Intensity calibration and automated cell cycle gating for high-throughput image-based siRNA screens of mammalian cells

Authors

  • Steven S. S. Poon,

    1. Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, B.C, Canada, V5Z1L3
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  • Jason T. Wong,

    1. Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, B.C, Canada, V5Z1L3
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  • Darren N. Saunders,

    1. Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, B.C, Canada, V5Z1L3
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  • Qianli C. Ma,

    1. Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, B.C, Canada, V5Z1L3
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  • Steven McKinney,

    1. Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, B.C, Canada, V5Z1L3
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  • John Fee,

    1. Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, B.C, Canada, V5Z1L3
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  • Samuel A. J. R. Aparicio

    Corresponding author
    1. Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, B.C, Canada, V5Z1L3
    2. Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
    • Department of Molecular Oncology, British Columbia Cancer Research Centre, 675 West 10th Avenue, Vancouver, B.C, Canada, V5Z1L3
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  • SAJRA is a recipient of a Canada Research Chair in Molecular Oncology

Abstract

High-content microscopic screening systems are powerful tools for extracting quantitative multiparameter measures from large number of cells under numerous conditions. These systems perform well in applications that monitor the presence of objects, but lack in their ability to accurately estimate object intensities and summarize these findings due to variations in background, aberrations in illumination, and variability in staining over the image and/or sample wells. We present effective and automated methods that are applicable to analyzing intensity-based cell cycle assays under high-throughput screening conditions. We characterize the system aberration response from images of calibration beads and then enhance the detection and segmentation accuracy of traditional algorithms by preprocessing images for local background variations. We also provide a rapid, adaptive, cell-cycle partitioning algorithm to characterize each sample well based on the estimated locally and globally corrected cell intensity measures of BrdU and DAPI incorporation. We demonstrated the utility and range of our cell ploidy and probe density measurement methods in a pilot screen using a siRNA library against 779 human protein kinases. With our method, multiple image-based quantitative phenotypes can be realized from a single high-throughput image-based microtiter-plate screen. © 2008 International Society for Advancement of Cytometry

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