Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time.

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 Res

journal_title

Genome research

authors

Harder N,Mora-Bermúdez F,Godinez WJ,Wünsche A,Eils R,Ellenberg J,Rohr K

doi

10.1101/gr.092494.109

subject

Has Abstract

pub_date

2009-11-01 00:00:00

pages

2113-24

issue

11

eissn

1088-9051

issn

1549-5469

pii

gr.092494.109

journal_volume

19

pub_type

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