Single-cell-based image analysis of high-throughput cell array screens for quantification of viral infection

Authors

  • Petr Matula,

    Corresponding author
    1. University of Heidelberg, Department of Bioinformatics and Functional Genomics, BIOQUANT, IPMB, Heidelberg, Germany
    2. German Cancer Research Center (DKFZ), Department of Theoretical Bioinformatics, Heidelberg, Germany
    3. Masaryk University, Faculty of Informatics, Center for Biomedical Image Analysis, Brno, Czech Republic
    • University of Heidelberg, BIOQUANT, Department of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
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  • Anil Kumar,

    1. University of Heidelberg, Department of Molecular Virology, Heidelberg, Germany
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  • Ilka Wörz,

    1. University of Heidelberg, Department of Molecular Virology, Heidelberg, Germany
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  • Holger Erfle,

    1. University of Heidelberg, BIOQUANT Center, Heidelberg, Germany
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  • Ralf Bartenschlager,

    1. University of Heidelberg, Department of Molecular Virology, Heidelberg, Germany
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  • Roland Eils,

    1. University of Heidelberg, Department of Bioinformatics and Functional Genomics, BIOQUANT, IPMB, Heidelberg, Germany
    2. German Cancer Research Center (DKFZ), Department of Theoretical Bioinformatics, Heidelberg, Germany
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  • Karl Rohr

    1. University of Heidelberg, Department of Bioinformatics and Functional Genomics, BIOQUANT, IPMB, Heidelberg, Germany
    2. German Cancer Research Center (DKFZ), Department of Theoretical Bioinformatics, Heidelberg, Germany
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  • A preliminary report of this work was presented at the SPIE Medical Imaging conference (San Diego, February 2008) and the Thirteenth Leipziger workshop (Leipzig, April 2008).

Abstract

The identification of eukaryotic genes involved in virus entry and replication is important for understanding viral infection. Our goal is to develop a siRNA-based screening system using cell arrays and high-throughput (HT) fluorescence microscopy. A central issue is efficient, robust, and automated single-cell-based analysis of massive image datasets. We have developed an image analysis approach that comprises (i) a novel, gradient-based thresholding scheme for cell nuclei segmentation which does not require subsequent postprocessing steps for separation of clustered nuclei, (ii) quantification of the virus signal in the neighborhood of cell nuclei, (iii) localization of regions with transfected cells by combining model-based circle fitting and grid fitting, (iv) cell classification as infected or noninfected, and (v) image quality control (e.g., identification of out-of-focus images). We compared the results of our nucleus segmentation approach with a previously developed scheme of adaptive thresholding with subsequent separation of nuclear clusters. Our approach, which does not require a postprocessing step for the separation of nuclear clusters, correctly segmented 97.1% of the nuclei, whereas the previous scheme achieved 95.8%. Using our algorithm for the detection of out-of-focus images, we obtained a high discrimination power of 99.4%. Our overall approach has been applied to more than 55,000 images of cells infected by either hepatitis C or dengue virus. Reduced infection rates were correctly detected in positive siRNA controls, as well as for siRNAs targeting, for example, cellular genes involved in viral infection. Our image analysis approach allows for the automatic and accurate determination of changes in viral infection based on high-throughput single-cell-based siRNA cell array imaging experiments. © 2008 International Society for Advancement of Cytometry

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