Circuit-specific cell types in aging and Alzheimer's disease

摘要 这个项目的长期目标是定义和识别电路特异性细胞类型-细胞尺度连接体- 它们选择性地易受细胞体或轴突连接的损失或转录组的变化的影响。 在健康衰老和阿尔茨海默病（AD）的进展过程中，单个神经元的特征。 有证据表明，关于细胞尺度连接体-细胞类型特异性回路变化的知识， 将单细胞转录组与大脑连接耦合-这是全面了解衰老和AD所必需的 并提供了一个实验上易于处理的基础来解决纵向变化。这些老化-和AD- 相关的变化可包括细胞类型、连接性的丧失或转录组学特征的改变。这 这里采用的方法是检验存在衰老或AD状态特异性神经和 分子电路驱动老化和AD的进展。大量证据表明，AD是 一种选择性地影响大脑某些区域（如内嗅皮层）的异质性多因素疾病 (EC)，而其他区域，如小脑，则不受影响。AD分期的研究进展 神经病理学显示AD相关的神经病理学开始于蓝斑（LC）或EC，然后是 海马体（HC）和前额叶皮层（PFC）。LC含有肾上腺素能（NA）和非肾上腺素能（NA）。 去甲肾上腺素能神经元，并提供整个大脑的主要NA输入。神经病理 分期显示缠结首先出现在LC中，并且NA活化已显示改善AD 赤字EC为HC提供关键的皮层输入，这在学习记忆中是必不可少的。的PFC 提供了对各种高阶函数的自顶向下调节。但是基于细胞类型的输入和/或输出 在单个神经元水平上选择性脆弱的网络还没有得到很好的理解。由于衰老是一个主要的 AD的危险因素，重要的是要了解是否有不同的，相似的或重叠的选择性 脆弱的电路特定的细胞类型之间的老化和AD。该项目是将联合收割机逆行标记与 多组sn-RNAseq和sn-ATACseq将细胞类型的转录组学和表观基因组学特性与 神经元投射，并研究与衰老和AD进展相关的回路特异性变化， 四个脑区，即LC，EC，HC和PFC，在雄性和雌性对照和AD小鼠中。的AD 小鼠，APPNLF小鼠系-在淀粉样前体蛋白基因中携带敲入人类突变， 重要的是，表达生理水平的Aβ，模拟迟发性AD。从这个数据 该项目将提供关于神经元类型的新见解，这些神经元容易变性和/或改变。 分子/信号特征网络的空间和时间的方式和相关性， 神经病理学和认知障碍。这种方法是建立多尺度模型的重要一步 这将有助于填补遗传变异的影响之间的差距（例如，APP、AOPE或TREM 2） 拓扑学与衰老和AD中的分子网络。

Abstract The long-term goal of this project is to define and identify circuit-specific cell types–cellular scale connectome– that are selectively vulnerable to loss of cell bodies or axonal connections or change of transcriptomic signatures of individual neurons during the progression of healthy aging and Alzheimer's disease (AD). Evidence suggests that knowledge on the change of cellular scale connectomes–cell type-specific circuits by coupling single cell transcriptome with brain connectivity– is needed for holistic understanding of aging and AD and provides an experimentally tractable basis to address longitudinal changes. These aging- and AD- associated changes may include loss of cell types, connectivity or alterations in transcriptomic signatures. This approach employed here is to test the hypothesis that there are aging- or AD state-specific neural and molecular circuits that drive the progression of aging and AD. A large body of evidence demonstrates that AD is a heterogeneous, multifactorial disease that selectively affects certain brain regions, e.g. the entorhinal cortex (EC), while other areas, such as the cerebellum, remain unaffected. Recent studies on the staging of AD neuropathology showed AD-related neuropathology begins in the locus coeruleus (LC) or the EC, followed by the hippocampus (HC) and then the prefrontal cortex (PFC). The LC contains both adrenergic (NA) and non- noradrenergic neurons and provides the major NA inputs throughout the entire brain. Neuropathological staging has shown that tangles fist appear in the LC and NA activation has been shown to ameliorate AD deficits. The EC provides key cortical inputs to the HC, which is essential in learning memory. The PFC provides the top-down regulation on various higher order functions. But cell types-based input and/or output networks that are selectively vulnerable at the single neurons level are not well understood. As aging is a major risk factor for AD, it is important to understand whether there are distinct, similar or overlapping selectively vulnerable circuit-specific cell types between aging and AD. This project is to combine retrograde labeling with multiomic sn-RNAseq and sn-ATACseq to link transcriptomic and epigenomic properties of cell types to neuronal projections and investigate circuit-specific changes associated with progression of aging and AD in four brain regions, namely the LC, EC, HC and PFC, in both male and female control and AD mice. For AD mice, the APPNLF mouse line–that carries knockin human mutations in the amyloid precursor protein gene and, importantly, expresses physiological levels of Aβ, mimicking late onset AD–will be used. The data from this project will provide novel insights on the types of neurons vulnerable to degeneration and/or alterations of molecular/signaling signature networks in a spatial and temporal fashion and the correlation with neuropathology and cognitive impairment. This approach is a major step toward establishing multiscale models that will help to fill the gap between the effects of genetic variants (e.g., APP, AOPE or TREM2) on brain topology with molecular networks in aging and AD.