Vizeacoumar Franco J, Chong Yolanda, Boone Charles, Andrews Brenda J
Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1.
FEBS Lett. 2009 Jun 5;583(11):1656-61. doi: 10.1016/j.febslet.2009.03.068. Epub 2009 Apr 5.
Large scale cell biological experiments are beginning to be applied as a systems-level approach to decipher mechanisms that govern cellular function in health and disease. The use of automated microscopes combined with digital imaging, machine learning and other analytical tools has enabled high-content screening (HCS) in a variety of experimental systems. Successful HCS screens demand careful attention to assay development, data acquisition methods and available genomic tools. In this minireview, we highlight developments in this field pertaining to yeast cell biology and discuss how we have combined HCS with methods for automated yeast genetics (synthetic genetic array (SGA) analysis) to enable systematic analysis of cell biological phenotypes in a variety of genetic backgrounds.
大规模细胞生物学实验正开始作为一种系统水平的方法来解读在健康和疾病状态下调控细胞功能的机制。自动显微镜与数字成像、机器学习及其他分析工具的结合,使得在各种实验系统中进行高内涵筛选(HCS)成为可能。成功的HCS筛选需要仔细关注检测方法的开发、数据采集方法以及可用的基因组工具。在这篇微型综述中,我们重点介绍了该领域与酵母细胞生物学相关的进展,并讨论了我们如何将HCS与自动酵母遗传学方法(合成遗传阵列(SGA)分析)相结合,以在各种遗传背景下对细胞生物学表型进行系统分析。