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Cellular Phenotype Recognition for High-Content RNA Interference Genome-Wide Screening

Center for Bioinformatics, Harvard Center for Neurodegeneration and Repair, Harvard Medical School, Boston, Massachusetts, Department of Electrical Engineering, Columbia University, New York

Xiaobo Zhou

Center for Bioinformatics, Harvard Center for Neurodegeneration and Repair, Harvard Medical School, Boston, Massachusetts, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, zhou{at}crystal.harvard.edu

Pamela L. Bradley

Department of Genetics and Howard Hughes Medical Institute Harvard Medical School, Boston, Massachusetts

Shih-Fu Chang

Department of Electrical Engineering, Columbia University, New York

Norbert Perrimon

Department of Genetics and Howard Hughes Medical Institute Harvard Medical School, Boston, Massachusetts

Stephen T.C. Wong

Center for Bioinformatics, Harvard Center for Neurodegeneration and Repair, Harvard Medical School, Boston, Massachusetts, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts

Genome-wide, cell-based screens using high-content screening (HCS) techniques and automated fluorescence microscopy generate thousands of high-content images that contain an enormous wealth of cell biological information. Such screens are key to the analysis of basic cell biological principles, such as control of cell cycle and cell morphology. However, these screens will ultimately only shed light on human disease mechanisms and potential cures if the analysis can keep up with the generation of data. A fundamental step toward automated analysis of high-content screening is to construct a robust platform for automatic cellular phenotype identification. The authors present a framework, consisting of microscopic image segmentation and analysis components, for automatic recognition of cellular phenotypes in the context of the Rho family of small GTPases. To implicate genes involved in Rac signaling, RNA interference (RNAi) was used to perturb gene functions, and the corresponding cellular phenotypes were analyzed for changes. The data used in the experiments are high-content, 3-channel, fluorescence microscopy images of Drosophila Kc167 cultured cells stained with markers that allow visualization of DNA, polymerized actin filaments, and the constitutively activated Rho protein Rac^V12. The performance of this approach was tested using a cellular database that contained more than 1000 samples of 3 predefined cellular phenotypes, and the generalization error was estimated using a cross-validation technique. Moreover, the authors applied this approach to analyze the whole high-content fluorescence images of Drosophila cells for further HCS-based gene function analysis. (Journal of Biomolecular Screening 2008:29-39)

Key Words: high-content screening • RNA interference • microscopic image segmentation • phenotype feature extraction and selection • phenotype classification

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