Next generation machine vision for automated behavioral phenotyping of knock-in ALS-FTD mouse models

项目摘要 肌萎缩侧索硬化症（ALS）和额颞叶痴呆（FTD）是一种毁灭性的神经退行性疾病， 存在于遗传和机械连续体上的疾病。ALS是一种运动神经元疾病， 在诊断后的3 - 5年内几乎一致致命。FTD是一种异质的，快速发展的 这是导致老年痴呆症的三大原因之一。 大约10%的ALS病例是由 显性遗传的基因缺陷SOD1和FUS突变导致侵袭性运动神经元病理学 而TDP43突变导致ALS-FTD。此外，野生型FUS和TDP43是异常FUS的组分。 在许多FTD病例中发现了包涵体，表明这些疾病之间存在机械联系。早期表型是 特别令人感兴趣的是，因为这些可能导致针对疾病根源的有针对性的干预措施 可以阻止目前不可阻挡的疾病发展。阐明这种早期的，潜在的共享 这些疾病的特征应大大有助于：1）基因敲入动物模型，表达家族性 ALS-FTD基因; 2）灵敏、严谨、客观的行为表型分析方法， 在不同的实验室中生成的模型。在已发表的工作中，联合PI应用了他们的第一代机器， 基于视觉的自动化表型分析方法，ACBM "1.0"（自动化连续行为监测）， 检测和定量Tdp43Q331K基因敲入小鼠中最早观察到的表型。该方法需要 连续视频记录5天，生成> 14百万帧/鼠标。然后这些视频由 一个训练有素的计算机视觉系统除了其灵敏性、客观性和可重复性外， 这种方法的一个重要特点是能够获取和存档视频记录，并在现场分析数据，包括 云，远离那些收购。我们将使用Google Cloud TPU超级计算机， 全新设计，旨在加速尖端机器学习工作负载，特别关注 深度学习我们将使用贝叶斯分层样条模型来分析这些数据， 小鼠行为沿着昼夜节律。目前的提案有两个主要目标：1）使用深度学习 完善和应用下一代ACBM-“2.0”-这将允许更敏感，更广泛和更强大的 沿着四种新的敲入模型以及良好表征的SOD 1G93 A的自动行为表型分析 转基因小鼠2)建立和验证能够远程获取录像的程序 基于云的数据分析。我们的愿景是建立敏感、稳健、客观和开源的 基于机器视觉的行为分析工具，将广泛提供给该领域的研究人员。由于所有 计算机注释的视频数据在ACBM 2.0中被标准化，并将被存档，我们设想 可搜索的“行为数据库”，可以自由挖掘和分析。这些工具对于加快 ALS-FTD的新型有效治疗药物的开发。

Project Summary Amyotrophic lateral sclerosis (ALS) and Frontotemporal Dementia FTD are devastating neurodegenerative disorders that lie on a genetic and mechanistic continuum. ALS is a disease of motor neurons that that is almost uniformly lethal within only 3-5 years of diagnosis. FTD is a heterogeneous, rapidly progressing syndrome that is among the top three causes of presenile dementia. About 10% of ALS cases are caused by dominantly transmitted gene defects. SOD1 and FUS mutations cause aggressive motor neuron pathology while TDP43 mutations cause ALS-FTD. Further, wild type FUS and TDP43 are components of abnormal inclusions in many FTD cases, suggesting a mechanistic link between these disorders. Early phenotypes are of particular interest because these could lead to targeted interventions aimed at the root cause of the disorder that could stem the currently inexorable disease progression. Elucidating such early, potentially shared characteristics of these disorders should be greatly aided by: 1) knock-in animal models expressing familial ALS-FTD genes; 2) sensitive, rigorous and objective behavioral phenotyping methods to analyze and compare models generated in different laboratories. In published work the co-PIs applied their first-generation, machine vision-based automated phenotyping method, ACBM ‘1.0’ (automated continuous behavioral monitoring) to detect and quantify the earliest-observed phenotypes in Tdp43Q331K knock-in mice. This method entails continuous video recording for 5 days to generate >14 million frames/mouse. These videos are then scored by a trained computer vision system. In addition to its sensitivity, objectivity and reproducibility, a major advantage of this method is the ability to acquire and archive video recordings and to analyze the data at sites, including the Cloud, remote from those of acquisition. We will use Google Cloud TPUs supercomputers that have been designed from the ground up to accelerate cutting-edge machine learning workloads, with a special focus on deep learning. We will analyze this data using Bayesian hierarchical spline models that describe the different mouse behaviors along the circadian rhythm. The current proposal has two main goals: 1) Use deep learning to refine and apply a Next Generation ACBM - ‘2.0’ - that will allow for more sensitive, expansive and robust automated behavioral phenotyping of four novel knock-in models along with the well characterized SOD1G93A transgenic mouse. 2) To establish and validate procedures to enable remote acquisition of video recording data with cloud-based analysis. Our vision is to establish sensitive, robust, objective, and open-source machine vision-based behavioral analysis tools that will be widely available to researchers in the field. Since all the computer-annotated video data is standardized in ACBM 2.0 and will be archived, we envision a searchable ‘behavioral database’, that can be freely mined and analyzed. Such tools are critical to accelerate the development of novel and effective therapeutics for ALS-FTD.