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

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2010-07Author
Held, Michael
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Schmitz, Michael H. A.
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Fischer, Bernd
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Walter, Thomas
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Neumann, Beate
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Olma, Michael H.
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Peter, Matthias
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Ellenberg, Jan
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Gerlich, Daniel W.
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https://hdl.handle.net/1912/3869As published
https://doi.org/10.1038/nmeth.1486Keyword
Live cell imaging; RNAi screening; GFP; Machine Learning; Image analysis; Hidden Markov model; Cdc20; Mitotic exitAbstract
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. The 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 a set of different assays and
perturbation conditions, including a candidate-based RNAi screen for mitotic exit
regulators in human cells. CellCognition is published as open source software,
enabling live imaging-based screening with assays that directly score cellular
dynamics.
Description
Author Posting. © The Authors, 2010. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature Methods 7 (2010): 747-754, doi:10.1038/nmeth.1486.
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Preprint: Held, Michael, Schmitz, Michael H. A., Fischer, Bernd, Walter, Thomas, Neumann, Beate, Olma, Michael H., Peter, Matthias, Ellenberg, Jan, Gerlich, Daniel W., "CellCognition : time-resolved phenotype annotation in high-throughput live cell imaging", 2010-07, https://doi.org/10.1038/nmeth.1486, https://hdl.handle.net/1912/3869Related items
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