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.

抽象的 Tau 通常调节神经元和神经胶质细胞中的微管，但在疾病发病机制中，一些后 翻译修饰导致该蛋白质过度磷酸化，从而对细胞有毒。 原发性年龄相关性 tau 蛋白病 (PART) 是一种与人类衰老相关的常见病理，估计会影响 1-7% 的人群和患有该疾病的患者可能认知正常或表现出一系列症状 症状包括轻度认知障碍或痴呆。从神经病理学角度来看，患有 PART 的患者患有多种症状。 内侧颞叶有一定程度的神经原纤维缠结，并且整个区域没有淀粉样斑块 我们的目标是部署三种独立的方法来理解 PART 如何收敛和收敛。 与其他原发性和继发性 tau蛋白病不同的特征，目的是使用新颖的高通量。 遗传和转录组技术与包括计算机在内的创新计算方法相结合 视觉和人工智能可以更好地表征 PART 中 tau 磷酸化的驱动因素。 学习分类器（有监督和无监督）与单细胞分析相结合将能够准确地 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 我们的基本原理是了解 tau 蛋白病的潜在淀粉样蛋白独立机制。 PART 的转录组学和临床架构将有助于了解疾病分期、诊断和 我们计划通过追求以下重要目标来检验我们的假设进展：（1）量化神经原纤维 使用机器学习模型来处理缠结负担，并将这些监督数据整合到遗传和临床病理学中- 临床关联研究 (2) 模拟 PART 中神经原纤维缠结变性的顺序进展 (3) 识别与神经原纤维相关的转录改变 所提出的研究具有创新性，因为它应用了新颖的方法。 转录组学和机器学习技术可在一组未充分研究的老年受试者中识别出 这项拟议的研究具有重要意义，因为它解决了未满足的关键需求。 开发可以协助神经病理学家进行尸检诊断并提供更好的定量的算法 该提案建立在有助于促进更好的神经保护策略的基础上的表型数据。 候选人对与年龄相关的 tau 蛋白病已建立的兴趣以及他之前接受过的生物医学工程培训和 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学家。 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 Thomas Fuchs 博士是计算遗传学和转录组学领域的杰出科学家 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 他们将确保拟议的研究和培训使申请人成为独立的人。 实验计算神经病理学研究员。