Analysis of tracking based phenotypes in CNS

描述(由申请人提供)：项目摘要本项目的目标是开发“原型和规模”的动态图像识别软件，以在基于显微镜的广泛中枢神经系统(CNS)应用中基于跟踪的表型进行评分。可以灵活地教导目标软件工具以评分或筛选感兴趣的精确动态表型，并且与成像分析平台(例如成像系统、试剂等)一起可扩展。以提供高吞吐量评分。它将提供1)分离异质时空模式的运动信号分解；2)可教的亚细胞对象检测；3)可教的亚细胞对象跟踪；4)全面的轨迹表征；以及5)支持动态表型发现和评分的动态数据分析接口。我们建议扩展我们的跟踪技术并将其商业化，包括基于可教跟踪状态的跟踪、运动能量跟踪增强和轨迹分类。工作产品将被整合到我们的旗舰产品SVCell中，并通过包括尼康公司在内的商业合作伙伴销售。我们的假设是，我们的跟踪软件可以用于对广泛的CNS应用进行准确和稳健的分析，并可扩展以高速、准确和可靠地对动态跟踪特性进行高通量评分。为了验证这一假设，我们制定了以下具体目标：目标1：针对广泛的基于跟踪的表型优化可教跟踪模块；目标2：优化和验证基于跟踪的表型的高通量评分；目标3：通过现场测试和科学合作评估SVCell跟踪测试版的产品准备情况。这个项目意义重大，因为需要能够以高通量灵活地筛选各种基于跟踪的动态表型的技术，以支持基础研究(例如表型鉴定)和应用研究(例如药物发现)中与中枢神经系统相关的实际和有效的科学计划。对延时显微镜电影中这些动态表型的分析可以为CNS疾病的形成提供洞察力，并使更早和更有效的干预成为可能。公共卫生相关性：项目叙事延时显微镜图像识别可以对对抗神经疾病和精神疾病产生重大影响。通过量化电影中复杂的、动态的表型，它为科学家提供了一个强大的工具，用来发现重要中枢神经系统功能背后的基本机制，然后筛选作用于这些功能的方法。这可以使更早和更有效的治疗干预成为可能。

DESCRIPTION (provided by applicant): Project Summary The objective of this project is to develop "Prototype and Scale" kinetic image recognition software to score tracking based phenotypes in a broad range of microscopy based central nervous system (CNS) applications. The target software tool can be flexibly taught to score or screen the precise dynamic phenotypes of interest, and scalable, together with the imaging assay platform (e.g. imaging system, reagents, etc.) to provide high throughput scoring. It will provide 1) motion signal decomposition to isolate heterogeneous spatial-temporal patterns; 2) teachable subcellular object detection; 3) teachable subcellular object tracking; 4) comprehensive track characterization; and 5) kinetic data analysis interfaces to support dynamic phenotype discovery and scoring. We propose to expand and commercialize our tracking technologies including teachable tracking state based tracking, motion energy tracking enhancement and track classification. The work product will be incorporated into our flagship product SVCell, and sold through our commercial partners including Nikon Corporation. The hypothesis is that our tracking software can be used for the accurate and robust analysis of a broad range of CNS applications, and scalable for the high throughput scoring of dynamic tracking characteristics with high speed, accuracy and reliability. To test this hypothesis, we set forth the following specific aims: Aim 1: Optimize the teachable tracking module for a broad range of tracking based phenotypes; Aim 2: Optimize and validate the high throughput scoring of tracking based phenotypes; Aim 3: Evaluate the product readiness of the SVCell tracking beta through field tests and scientific collaborations. This project is significant because technologies are needed which can flexibly screen a variety of tracking based, dynamic phenotypes at high throughput to support practical and effective CNS related scientific programs in basic research (e.g. phenotyping) and applied research (e.g. drug discovery). Analysis of these dynamic phenotypes in time-lapse microscopy movies could provide insights into CNS disease formation and enable earlier and more effective interventions. PUBLIC HEALTH RELEVANCE: Project Narrative Time-lapse microscopy image recognition can make a significant impact on the fight against neurological diseases and mental illness. By quantifying complex, dynamic phenotypes in the movies, it gives scientists a powerful tool with which to discover the basic mechanisms underlying important CNS functions, and then screen methods that act on them. This could enable earlier and more effective therapeutic interventions.