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）是一种异质的多因素疾病，有选择地影响 大脑，例如基因座，内嗅和海马。这种选择性脆弱性（SV）的因素 尚不清楚：为什么进步如此刻板印象？为什么在早期的特定结构中看到病理学？ 某些细胞使它们容易受到AD的影响？当前的假设集中于特定特征， 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，脆弱性（“ SV-C”）已获得货币。 另一个脆弱性涉及基于网络的跨神经元“类似prion”病理的传播 某些电路，纤维路径和区域（网络中心）可能会有选择地脆弱（“ SV-N”）。 该建议将定量检验并验证有关SV-C和SV-N的假设：1）蛋白质聚集， 在病理的时空进展下，网络上的清除和传播；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。 另外，2）SV取决于某些神经细胞类型的分布和组成（例如大型金字塔 神经元）由AD病理选择性靶向。超越这些假设是可能性 这两个因素结合在一起：3）病理受网络传输的控制，但其本地和传播 参数是由某些细胞类型（例如小胶质细胞）介导的。不幸的是，到目前为止，广告研究一直无法 在这些假设之间进行充分检验或确定这些漏洞中的哪些是隐密的。大部分可用 台，动物或人类数据具有描述性，不适合定量模型。 我们建议为SV-C和SV-N以及正式统计模型开发网络模型来测试它们。 大写并建立在最近从我们的实验室出现的两种有利技术：矩阵倒置 以及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）在数学上概括了沿纤维项目的TAU传输。随着进一步的发展 在这些有利的技术中，我们将在小鼠tauopathy数据中探索SV-C和SV-N，以及人类tau-和 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。如果成功，该项目可能会导致对已确定的SV或针对每个已确定的SV的结论性证据 假设。我们将在未来的工作中探索形态，分子或电生理特性 入围细胞，基因，神经途径和网络震中。我们的方法可能成为计算 测试未来假设产生和测试的床，而无需进行昂贵的实验室实验。 拟议的平台技术（MISS和NEXIS）可能在神经科学中更广泛地适用。