Novel artificial intelligence-based approaches to understand the pathological and genetic drivers of primary tauopathies

基于人工智能的新方法来了解原发性 tau 蛋白病的病理和遗传驱动因素

• 批准号: 10701779
• 负责人: Kurt William Farrell
• 金额: $ 12.6万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10701779
• 关键词: Address; Aging; Algorithms; Alzheimer' s Disease; Amyloid; Amyloid beta-Protein; Amyloidosis; Apoptosis; Architecture; Area; Artificial Intelligence; Autopsy; Back; Basic Science; Biomedical Engineering; Brain; Categories; Cells; Chromosome 4; Clinical; Cognitive; Computer Vision Systems; Computing Methodologies; Data; Dementia; Diagnosis; Disease; Disease Progression; Elderly; Exhibits; Genes; Genetic; Genetic Markers; Genomics; Goals; Grant; Human; Impaired cognition; Impairment; Individual; Knowledge; Machine Learning; Measurement; Medial; Mediating; Mentors; Methods; Microtubules; Modeling; Molecular; Morphology; Nerve Degeneration; Neurofibrillary Tangles; Neuroglia; Neurons; Pathogenesis; Pathologic; Pathology; Patients; Pattern; Pick Disease of the Brain; Population; Positioning Attribute; Post-Translational Protein Processing; Proteins; Research; Research Personnel; Scientist; Semantics; Senile Plaques; Staging; Stains; Tauopathies; Techniques; Technology; Temporal Lobe; Testing; Tissue Banks; Tissue Sample; Tissues; Training; Transcription Alteration; Up-Regulation; Work; abeta deposition; age related; aged; beta amyloid pathology; cell type; data integration; differential expression; experience; genetic association; genome wide association study; genome-wide; innovation; insight; interest; mRNA Expression; machine learning classifier; machine learning model; mild cognitive impairment; molecular marker; neural network; neuropathology; neuroprotection; normal aging; novel; novel marker; pathology imaging; phenotypic data; proteostasis; risk variant; single cell analysis; single cell sequencing; single-cell RNA sequencing; supervised learning; tau Proteins; tau-1; trait; transcriptome; transcriptome sequencing; transcriptomics; vector; whole slide imaging

ABSTRACT Tau normally regulates microtubules in neurons and glia, however during diseases pathogenesis, several post- translational modifications cause hyperphosphorylation of this protein which consequently is toxic to the cell. Primary age-related tauopathy (PART), a common pathology associated with human aging is estimated to effect 1-7 % of the of the population and patients with the disorder can be cognitively normal or exhibit a range of symptomology including mild cognitive impairment or dementia. Neuropathologically, those with PART have var- ying degrees neurofibrillary tangles in the medial temporal lobe, and an absence of amyloid plaques throughout the brain. Our goal is to deploy three independent approaches to understand how PART has convergent and divergent features from other primary and secondary tauopathies. The objective is to use novel high-throughput genetic and transcriptomic technologies combined with innovative computational methods including computer vision and AI to better characterize drivers of tau phosphorylation in PART. Our hypothesis is that machine learning classifiers (supervised and unsupervised) combined with single cell analysis will be able to accurately identify and quantify transcriptomic, genomic, clinical, and morphological features in PART to further understand the underlying amyloid independent mechanisms of tauopathy. Our rationale is that understanding the genetic transcriptomic and clinical architecture of PART will assistant in understand disease staging, diagnosis, and progression. We plan to test our hypothesis by pursing the following significant aims: (1) Quantify neurofibrillary tangle burden using supervised machine learning models and integrate this data in genetic and clinicopatholog- ical association studies (2) Model the sequential progression of neurofibrillary tangle degeneration in PART with unsupervised deep generative approaches. (3) Identify transcriptional alterations associated with neurofibrillary tangles in PART using single cell RNA sequencing. The proposed research is innovative as it applies novel transcriptomic and machine learning techniques to identify in an understudied group of elderly subjects with tauopathy lacking amyloidosis. This proposed research is significant as it addresses a critical unmet need to develop algorithms which can assist neuropathologists in their post-mortem diagnosis and provide better quan- titative phenotypic data which can aid in facilitating better neuroprotective strategies. The proposal builds upon the candidate's established interest in age-related tauopathy and his prior training in biomedical engineering and translational basic science research. The candidate’s primary mentor, Dr. John Crary, is an experienced neuro- pathologist and tau neuroscientist and will be supplemented by mentoring team consisting of Dr. Bin Zhang with specific expertise in computational genetics and transcriptomics and Dr. Thomas Fuchs, a prominent scientist in the field of computational pathology with a specific expertise in machine learning classifiers, AI, and computer vision. They will assure that the proposed research and training prepare the applicant to be an independent investigator in experimental computational neuropathology.

摘要 Tau通常调节神经元和神经胶质中的微管，但在疾病发病过程中，一些后- 翻译修饰引起该蛋白质的过度磷酸化，从而对细胞有毒。 原发性年龄相关性tau蛋白病（PART），一种与人类衰老相关的常见病理学，据估计影响 1-7%的人群和患有该病症的患者可以是认知正常的或表现出一系列的认知障碍。 包括轻度认知障碍或痴呆症在内的神经病学。神经病理学上，那些与部分有不同的- 内侧颞叶神经原纤维缠结程度较轻，且整个区域无淀粉样斑块 大脑我们的目标是部署三种独立的方法来了解PART如何收敛和 与其他原发性和继发性tau蛋白病不同的特征。目的是使用新的高通量 遗传学和转录组学技术结合创新的计算方法，包括计算机 视觉和AI来更好地表征PART中tau磷酸化的驱动因素。我们的假设是这台机器 结合单细胞分析的学习分类器（监督和无监督）将能够准确地 识别和量化PART中的转录组学、基因组学、临床和形态学特征，以进一步了解 tau蛋白病的潜在淀粉样蛋白非依赖性机制。我们的理论基础是， PART的转录组学和临床结构将有助于理解疾病分期、诊断和 进展我们计划通过以下几个方面来验证我们的假设：（1）量化神经元的数量 使用有监督机器学习模型来处理负担，并将这些数据整合到遗传学和临床病理学中， 临床关联研究（2）模拟PART中神经元缠结变性的顺序进展， 无监督的深度生成方法。(3)识别与神经系统疾病相关的转录改变 使用单细胞RNA测序在PART中缠结。这项研究是创新的，因为它适用于新的 转录组学和机器学习技术，以确定在一个未充分研究的老年受试者组， 缺乏淀粉样变性的tau蛋白病。这项拟议的研究意义重大，因为它解决了一个关键的未满足的需求， 开发算法，可以帮助神经病理学家在他们的死后诊断，并提供更好的量化， 可以帮助促进更好的神经保护策略的定量表型数据。该提案建立在 候选人对年龄相关的tau蛋白病的既定兴趣以及他之前在生物医学工程方面的培训， 转化型基础科学研究候选人的主要导师约翰克拉里博士是一位经验丰富的神经科医生， 病理学家和tau神经科学家，并将由张斌博士组成的指导团队补充， 在计算遗传学和转录组学的专业知识和托马斯富克斯博士，一个著名的科学家， 计算病理学领域，在机器学习分类器，人工智能和计算机方面具有特定的专业知识 视野他们将确保拟议的研究和培训准备申请人是一个独立的 实验计算神经病理学研究者。