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

阿尔茨海默病和相关痴呆症细胞和网络脆弱性的计算剖析

• 批准号: 10900995
• 负责人: Ashish Raj
• 金额: $ 76.07万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10900995
• 关键词: Affect; Algorithms; Alprostadil; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease related dementia; Amyloid; Amyloid beta-Protein; Atrophic; Beds; Brain; Brain region; Calcium; Cell model; Cells; Cellular Morphology; Data; Deposition; Development; Diffusion; Disease; Dissection; Electrophysiology (science); Exhibits; Fiber; Functional disorder; Future; Generations; Genes; Hippocampus; Human; Joints; Laboratories; Location; Maps; Mathematics; Mediating; Mediator; Microglia; Mitochondria; Modeling; Molecular; Molecular Profiling; Morphology; Mus; Network-based; Neural Pathways; Neurons; Neurosciences; Neurotransmitters; Outcome; Pathology; Pathway interactions; Patients; Positron-Emission Tomography; Predisposition; Process; Property; Research; Spatial Distribution; Statistical Models; Stereotyping; Structure; System; Tauopathies; Technology; Testing; Transgenic Mice; Work; amyloid pathology; animal data; brain cell; cell type; connectome; experimental study; hippocampal pyramidal neuron; human data; in silico; locus ceruleus structure; mathematical model; network models; neural network; pathogen; predictive modeling; prion-like; protein aggregation; protein misfolding; proteostasis; response; single cell sequencing; spatiotemporal; stressor; tau Proteins; technology platform; transcriptomics; transmission process

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.

项目摘要 阿尔茨海默氏病（AD）是一种异质性、多因素疾病，其选择性地影响大脑皮层的某些区域， 脑，例如蓝斑、内嗅和海马。造成这种选择性脆弱性的因素 仍然不清楚：为什么进步如此刻板？为什么病理学在早期阶段出现在特定的结构中？ 是什么让某些细胞容易患上AD？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。细胞的概念 由于单细胞测序和空间转录组学的进步，易感染性（“SV-C”）已经流行起来。 另一个弱点涉及基于网络的跨神经元“朊病毒样”病理学传播， 某些电路、光纤路径和区域（网络集线器）可能变得选择性易受攻击（“SV-N”）。 该提案将定量测试和验证关于SV-C和SV-N的假设：1）蛋白质聚集， 网络上的清除和传播是病理学时空进展的基础;因此， 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，2）SV由某些神经细胞类型（例如大锥体细胞）的分布和组成决定 神经元），其被AD病理学选择性地靶向。除了这些相互竞争的假设之外， 这两个因素联合收割机：3）病理是由网络传播，但其本地和传播 参数由某些细胞类型（例如小胶质细胞）介导。不幸的是，AD研究迄今为止还无法 以充分测试这些假设之间的关系，或确定这些漏洞中哪些是密切相关的。许多可用的 实验室、动物或人体数据是描述性的，不适用于定量模型。 我们建议开发SV-C和SV-N的网络模型和正式的统计模型来测试它们。我们 利用并构建我们实验室最近推出的两项技术：矩阵反演 和子集选择（MISS）算法，用于创建全脑细胞类型图;和网络扩散模型 (NDM)其在数学上概括了tau蛋白沿沿着纤维突起的传递。进行了进一步地完善和发展 在这些使能技术中，我们将在小鼠tau蛋白病数据和人类tau蛋白病数据中探索SV-C和SV-N， 淀粉样蛋白PET扫描。我们还将开发和测试细胞或基因介导网络脆弱性的模型 间接地。如果成功，该项目可能导致支持或反对每个已识别SV的确凿证据 假设我们将在未来的工作中探索的形态学，分子或电生理特性， 入围的细胞、基因、神经通路和网络中心。我们的方法可以成为一种计算方法， 用于未来假设生成和测试的测试床，而不需要昂贵和费力的实验。 拟议的平台技术（MISS和NexIS）可能在神经科学中具有更广泛的适用性。