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

描述(申请人提供)：高含量筛选(HCS)被定义为样品制备、自动显微成像和生物信息学工具的集成，这些工具允许用高通量细胞图像进行实验和发现。它有可能通过自动测量活细胞中基因和蛋白质的时间和空间活动来产生功能信息，从而使大规模细胞生物学成为一种易于处理的方法。然而，在高内容筛选中，存在着巨大的计算挑战，例如对大量细胞群体的准确分割和细胞表型的分类，而图像信息学已经成为充分发挥其潜力的速度限制因素。因此，我们建议开发新一代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来提取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来细化细胞边界，并提高评分方法的准确性，以反映不同细胞表型在筛选中的混合。第三，我们将开发一种基于模糊集合论方法的创新和有效的评分方法。简明的评分将使研究人员很容易理解结果的意义，并识别感兴趣的基因。这一应用的假设是，所提出的图像信息学系统G-CELLIQ(基因组细胞成像量化器)对于大规模RNAi基因组筛选以识别Rho蛋白的新效应物至关重要。Rho家族的小GTP酶在正常细胞迁移和肿瘤转移过程中对细胞形状的改变是必不可少的。全基因组RNAi筛选的目标是使用基于细胞的Rho活性分析来识别Rho蛋白的新效应物。为了验证我们的假设，我们将通过一组定义良好的、生物驱动的实验来评估G-CELLIQ的实用性。在建议的计划完成后，我们计划透过公众网站免费向生物医学研究团体提供这套资料。更重要的是，这一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促进我们在肿瘤生物学方面的知识，并为未来新的治疗方法打开可能性。这个项目将通过了解Rho家族的小GTP酶对公共卫生做出重大贡献，Rho家族与发育和癌症生物学具有基本的相关性。更重要的是，这一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促进我们在肿瘤生物学方面的知识，并为未来新的治疗方法打开可能性。

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.