RT Journal
A1 Harder, Nathalie
A1 Mora-Bermúdez, Felipe
A1 Godinez, William J.
A1 Wünsche, Annelie
A1 Eils, Roland
A1 Ellenberg, Jan
A1 Rohr, Karl
T1 Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time
JF Genome Research 
JO Genome Research 
YR 2009 
FD November 01 
VO 19 
IS 11 
SP 2113 
OP 2124 
DO 10.1101/gr.092494.109 
UL http://genome.cshlp.org/content/19/11/2113.abstract 
AB 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.