Cell and Network Disruptions and Associated Pathogenenesis in Tauopathy and Down Syndrome

Tau 蛋白病和唐氏综合症的细胞和网络破坏及相关发病机制

• 批准号: 10599756
• 负责人: STEVEN M FINKBEINER
• 金额: $ 37.8万
• 依托单位: J. DAVID GLADSTONE INSTITUTES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10599756
• 关键词: 3-Dimensional; Acute; Affect; Alzheimer' s Disease; Alzheimer' s disease model; Animal Model; Animals; Artificial Intelligence; Biological Markers; Biology; Biosensor; Brain; Cell Death; Cells; Cellular Morphology; Cessation of life; Communities; Computer Vision Systems; Data; Data Analyses; Data Set; Development; Down Syndrome; Ethical Analysis; Ethics; Frontotemporal Dementia; Funding; Grant; Human; Image; Image Analysis; Imaging technology; Individual; Induced pluripotent stem cell derived neurons; Label; Learning; Link; Machine Learning; Mathematics; Microscopy; Modeling; Monitor; Morphology; Nerve Degeneration; Neurodegenerative Disorders; Neurons; Nuclear; Organoids; Outcome; Outcome Study; Parents; Pathogenesis; Patients; Population Heterogeneity; Process; Property; Proteins; Reporting; Research; Research Personnel; Resistance; Signal Transduction; Standardization; Structure; Supervision; Tauopathies; Technology; Time; Training; United States National Institutes of Health; Visual; Zebrafish; algorithm training; base; cell type; convolutional neural network; deep learning algorithm; deep neural network; experimental study; human imaging; human model; imaging modality; imaging study; improved; in vivo; innovation; microscopic imaging; neural network; neural network algorithm; neuron loss; novel; novel strategies; robotic microscopy; serial imaging; tau Proteins; tool; unethical

PROJECT SUMMARY Our studies will develop and implement novel artificial intelligence (AI)/ machine learning (ML) technologies to reduce experimental unethical bias in analysis of imaging data of our studies in our parent, NIH-funded grant (AG064579-02) that focuses on identifying mechanisms of neurodegeneration in Alzheimer’s disease and Frontotemporal dementia. We study neurodegeneration in human iPSC-derived neurons (i-neurons) of controls and patients with tauopathies in 3-dimensional (3D) human brain organoids and use robotic microscopy (RM) to monitor changes in morphology and structure of individual i-neurons in large populations of heterogeneous cells over time as an indicator of neurodegeneration. Since the initiation of this grant, we have developed novel approaches to study mechanisms of neurodegeneration with a unique biosensor (Genetically encoded cell death indicator – GEDI) that acutely identifies living neurons at a stage at which they are irreversibly committed to die. Initially, imaging data from these studies involved human curation, which carries some degree of experimental bias that can cause ethical problems in interpretation of data. To reduce experimental bias of our data analysis, we have developed ML and deep neural networks (DNN) and use a subclass of DNN, convolutional neural networks (CNNs) which have mathematical properties particularly adept at Computer Vision. We have developed deep learning (DL) algorithms for detecting neuronal death by constructing a novel quantitative RM pipeline that automatically generates GEDI-curated data to train a CNN without human input. The resulting GEDI-CNN detects neuronal death from images of morphology alone, alleviating the need for any additional use of GEDI in subsequent experiments. Through systematic analysis of a trained GEDI-CNN, we find that it learns to detect death in neurons by locating morphology linked to death, despite receiving no explicit supervision toward these features. Uniquely, it detects cell death as a change in nuclear readouts as well as other cellular features, which human curation can’t easily identify. We also show that this model generalizes to images captured with different parameters or displays of neurons and cell types from different species without additional training. The advances we made in unbiased AI image analysis are not restricted to our benefit but will be applicable to a large range of ML based imaging studies of other investigators because it focuses on improving how the CNN algorithms are trained to analyze data without the need of humans but with super-human accuracy. In this supplemental application, we will further develop this novel ethical AI technology for studies on neurodegeneration of human i-neurons in 3D brain organoids using GEDI-CNN. We will refine the CNN algorithms to optimize and standardize its widespread use in ethical analysis of live imaging analysis and provide the technology to the scientific community for AI-based imaging research.

项目摘要 我们的研究将开发和实施新的人工智能（AI）/机器学习（ML）技术， 在我们的母公司，NIH资助的赠款中，减少我们研究的成像数据分析中的实验不道德偏见 （AG 064579 -02），其专注于识别阿尔茨海默病中神经变性的机制， 额颞叶痴呆。我们研究了对照组的人iPSC衍生的神经元（i-神经元）中的神经变性。 和患有3维（3D）人脑类器官中的tau蛋白病的患者，并使用机器人显微镜（RM） 监测大量异质细胞中单个i神经元的形态和结构变化 作为神经退化的指标。自从获得这笔赠款以来，我们开发了新的 用独特的生物传感器（遗传编码的细胞死亡）研究神经变性机制的方法 指示器- GEDI），其在活神经元不可逆地致力于死亡的阶段敏锐地识别活神经元。 最初，来自这些研究的成像数据涉及人类策展，这带有一定程度的实验性。 在解释数据时可能导致伦理问题的偏见。为了减少我们数据分析的实验偏差， 我们已经开发了ML和深度神经网络（DNN），并使用DNN的子类卷积神经网络， 神经网络（CNN）具有数学特性，特别适合计算机视觉。我们已经开发 深度学习（DL）算法，用于通过构建一种新的定量RM管道来检测神经元死亡， 自动生成GEDI策划的数据来训练CNN，而无需人工输入。GEDI-CNN 仅从形态学图像中检测神经元死亡，减轻了对任何额外使用GEDI的需要。 随后的实验。通过对经过训练的GEDI-CNN的系统分析，我们发现它学会了检测 通过定位与死亡相关的形态学来研究神经元的死亡，尽管没有对这些形态学进行明确的监督。 功能.独特的是，它检测细胞死亡作为核读数的变化以及其他细胞特征， 人类策展不容易识别。我们还表明，该模型推广到不同的捕获图像 参数或显示来自不同物种的神经元和细胞类型，而无需额外的训练。的进展 我们在无偏见的AI图像分析中所做的并不局限于我们的利益，而是将适用于大范围的 其他研究人员的基于ML的成像研究，因为它专注于改进CNN算法 训练他们分析数据，不需要人类，但具有超人的准确性。本补充 应用，我们将进一步发展这种新的伦理AI技术，用于人类神经退行性疾病的研究。 使用GEDI-CNN的3D脑类器官中的i-神经元。我们将完善CNN算法，以优化和标准化 其广泛应用于伦理分析的现场成像分析，为科学的技术提供了 基于AI的成像研究社区。