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; 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颖的人工智能（AI）/机器学习（ML）技术 在父母的研究中，减少实验性不道德偏见，分析我们的研究的成像数据，NIH资助的赠款 （AG064579-02）专注于识别阿尔茨海默氏病和 额颞痴呆。我们研究对照人IPSC衍生的神经元（I-神经元）的神经变性 三维（3D）人脑器官中有扭曲的患者，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的变化 随着时间的流逝，作为神经变性的指标。自从这笔赠款的倡议以来，我们已经开发了小说 使用独特的生物传感器研究神经退行性的机制（遗传编码的细胞死亡） 指标 - Gedi）在不可逆地致力于死亡的阶段急性地识别活神经元。 最初，来自这些研究的成像数据涉及人类策划，该研究具有一定程度的实验 可能在数据解释时引起道德问题的偏见。为了减少我们数据分析的实验偏见， 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 具有数学属性的网络（CNN）尤其熟悉计算机视觉。我们已经发展了 通过构建一种新型的定量RM管道来检测神经元死亡的深度学习（DL）算法 自动生成GEDI策划的数据，以训练无人输入的CNN。由此产生的Gedi-CNN 从单独形态的图像中检测神经元死亡，从而减轻了GEDI在 随后的实验。通过对受过训练的Gedi-CNN的系统分析，我们发现它学会了检测 通过找到与死亡有关的形态，目的地没有明确监督的神经元死亡 独特地，它检测到细胞死亡是核读数的变化以及其他细胞特征， 人类策划无法轻易识别。我们还表明，该模型概括为用不同的图像 来自不同物种的神经元和细胞类型的参数或显示，而无需其他训练。进步 我们以公正的AI图像分析制作不限于我们的利益，但将适用于大量 基于ML的其他研究者的成像研究，因为它重点是改善CNN算法的方式 经过培训，可以分析数据而无需人类，但具有超人的准确性。在这个补充中 应用，我们将进一步开发这种新型的道德AI技术来研究人类神经退行性的研究 使用GEDI-CNN中的3D脑器官中的I-神经元。我们将完善CNN算法以优化和标准化 它在实时成像分析的道德分析中的广泛使用，并为科学提供技术 基于AI的成像研究的社区。