Computational dissection of cellular and network vulnerability in Alzheimer's and related dementias

项目摘要 阿尔茨海默病(AD)是一种异质性、多因素疾病，选择性地影响大脑的某些区域。 脑，如蓝斑、内嗅核和海马体。此选择性漏洞(SV)背后的因素 仍然不清楚：为什么进步如此刻板？为什么病理在早期出现在特定的结构中？ 某些细胞使它们容易患上阿尔茨海默病呢？目前的假设集中在特定的特征上， 例如细胞结构、细胞形态、神经递质系统或分子组成。元胞的概念 由于单细胞测序和空间转录技术的进步，脆弱性(“SV-C”)已经流行起来。 另一个漏洞与基于网络的跨神经元“普里恩”病理传递有关，原因是 其中某些电路、光纤路径和区域(网络集线器)可能变得选择性地易受攻击(“SV-N”)。 这项提议将定量测试和验证关于SV-C和SV-N的假设：1)蛋白质聚集， 清除和在网络上的传播低于病理的时空进展；因此，SV 某些区域(例如EC和HIPP)可能仅仅是它们在网络拓扑中的位置的结果。 或者，SV由某些神经细胞类型(例如，大锥体)的分布和组成决定 神经元)，这些细胞是AD病理学选择性靶点。除了这些相互竞争的假设之外，还有一种可能性 这两个因素结合在一起：3)病理受网络传播的支配，但其局部性和传播性 参数受某些细胞类型(如小胶质细胞)的调节。不幸的是，AD研究到目前为止还不能 在这些假设之间进行全面测试，或确定这些漏洞中哪些与之相关。大部分可用 工作台、动物或人体的数据是描述性的，不适用于量化模型。 我们建议为SV-C和SV-N建立网络模型，并建立正式的统计模型来测试它们。我们 充分利用我们实验室最近推出的两项使能技术：矩阵求逆 以及用于创建全脑细胞类型地图的子集选择(MISS)算法；以及网络扩散模型 (NDM)，它从数学上概括了tau沿纤维投影的传输。随着进一步的发展 在这些使能技术中，我们将探索小鼠变态数据和人类变态数据中的SV-C和SV-N 淀粉样蛋白-正电子发射断层扫描。我们还将开发和测试细胞或基因调节网络脆弱性的模型 间接的。如果成功，这个项目可能导致确凿的证据支持或反对每一个已确定的SV 假设。我们将在未来的工作中探索其形态、分子或电生理特性。 入围的细胞、基因、神经通路和网络震中。我们的方法可以成为一种计算性的 为将来的假设生成和测试提供测试平台，而不需要昂贵和费力的实验。 拟议的平台技术(MISH和NEXIS)可能在神经科学中具有更广泛的适用性。

Project Summary Alzheimer's disease (AD) is a heterogeneous, multifactorial disease that selectively affects certain regions of the brain, e.g. locus coeruleus, entorhinal and hippocampus. Factors underlying this selective vulnerability (SV) remain unclear: Why is progression so stereotyped? Why is pathology seen in specific structures at early stages? What about certain cells makes them susceptible to AD? Current hypotheses have focused on specific features, e.g. cytoarchitecture, cell morphology, neurotransmitter system or molecular composition. The concept of cellular vulnerability (“SV-C”) has gained currency due to advances in single cell sequencing and spatial transcriptomics. Another vulnerability relates to network-based trans-neuronal “prion-like” transmission of pathology, due to which certain circuits, fiber pathways and regions (network hubs) may become selectively vulnerable (“SV-N”). This proposal will quantitatively test and validate hypotheses regarding SV-C and SV-N: 1) Protein aggregation, clearance and transmission on the network underly the spatiotemporal progression of pathology; hence SV of certain regions (e.g. EC and Hipp) may simply be a result of their location within the network topology. Alternatively, 2) SV is dictated by distribution and composition of certain neural cell types (e.g. large pyramidal neurons) that are selectively targeted by AD pathology. Beyond these are competing hypotheses is the possibility that both factors combine: 3) Pathology is governed by network transmission, but whose local and spread parameters are mediated by certain cell types (e.g. microglia). Unfortunately, AD research has so far been unable to fully test between these hypotheses or to identify which of these vulnerabilities are germane. Much of available bench, animal or human data are descriptive and do not accommodate quantitative models. We propose to develop network models for SV-C and SV-N and formal statistical models to test them. We capitalize and build on two enabling technologies that have recently come out of our laboratory: Matrix Inversion and Subset Selection (MISS) algorithm for creating whole-brain cell type maps; and Network Diffusion Model (NDM) which mathematically recapitulates transmission of tau along fiber projections. With further development of these enabling technologies, we will explore SV-C and SV-N in mouse tauopathy data and human tau- and amyloid-PET scans. We will also develop and test models where cells or genes mediate network vulnerability indirectly. If successful this project could lead to conclusive evidence for or against each of the identified SV hypotheses. We will explore in future work the morphological, molecular or electrophysiological properties of short-listed cells, genes, neural pathways and network epicenters. Our approach could become a computational test bed for future hypothesis generation and testing, without requiring expensive and laborious experiments. Proposed platform technologies (MISS and NexIS) may have even broader applicability in neuroscience.