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

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.
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Soumis le : mardi 10 janvier 2017 - 18:23:05
Dernière modification le : mardi 21 juillet 2020 - 03:58:57
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Michael Held, Michael Schmitz, Bernd Fischer, Thomas Walter, Beate Neumann, et al.. CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging. Nature Methods, Nature Publishing Group, 2010, 7, pp.747 - 754. ⟨10.1038/nmeth.1486⟩. ⟨hal-01431427⟩



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