AI based system for longitudinal, repeated measure analyses of freely moving C. elegans worms

基于人工智能的系统，用于对自由移动的秀丽隐杆线虫进行纵向、重复测量分析

• 批准号: 10258638
• 负责人: JACOB R GLASER
• 金额: $ 45万
• 依托单位: MICROBRIGHTFIELD, LLC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-09 至 2022-07-08
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10258638
• 关键词: Acetylcholine; Address; Agar; Aging; Alzheimer' s Disease; Animal Behavior; Animal Model; Appearance; Artificial Intelligence; Behavior; Behavioral; Behavioral Research; Biological Assay; Biotechnology; Brain; Brain Diseases; Caenorhabditis elegans; Classification; Collaborations; Complex; Computer software; Data; Development; Disease; Dopamine; Eukaryota; Feasibility Studies; Glutamates; Goals; Human; Image; Individual; Laboratories; Legal patent; Lighting; Locomotion; Longevity; Longitudinal Studies; Market Research; Massachusetts; Measures; Microscope; Molecular; Motion; Mus; Names; National Institute of Mental Health; Nematoda; Neurodegenerative Disorders; Neurodevelopmental Disorder; Neurosciences; Neurotransmitters; Pathologic; Pharmacologic Substance; Phase; Population Analysis; Preparation; Psyche structure; Rattus; Research; Research Design; Research Personnel; Rodent; Schizophrenia; Schools; Serotonin; Speed; Strategic Planning; System; Technology; Testing; Time; Toxicology; Traumatic Brain Injury; Validation; Visual Fields; Work; analytical method; autism spectrum disorder; base; behavioral study; convolutional neural network; design; digital; drug discovery; fighting; free behavior; gamma-Aminobutyric Acid; high throughput screening; innovation; intelligent algorithm; longitudinal analysis; neural network architecture; neurodevelopment; neuropsychiatric disorder; neurotransmitter release; next generation; novel; novel therapeutics; prevent; prototype; receptor; social; usability

Abstract This project aims to develop WormInvestigator™, a novel, highly innovative system for performing automated, high-throughput and longitudinal studies of the behavior of C. elegans worms freely moving and socially interacting on agar plates (hereafter: "freely moving worms") across multiple time points over extended times (e.g., multiple days) with repeated measures designs. Work in Phase I will focus on demonstrating feasibility of our novel, patent pending, WormRecognizer™ technology – the ability to perform automatic, image-based identification of individual C. elegans worms within a group of freely moving worms ("digital tagging of freely moving worms"). Work in Phase II will focus on creating the full functionality of WormInvestigator for the commercial release. The innovation inherent in WormRecognizer will serve as the basis for enabling a game- changing innovation in the field – the ability to perform high throughput longitudinal, repeated measures design analyses of locomotion and other behavior of freely moving C. elegans worms from discrete, non-continuous video sequences. Compared to study designs that have independent groups repeated measures designs offer more statistical power and the possibility to track an effect over time. Specifically, repeated measures designs for analyzing locomotion and other behavior of freely moving worms will allow researchers to definitively assess the likelihood that a particular behavior is associated with a prior behavior, which is impossible without repeated measures designs or impractical continuous imaging and tracking under constant illumination. WormRecognizer will leverage the Deep Convolutional Neural Network (CNN) architecture to perform automatic identification of the tracks of the same worm in videos of groups of freely moving worms recorded at different time points; encouraging pilot data were generated during preparation of this application. C. elegans is increasingly used as a model organism in research focusing on brain mechanisms underlying complex behaviors and pathological alterations thereof, including research into neurodevelopment, Alzheimer's disease, autism, schizophrenia and traumatic brain injury. Thus, WormInvestigator will enable significant advancements in various mental neuroscience applications that use C. elegans as a model organism. Specifically, the fact that C. elegans express many of the neurotransmitters and associated receptors that are found in higher eukaryotes, including humans, makes C. elegans highly attractive for the (high throughput) screening of next generation therapeutics for mental diseases such as Alzheimer's disease, as well as for disorders that rely on neurotransmitter release modulation such as next generation treatments for schizophrenia. We will perform extensive feasibility studies, product validation and usability studies of WormInvestigator in close collaboration with expert neuroscientists. Market research performed during preparation of this application indicated that WormInvestigator will expand the use of C. elegans as a model organism to many laboratories that do not currently use them. A competing technology is not available. We anticipate the global market size for WormInvestigator to be more than 300 systems.

抽象的 该项目旨在开发 WormInvestigator™，这是一种新颖的、高度创新的系统，用于执行自动化、 对线虫自由移动和社交行为的高通量纵向研究 在琼脂平板上（以下简称“自由移动的蠕虫”）在多个时间点上长时间相互作用 （例如，多天）具有重复测量设计。第一阶段的工作重点是论证可行性 我们新颖的、正在申请专利的 WormRecognizer™ 技术 – 能够自动执行基于图像的操作 识别一组自由移动的蠕虫中的个体线虫（“自由移动的数字标记”） 移动蠕虫”）。第二阶段的工作将侧重于为蠕虫创建完整的功能 商业发布。 WormRecognizer 固有的创新将作为实现游戏的基础 改变该领域的创新——执行高通量纵向、重复测量设计的能力 从离散、非连续的角度分析自由移动的线虫的运动和其他行为 视频序列。与具有独立组的研究设计相比，重复测量设计提供 更强的统计能力以及随着时间的推移跟踪效果的可能性。具体来说，重复测量设计 用于分析自由移动蠕虫的运动和其他行为将使研究人员能够明确评估 特定行为与先前行为相关的可能性，如果不重复，这是不可能的 测量设计或在恒定照明下不切实际的连续成像和跟踪。蠕虫识别器 将利用深度卷积神经网络（CNN）架构来执行自动识别 在不同时间点记录的自由移动蠕虫群视频中同一蠕虫的踪迹； 在准备本应用程序期间产生了令人鼓舞的试点数据。线虫越来越多地被用作 研究重点是复杂行为和病理背后的大脑机制的模型生物体 其改变，包括对神经发育、阿尔茨海默病、自闭症、精神分裂症和 脑外伤。因此，WormInvestigator 将在各种心理方面取得显着进步。 使用线虫作为模式生物的神经科学应用。具体来说，秀丽隐杆线虫表达的事实 在包括人类在内的高等真核生物中发现的许多神经递质和相关受体， 使得线虫对于下一代精神疗法的（高通量）筛选极具吸引力 阿尔茨海默病等疾病，以及依赖神经递质释放调节的疾病 例如治疗精神分裂症的下一代疗法。我们将进行广泛的可行性研究，产品 与神经科学家专家密切合作，对 WormInvestigator 进行验证和可用性研究。市场 在准备本应用程序期间进行的研究表明，WormInvestigator 将扩大使用 许多实验室目前尚未使用线虫作为模式生物。一项竞争技术是 无法使用。我们预计 WormInvestigator 的全球市场规模将超过 300 个系统。