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.

项目总结 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，以 在我们母公司NIH资助的基金中减少对我们研究的成像数据分析的实验不道德偏见 (AG064579-02)，其重点是确定阿尔茨海默病和 额颞部痴呆。我们研究了对照组人IPSC来源的神经元(I-神经元)的神经变性 以及患有三维(3D)人脑器官病变的患者，并使用机器人显微镜(RM)来 监测大量异质细胞中单个I神经元的形态和结构的变化 随着时间的推移作为神经退化的指标。自从这笔赠款启动以来，我们开发了一部小说 用一种独特的生物传感器(遗传编码的细胞死亡)研究神经变性机制的方法 指示器-GEDI)，它敏锐地识别活着的神经元处于它们不可逆转地致力于死亡的阶段。 最初，来自这些研究的成像数据涉及人类的管理，这带有一定程度的实验 在数据解释中可能导致道德问题的偏差。为了减少我们数据分析的实验偏差， 我们开发了ML和深度神经网络(DNN)，并使用了DNN的一个子类-卷积神经网络 网络(CNN)具有特别擅长计算机视觉的数学特性。我们已经开发出 通过构建一种新的定量RM流水线来检测神经元死亡的深度学习(DL)算法 自动生成GEDI管理的数据来训练CNN，而不需要人工输入。由此产生的GEDI-CNN 仅从形态图像检测神经元死亡，减少了对GEDI的任何额外使用的需要 随后的实验。通过对一个训练有素的GEDI-CNN进行系统分析，我们发现它学习检测 通过定位与死亡相关的形态在神经元中死亡，尽管没有得到对这些的明确监督 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，这是 人类的策划者不容易识别。我们还表明，该模型可以推广到不同类型的图像 来自不同物种的神经元和细胞类型的参数或显示，而无需额外的训练。最新进展 我们所做的不偏不倚的人工智能图像分析不仅对我们有利，而且将适用于广泛的 基于ML的其他研究人员的成像研究，因为它专注于改进CNN算法的方式 经过训练，可以在不需要人类的情况下分析数据，但具有超乎人类的准确性。在本补充资料中 应用，我们将进一步开发这一新的伦理学人工智能技术，用于研究人类神经退行性变 GEDI-CNN在3D脑有机体中的I-神经元。我们将完善CNN算法，以优化和标准化 它广泛应用于伦理学分析中的实时成像分析，并为科学研究提供技术支持 基于人工智能的成像研究社区。