Automated image-based phenotypic analysis in zebrafish embryos

Authors

  • Andreas Vogt,

    1. Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
    2. University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania
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  • Andrzej Cholewinski,

    1. Definiens Inc., Morristown, New Jersey
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  • Xiaoqiang Shen,

    1. Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania
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  • Scott G. Nelson,

    1. Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania
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  • John S. Lazo,

    1. Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
    2. University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania
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  • Michael Tsang,

    Corresponding author
    1. University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania
    2. Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania
    • Department of Microbiology and Molecular Genetics, University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15213
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  • Neil A. Hukriede

    Corresponding author
    1. University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania
    2. Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania
    • Department of Microbiology and Molecular Genetics, University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15213
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Abstract

Presently, the zebrafish is the only vertebrate model compatible with contemporary paradigms of drug discovery. Zebrafish embryos are amenable to automation necessary for high-throughput chemical screens, and optical transparency makes them potentially suited for image-based screening. However, the lack of tools for automated analysis of complex images presents an obstacle to using the zebrafish as a high-throughput screening model. We have developed an automated system for imaging and analyzing zebrafish embryos in multi-well plates regardless of embryo orientation and without user intervention. Images of fluorescent embryos were acquired on a high-content reader and analyzed using an artificial intelligence-based image analysis method termed Cognition Network Technology (CNT). CNT reliably detected transgenic fluorescent embryos (Tg(fli1:EGFP)y1) arrayed in 96-well plates and quantified intersegmental blood vessel development in embryos treated with small molecule inhibitors of anigiogenesis. The results demonstrate it is feasible to adapt image-based high-content screening methodology to measure complex whole organism phenotypes. Developmental Dynamics 238:656–663, 2009. © 2009 Wiley-Liss, Inc.

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