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

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    held_NMETH_2010-1.pdf (2.389Mb)
    Date
    2010-07
    Author
    Held, Michael  Concept link
    Schmitz, Michael H. A.  Concept link
    Fischer, Bernd  Concept link
    Walter, Thomas  Concept link
    Neumann, Beate  Concept link
    Olma, Michael H.  Concept link
    Peter, Matthias  Concept link
    Ellenberg, Jan  Concept link
    Gerlich, Daniel W.  Concept link
    Metadata
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    Citable URI
    https://hdl.handle.net/1912/3869
    As published
    https://doi.org/10.1038/nmeth.1486
    Keyword
     Live cell imaging; RNAi screening; GFP; Machine Learning; Image analysis; Hidden Markov model; Cdc20; Mitotic exit 
    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. 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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    Suggested Citation
    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/1912/3869
     

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