University of Heidelberg, IPMB, BIOQUANT, and DKFZ Heidelberg, Department of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, D-69120 Heidelberg, Germany.
Genome Res. 2009 Nov;19(11):2113-24. doi: 10.1101/gr.092494.109. Epub 2009 Oct 1.
Live-cell imaging allows detailed dynamic cellular phenotyping for cell biology and, in combination with small molecule or drug libraries, for high-content screening. Fully automated analysis of live cell movies has been hampered by the lack of computational approaches that allow tracking and recognition of individual cell fates over time in a precise manner. Here, we present a fully automated approach to analyze time-lapse movies of dividing cells. Our method dynamically categorizes cells into seven phases of the cell cycle and five aberrant morphological phenotypes over time. It reliably tracks cells and their progeny and can thus measure the length of mitotic phases and detect cause and effect if mitosis goes awry. We applied our computational scheme to annotate mitotic phenotypes induced by RNAi gene knockdown of CKAP5 (also known as ch-TOG) or by treatment with the drug nocodazole. Our approach can be readily applied to comparable assays aiming at uncovering the dynamic cause of cell division phenotypes.
活细胞成像允许对细胞生物学进行详细的动态细胞表型分析，并且与小分子或药物文库结合使用，还可进行高内涵筛选。由于缺乏能够以精确方式随时间跟踪和识别单个细胞命运的计算方法，因此对活细胞电影的全自动分析受到了阻碍。在这里，我们提出了一种全自动的方法来分析有丝分裂细胞的延时电影。我们的方法可以动态地将细胞分类为细胞周期的七个阶段，并随着时间的推移将其分类为五个异常形态表型。它可以可靠地跟踪细胞及其后代，因此可以测量有丝分裂阶段的长度，并在有丝分裂出错时检测原因和结果。我们将我们的计算方案应用于通过 RNAi 基因敲低 CKAP5（也称为 ch-TOG）或用药物 nocodazole 处理诱导的有丝分裂表型的注释。我们的方法可以很容易地应用于旨在揭示细胞分裂表型动态原因的类似测定中。
