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特定于电路的细胞类型-细胞规模的连接体- 选择性地易受细胞体或轴突连接丢失或转录改变的影响 健康老化和阿尔茨海默病(AD)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-细胞类型特定电路变化的知识通过 将单细胞转录组与脑连接相结合--是全面理解衰老和阿尔茨海默病的必要条件 并提供了解决纵向变化的实验上容易处理的基础。这些老化-和AD- 相关的变化可能包括细胞类型的丧失、连接性或转录特征的改变。这 这里采用的方法是检验这一假设，即存在衰老或AD状态特定的神经和 推动衰老和阿尔茨海默病进展的分子回路。大量证据表明，AD是 一种异质性、多因素的疾病，选择性地影响某些大脑区域，如内嗅皮层 (EC)，而其他区域，如小脑，仍未受到影响。阿尔茨海默病分期研究进展 神经病理学显示，AD相关的神经病理始于蓝斑(LC)或EC，其次是 海马体(HC)，然后是前额叶皮质(PFC)。LC既含有肾上腺素(NA)，又含有非肾上腺素 去甲肾上腺素能神经元，并提供整个大脑的主要NA输入。神经病理学 分期表明，LC中出现了缠绕拳头，NA激活已被证明可以改善AD 赤字。EC向HC提供关键的皮质输入，而HC在学习记忆中是必不可少的。PFC 对各种高阶函数提供自上而下的调节。而是基于单元类型的输入和/或输出 在单个神经元水平上选择性地易受攻击的网络并没有得到很好的理解。因为老龄化是一个主要问题 AD的危险因素，了解选择性地存在明显、相似或重叠是很重要的 衰老和阿尔茨海默病之间易受攻击的电路特定细胞类型。该项目是结合逆行标记和 多组体sn-RNAseq和sn-ATACseq将细胞类型的转录和表观基因组特性连接到 神经元投射和研究与衰老和阿尔茨海默病进展相关的电路特异性变化 在雄性和雌性对照组和AD小鼠中，有四个脑区，即LC，EC，HC和PFC。对于AD 小鼠，APPNLF小鼠系--携带人类淀粉样前体蛋白基因的敲门突变， 重要的是，表达生理水平的Aβ，模拟迟发性AD-将被使用。来自这里的数据 该项目将为易受变性和/或改变影响的神经元类型提供新的见解 空间和时间方式的分子/信号签名网络以及与 神经病理学和认知障碍。这种方法是朝着建立多尺度模型迈出的重要一步 这将有助于填补遗传变异(如APP、AOPE或TREM2)对大脑的影响之间的空白 衰老和AD中分子网络的拓扑学。

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.