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Nat Methods. 2010 Sep;7(9):747-54. doi: 10.1038/nmeth.1486. Epub 2010 Aug 8.

CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging.

Author information

1
Institute of Biochemistry, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland.

Abstract

Fluorescence time-lapse imaging has become a powerful tool to investigate complex dynamic processes such as cell division or intracellular trafficking. Automated microscopes generate time-resolved imaging data at high throughput, yet tools for quantification of large-scale movie data are largely missing. Here we present CellCognition, a computational framework to annotate complex cellular dynamics. We developed a machine-learning method that combines state-of-the-art classification with hidden Markov modeling for annotation of the progression through morphologically distinct biological states. Incorporation of time information into the annotation scheme was essential to suppress classification noise at state transitions and confusion between different functional states with similar morphology. We demonstrate generic applicability in different assays and perturbation conditions, including a candidate-based RNA interference screen for regulators of mitotic exit in human cells. CellCognition is published as open source software, enabling live-cell imaging-based screening with assays that directly score cellular dynamics.

PMID:
20693996
DOI:
10.1038/nmeth.1486
[Indexed for MEDLINE]

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