Antisense Oligonucleotides targeting APP to prevent neurodegeneration in models of Down Syndrome and Alzheimer's disease

靶向 APP 的反义寡核苷酸可预防唐氏综合症和阿尔茨海默病模型中的神经变性

• 批准号: 10543710
• 负责人: William C Mobley
• 金额: $ 27.03万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-15 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10543710
• 关键词: 3-Dimensional; Address; Alzheimer' s Disease; Antisense Oligonucleotides; Biological Models; Brain; COVID-19 pandemic; Calibration; Carbon Dioxide; Cell Count; Cells; Collaborations; Collection; Communities; Computer Vision Systems; Computer software; Couples; Data; Data Collection; Detection; Down Syndrome; Ensure; Equipment; Evaluation; Feedback; Future; Goals; Grant; Hand; Home; Human; Image; Improve Access; Investigation; Laboratories; Machine Learning; Manuals; Measures; Methods; Microscope; Microscopic; Microscopy; Modeling; Nerve Degeneration; Neurodegenerative Disorders; Neurons; Parents; Phenotype; Positioning Attribute; Protocols documentation; Quality Control; Reproducibility; Research; Research Personnel; Running; Sampling; Site; Speed; System; Thick; Time; Tissues; Vendor; Visual; Vulnerable Populations; Work; base; cost; data acquisition; experimental study; graphical user interface; improved; innovation; interest; microscopic imaging; novel; open source; parent grant; prevent; programs; sensor; software systems; system architecture

Project Summary/Abstract: Critical to ensuring rigor in investigation of neurodegenerative disorders is the use of methods by which to accurately, and in an unbiased way to, assess degenerative phenotypes, including the number of neurons in vulnerable populations. Unbiased stereological assessment is the standard in the field. However, these studies are technically demanding and are costly in terms of the equipment required to conduct them. Thus, limitation of access to existing, specialized and prohibitively expensive systems has been a hindrance to progress, especially during the Covid-19 pandemic. To overcome these limitations we propose to develop a novel scalable open-source stereological software system with drastically reduced system requirements and at the fraction of the cost of commercial stereology systems. The proposed system will be capable of running on standard windows systems and standard microscopy hardware that is widely available in laboratories. Increasing the availability of microscope systems capable of stereological analysis promises to dramatically improve access to stereological data collection, speed analyses and enhance rigor as well as transparency. As such, our goal is to achieve stereological protocol requirements with a standard lab microscope without the need for expensive specialized hardware. We will acquire statistically equivalent stereological data using innovative analytical components using well-established computer vision methods like “visual odometry” and “depth from focus” that will make use of camera images in combination with open-loop stage positioning data to replace the functionality of expensive feedback-based high precision 3D positioning sensors. An additional benefit to developing an open-source system architecture that de-couples hardware from software protocols is that it will more naturally allow integration of AI/machine learning based components to automate and speed manual stereology tasks like feature/cell detection and counting. To demonstrate reproducibility and statistical equivalence with currently in use commercial systems, we will use samples previously analyzed using existing systems. Future directions would include automated cell detection to improve the reproducibility and effeciency of performing stereology experiments.

项目总结/摘要：确保神经退行性疾病研究的严谨性至关重要 疾病是使用准确的方法，并以公正的方式，评估 退化表型，包括脆弱人群中的神经元数量。无偏 体视学评估是该领域的标准。这些研究在技术上 要求很高，并且就进行它们所需的设备而言是昂贵的。因此，限制 获得现有的、专门的和昂贵得令人望而却步的系统一直是一个障碍， 尤其是在2019冠状病毒病疫情期间。为了克服这些局限性，我们建议 开发一种新的可扩展的开源体视学软件系统， 系统要求，并且成本仅为商业体视学系统的一小部分。的 拟议的系统将能够在标准视窗系统和标准 在实验室中广泛使用的显微镜硬件。更多地提供 能够进行体视学分析的显微镜系统有望极大地改善 这将有助于立体数据收集、加快分析速度、提高严谨性和透明度。作为 因此，我们的目标是用标准实验室显微镜达到体视学协议要求 而不需要昂贵的专用硬件。 我们将获得统计等效体视学数据使用创新的分析组件 使用成熟的计算机视觉方法，如“视觉里程计”和“焦点深度”， 将利用摄像机图像结合开环载物台定位数据， 昂贵的基于反馈的高精度3D定位传感器的功能。一个 开发一个开源系统架构的额外好处， 它将更自然地允许基于AI/机器学习的集成 自动化和加速手动体视学任务（如特征/细胞检测）的组件， 数数证明与当前使用的产品的重现性和统计学等效性 商业系统，我们将使用以前使用现有系统分析的样品。未来 方向将包括自动化细胞检测，以提高重复性和效率， 进行体视学实验