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CellCognition: Time-resolved Phenotype Annotation in High-throughput Live Cell Imaging

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Journal Nat Methods
Date 2010 Aug 10
PMID 20693996
Citations 169
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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.

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References
1.
Zhou X, Li F, Yan J, Wong S . A novel cell segmentation method and cell phase identification using Markov model. IEEE Trans Inf Technol Biomed. 2009; 13(2):152-7. PMC: 2846548. DOI: 10.1109/TITB.2008.2007098. View

2.
Conrad C, Gerlich D . Automated microscopy for high-content RNAi screening. J Cell Biol. 2010; 188(4):453-61. PMC: 2828931. DOI: 10.1083/jcb.200910105. View

3.
Chen X, Zhou X, Wong S . Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy. IEEE Trans Biomed Eng. 2006; 53(4):762-6. DOI: 10.1109/TBME.2006.870201. View

4.
Bollen M, Gerlich D, Lesage B . Mitotic phosphatases: from entry guards to exit guides. Trends Cell Biol. 2009; 19(10):531-41. DOI: 10.1016/j.tcb.2009.06.005. View

5.
Wang M, Zhou X, King R, Wong S . Context based mixture model for cell phase identification in automated fluorescence microscopy. BMC Bioinformatics. 2007; 8:32. PMC: 1800869. DOI: 10.1186/1471-2105-8-32. View