ABI: Innovation: Analyzing Neuroglial Cell Dynamics in their Natural Environment with Video Microscopy

脑细胞和神经细胞的显微镜视频为科学家提供了前所未有的机会，在这些细胞发育、相互作用和对损伤、疾病或环境中的其他变化做出反应时，他们可以接触到这些细胞。然而，这些视频经常包含数十个或数百个细胞，其行为方式复杂，人类观看者不易辨别。弗吉尼亚大学主持的这个项目正在创新基于计算机的处理方法，以挑选出这些细胞并自动分析它们的运动。通过简化这些任务，该软件将允许研究大量数据，以寻找有意义的模式和管理这些细胞行为的规则。前端界面和后端模块将支持将这些功能从单个视频扩展到庞大的数据库，仅受可用计算能力的限制。这些新能力将帮助科学家发现有关这些关键细胞的新见解，使研究人员更接近了解动物细胞行为的相似和差异，这些动物被用于研究影响数百万美国人的疾病和障碍。通过将生物学家和工程师聚集在一起，这个项目还为学生提供了令人兴奋的新体验，让他们了解这些领域未来的可能性。对高中教师进行图像处理及其应用方面的培训，为更多的学生提供了这种全面的体验。为了帮助神经科学家观察神经元和神经胶质细胞在其原始环境中的行为，该软件将在高分辨率显微镜视频中自动处理和分析数十或数百个细胞之间的复杂运动和相互作用。该界面将是可伸缩和高效的，允许将视频增强、分割和跟踪软件快速应用于大型显微镜视频数据库。内容感知增强将在保留单元格特征的同时抑制杂乱。时间序列分割将识别细胞体和分支过程，因为它们在视频帧和z堆栈的切片之间移动。运输理论将使追踪细胞运动和其他变化成为可能，而不必构建可能会对结果产生偏差的复杂模型。为ImageJ和Vaa3D等常用软件包开发的插件将允许科学家将这些工具与现有工作流集成。模块化设计将有助于随着时间的推移扩展和完善该软件的功能。这些软件组件可以跟踪小胶质细胞和少突胶质前体细胞等细胞对环境的反应，以及这些行为在感染过程中的变化。老鼠、斑马鱼和其他动物模型的显微镜视频将揭示由于这些细胞的复杂行为而目前无法获得的见解。这个项目的协作性质将为嵌入调查人员的学生提供宝贵的经验？实验室学习更多关于图像处理和生物应用的知识。在夏季对高中教师进行生物图像处理方面的培训，将使这些教师能够与弗吉尼亚州中部和其他地区学校的学生分享这些经验。这些软件和研究产品将在https://pages.shanti.virginia.edu/Neuroglia_Image_Toolkit/.This网站上在线获得，该奖项反映了美国国家科学基金会的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Microscope videos of brain and nerve cells provide scientists unprecedented access to these cells as they develop, interact with each other, and respond to injury, disease, or other changes in their environment. However, these videos frequently contain dozens or hundreds of cells, behaving in complicated ways not easily discernable to a human viewer. This project hosted at the University of Virginia is innovating computer-based processing to pick out these cells and analyze their movements automatically. By simplifying these tasks, this software will permit studying large quantities of data for meaningful patterns and rules governing these cells' behavior. A front-end interface and back-end modules will enable scaling these capabilities from single videos to enormous databases, limited only by available computing power. These new capabilities will help scientists uncover new insights about these critical cells, leading researchers closer to understanding similarities and differences in cell behavior among animals used to study diseases and disorders that affect millions of Americans. By bringing together biologists and engineers, this project also provides exciting new experiences for students to learn about the future possibilities at the intersection of these fields. Training high-school teachers in image processing and its applications provides this well-rounded experience to even more students. To help neuroscientists observe the behavior of neurons and glia in their native setting, this software will automate the processing and analysis of complicated movements and interactions among dozens or hundreds of cells in high-resolution microscope videos. The interface will be both scalable and efficient, permitting rapid application of video enhancement, segmentation, and tracking software to large databases of microscope videos. The content-aware enhancement will suppress clutter while preserving cell features. Time-series segmentation will identify both cell bodies and ramified processes as they move between video frames and slices of the z-stack. Transport theory will enable tracking cell movements and other changes without having to construct a complicated model that could bias the results. Plugins under development for common packages like ImageJ and Vaa3D will allow scientists to integrate these tools with existing workflows. A modular design will facilitate expanding and refining the capabilities of this software over time. These software components can track how cells such as microglia and oligodendrocyte progenitor cells react to their environment and how these behaviors change during infection. Microscopy videos of mice, zebrafish, and other animal models will reveal insights not currently accessible due to the complicated behaviors of these cells. The collaborative nature of this project will provide valuable experiences for students embedded in the investigators? laboratories to learn more about image processing and biological applications. Training high school teachers on biological image processing during the summer will enable these teachers to share these experiences with students at schools across central Virginia and beyond. The software and research products will become available online at https://pages.shanti.virginia.edu/Neuroglia_Image_Toolkit/.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.