Abstract:
: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.
journal_name
Genome Resjournal_title
Genome researchauthors
Harder N,Mora-Bermúdez F,Godinez WJ,Wünsche A,Eils R,Ellenberg J,Rohr Kdoi
10.1101/gr.092494.109subject
Has Abstractpub_date
2009-11-01 00:00:00pages
2113-24issue
11eissn
1088-9051issn
1549-5469pii
gr.092494.109journal_volume
19pub_type
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