠模型和人类患者中的神经变性。这些子回路作为神经网络的枢纽， 处理来自许多其他皮层和皮层下大脑区域的信息。我们将使用转基因技术 我们的团队开发的跨突触嗜神经病毒，用于绘制全脑顺行和逆行神经 网络.从病毒追踪实验中产生的大脑连接体将在空间上得到增强。 使用MERFISH（多路错误鲁棒性）进行基于单细胞转录组学的分子注释 荧光原位杂交）。我们将确定分子候选人，赋予脆弱性与疾病 阻力，因为我们收集了AD调制电路连接体的空间转录组数据。整个 数据集将使用艾伦小鼠大脑通用坐标框架进行注释，以便于资源 和数据共享。我们的工作将提高我们对易受衰老和AD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.