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.

项目概要 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。 大脑，例如蓝斑、内嗅和海马。这种选择性脆弱性 (SV) 的潜在因素 仍不清楚：为什么进展如此刻板？为什么早期阶段会在特定结构中看到病理？ 某些细胞为何容易患 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。蜂窝的概念 由于单细胞测序和空间转录组学的进步，脆弱性（“SV-C”）已获得流行。 另一个漏洞与基于网络的跨神经元“类朊病毒”病理传播有关，这是由于 某些电路、光纤路径和区域（网络集线器）可能会选择性地变得脆弱（“SV-N”）。 该提案将定量测试和验证有关 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时空进展的基础；因此 SV 为 某些区域（例如 EC 和 Hipp）可能只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 AD 病理选择性靶向的神经元）。除了这些相互竞争的假设之外，还有可能性 这两个因素结合在一起：3）病理学受网络传播控制，但其局部性和传播性 参数由某些细胞类型（例如小胶质细胞）介导。不幸的是，迄今为止AD研究还无法 充分测试这些假设或确定这些漏洞中哪些是密切相关的。大部分可用 工作台、动物或人类数据是描述性的，不适合定量模型。 我们建议开发 SV-C 和 SV-N 的网络模型以及正式的统计模型来测试它们。我们 利用我们实验室最近推出的两项支持技术并以此为基础：矩阵求逆 用于创建全脑细胞类型图的子集选择（MISS）算法；和网络扩散模型 (NDM)，它在数学上概括了 tau 蛋白沿纤维投影的传输。随着进一步发展 在这些使能技术中，我们将探索小鼠 tau 蛋白病数据和人类 tau 蛋白病数据中的 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，该项目可能会得出支持或反对每个已确定的 SV 的决定性证据 假设。我们将在未来的工作中探索形态学、分子或电生理学特性 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 未来假设生成和测试的测试平台，无需昂贵且费力的实验。 所提出的平台技术（MISS 和 NexIS）可能在神经科学领域具有更广泛的适用性。