Individual Cell Motility Image Informatics

描述（由申请人提供）：细胞运动性是胚胎发育、免疫应答、伤口愈合、血管生成、组织工程和包括癌症转移在内的各种疾病过程的核心基本过程。定量单细胞运动分析提供了一个强大的工具，以评估在这些领域的目标感兴趣的实验治疗的影响。尽管自动化活细胞成像系统的性价比和可用性稳步提高，但定量单个细胞运动性测定仍然是一个繁琐的、主要是手动的和不精确的过程。缺乏自动化主要是由于最先进的图像信息学软件中的细胞识别能力不足。一种易于使用的图像信息学工具，用于准确的细胞识别和单细胞运动性测定的高度自动化分析，将加速和扩展定量细胞运动性研究，影响基础研究、药物发现和疾病相关研究的许多领域。我们正在开发下一代显微图像信息学工具SVCell，该工具将显著改进的细胞识别，测量和分析技术用于定点测定中的广泛细胞类型。我们建议扩展SVCell的技术，以实现准确和高度自动化的基于图像的单个细胞运动分析。具体目标是：1）扩展SVCell以在延时、相差图像中对单个细胞进行高度自动化的动力学识别; 2）扩展SVCell以在集成分析环境中对动力学测量进行全面有效的分析;以及3）在具有良好基准的细胞运动实验中测试扩展的SVCell。主要创新是动态识别，可实现高识别精度和跟踪自动化。第二个创新是在集成分析环境中进行全面的动力学测量，提高了运动测定结果的质量和效率。SVCell Motility将是一套开放平台图像信息学应用程序之一，我们将在SVCell平台上向广泛的生命科学社区销售。

DESCRIPTION (provided by applicant): Cell motility is fundamental process central to embryonic development, immune response, wound healing, angiogenesis, tissue engineering and various disease processes including cancer metastasis. Quantitative single cell motility assays provide a powerful tool to evaluate the impact of experimental treatments on targets of interest in these fields. Despite the steady improving price-performance and availability of automated, live cell imaging systems, quantitative individual cell motility assays continue to be a tedious, largely manual and inexact process. The lack of automation is primarily due to inadequate cell recognition capabilities in the start-of-the-art image informatics software. An easy to use image informatics tool for the accurate cell recognition and highly automated analyses of single cell motility assays would accelerate and expand quantitative cell motility studies, impacting many fields in basic research, drug discovery and disease related research. We are developing a next generation microscopy image informatics tool, SVCell, incorporating significantly improved cell recognition, measurement, and analysis technology for a broad range of cell types in fixed-point assays. We propose to extend SVCell's technology to enable accurate and highly automated image based individual cell motility assays. The specific aims are: 1) Extend SVCell to perform highly automated kinetic recognition of individual cells in time-lapse, phase contrast images; 2) Extend SVCell to enable the comprehensive and efficient analysis of kinetic measurements in an integrated analysis environment; and 3) Test extended SVCell in a well benchmarked cell motility experiment. The primary innovation is kinetic recognition that enables high recognition accuracy and tracking automation. A second innovation is comprehensive kinetic measurements within an integrated analysis environment that improves the quality and efficiency of motility assay outcomes. SVCell Motility will be one of a suite of open platform image informatics applications that we will market to the broad life sciences community on the SVCell platform.