High-content Image Analysis and Modeling for RANigenome-wide Screening

描述（由申请人提供）：高含量筛选（HCS）定义为样品制备、自动显微成像和生物信息学工具的集成，这些工具允许使用高通量细胞图像进行实验和发现。它有可能使大规模细胞生物学成为一种易于处理的方法，通过自动测量活细胞中基因和蛋白质的时空活动来生成功能信息。然而，在高内容筛选中存在显著的计算挑战，例如大量细胞的准确分割和细胞表型的分类，并且图像信息学已经成为实现其全部潜力的限速因素。因此，我们建议开发新一代的计算工具来填补这一空白。我们强调G-CELLIQ的三个关键技术贡献。首先，G-CELLIQ将提供一个集成的细胞图像处理管道，使用先进的计算算法提取RNAi筛选图像的内容，减少人工分析处理所需的时间和人工分析的可变性。其次，我们将开发新的分类控制反馈系统，以细化细胞边界，并提高评分方法的准确性，反映筛选中不同细胞表型的混合物。第三，我们将开发一个创新的和有效的评分方法的基础上模糊集理论的方法。简洁的分数将使研究人员能够轻松理解结果的意义并识别感兴趣的基因。本申请的假设是，所提出的图像信息学系统G-CELLIQ（基因组细胞成像定量器）对于大规模RNAi基因组筛选以鉴定Rho蛋白的新型效应物是至关重要的。Rho家族的小GTP酶对于正常细胞迁移和癌症转移期间的细胞形状变化是必需的。全基因组RNAi筛选的目标是使用基于细胞的Rho活性测定来鉴定Rho蛋白的新型效应子。为了验证我们的假设，我们将通过一组定义明确的生物驱动实验来评估G-CELLIQ的实用性。拟议项目完成后，我们计划通过公共网站向生物医学研究界免费提供该软件包。更重要的是，这个筛查项目的完成将有助于回答一些与癌症转移相关的关键问题。这样的理解将反过来推进我们在肿瘤生物学方面的知识，并为未来的新治疗开辟可能性。该项目将通过了解与发育和癌症生物学基本相关的小GTP酶Rho家族，为公共卫生做出重大贡献。更重要的是，这个筛查项目的完成将有助于回答一些与癌症转移相关的关键问题。这样的理解将反过来推进我们在肿瘤生物学方面的知识，并为未来的新治疗开辟可能性。

DESCRIPTION (provided by applicant): High-content screening (HCS) is defined as the integration of sample preparation, automatic microscopic imaging, and bioinformatics tools that permit experimentation and discovery with high-throughput cell images. It has potential to make large-scale cell biology a tractable approach by generating functional information through the automated measurements of the temporal and spatial activities of genes and proteins in living cells. However, there are significant computational challenges, such as accurate segmentation of the large population of cells and classification of cellular phenotypes, in high-content screening, and image informatics has become the rate-limiting factor in realizing its full potential. Therefore, we propose to develop a new generation of computational tools to fill that gap. We emphasize three key technical contributions of G-CELLIQ. First, G-CELLIQ will provide an integrated cell image processing pipeline using advanced computational algorithms to extract contents of RNAi screening images, reducing the time required in processing by manual analysis and the variability in manual analysis. Second, we will develop novel classification-controlled feedback systems to refine cell boundaries and to increase the accuracy of the scoring method that reflect the mixture of different cell phenotypes in the screening. Third, we will develop an innovative and effective scoring method based on the fuzzy set-theoretic approach. The succinct score will allow researchers to easily comprehend the significance of the results and identify the genes of interest. The hypothesis of this application is that the proposed image informatics system, G-CELLIQ (Genomic CELLular Imaging Quantitator), is critical for large scale RNAi genome screening to identify novel effectors of Rho proteins. The Rho family of small GTPases is essential for cell shape changes during normal cell migration and cancer metastasis. The goal of genome-wide RNAi screening is to identify novel effectors of Rho proteins using a cell-based assay for Rho activities. To test our hypothesis, we will evaluate the utility of the G-CELLIQ with a set of well defined, biological-driven experiments. Upon completion of the proposed project, we plan to make this package freely available to biomedical research community through a public website. More importantly, the completion of this screening project will help to answer some critical questions related to cancer metastasis. Such understanding will in turn advance our knowledge in tumor biology and open up the possibility of novel treatments in the future. This project will be a substantial contribution to the public health by understanding Rho family of small GTPases which is of fundamental relevance to developmental and cancer biology. More importantly, the completion of this screening project will help to answer some critical questions related to cancer metastasis. Such understanding will in turn advance our knowledge in tumor biology and open up the possibility of novel treatments in the future.