Morphological Profiling in Saccharomyces cerevisiae and Development of high-content imaging techniques

酿酒酵母的形态分析和高内涵成像技术的开发

• 批准号: 21310127
• 负责人: OHYA Yoshikazu
• 金额: $ 11.07万
• 依托单位: The University of Tokyo
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2009
• 资助国家: 日本
• 起止时间: 2009 至 2011
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-21310127/
• 关键词: 出芽酵母; 機能ゲノム; 細胞周期細胞形態; 画像解析; 薬剤標的; 細胞形態; 細胞周期; 細胞壁; HOG; MAP kinase; シグナル伝達; 紡錘体; M期; 出芽酵母; 機能ゲノム; 細胞周期細胞形態; 画像解析; 薬剤標的; 細胞形態; 細胞周期; 細胞壁; HOG; MAP kinase; シグナル伝達; 紡錘体; M期

Drug discovery are based on elucidation of the potential mechanisms of action and cellular targets of candidate chemical compounds. We developed high-content imaging techniques in Saccharomyces cerevisiae, allowing simultaneous analysis of morphological phenotypes regarding to cell shape, actin and nucleus. We proposed a novel strategy to identify drug targets by combining phenome database and high-content imaging in yeast. In this approach, we infer the cellular functions affected by candidate drugs by comparing morphologic profiles induced by the compounds with the phenotypes of yeast mutants. Using this method and four well-characterized reagents, we successfully identified previously known target genes of the compounds as well as other genes involved with functionally related cellular pathways. We also expanded our morphological analyses on other cellular structures. We succeeded to extract 1,111 parameters from digital images of 9 subcellular structures including cell shape, nuclear DNA, mitochondria, actin structures, spindle pole bodies, septin rings, the vacuole, the cis- and trans-Golgi. With advancements in our study, systematic and comprehensive morphological analyses will inevitably continue to progress.

药物发现是基于阐明候选化合物的作用机理和细胞靶标。我们在酿酒酵母中开发了高含量成像技术，从而可以同时分析有关细胞形状，肌动蛋白和核的形态表型。我们提出了一种新的策略，以结合现象数据库和酵母中的高含量成像来识别药物靶标。在这种方法中，我们通过将化合物诱导的形态学特征与酵母突变体的表型进行比较来推断受候选药物影响的细胞功能。使用此方法和四种特征良好的试剂，我们成功地鉴定了该化合物的先前已知靶基因以及与功能相关的细胞途径有关的其他基因。我们还扩展了对其他细胞结构的形态分析。我们成功地从9个亚细胞结构的数字图像中提取了1,111个参数，包括细胞形状，核DNA，线粒体，肌动蛋白结构，纺锤体体，septin环，液泡，顺式和反式高尔基。随着我们研究的进步，系统的和全面的形态分析将不可避免地继续发展。