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High throughput phenotyping of model organisms
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
2012 (English)In: BioImage Informatics 2012 / [ed] Fuhui Long, Ivo F. Sbalzarini, Pavel Tomancak and Michael Unser, Dresden, Germany, 2012, 45-45 p.Conference paper, Abstract (Refereed)
Abstract [en]

Microscopy has emerged as one of the most powerful and informative ways to analyze cell-based high-throughput screening samples in experiments designed to uncover novel drugs and drug targets. However, many diseases and biological pathways can be better studied in whole animals – particularly diseases that involve organ systems and multi-cellular interactions, such as metabolism, infection, vascularization, and development. Two model organisms compatible with high-throughput phenotyping are the 1mm long round worm C. elegans and the transparent embryo of zebrafish (Danio rerio). C. elegans is tractable as it can be handled using similar robotics, multi-well plates, and flow-sorting systems as are used for high-throughput screening of cells. The worm is also transparent throughout its lifecycle and is attractive as a model for genetic functions as its genes can be turned off by RNA-interference. Zebrafish embryos have also proved to be a vital model organism in many fields of research, including organismal development, cancer, and neurobiology. Zebrafish, being vertebrates, exhibit features common to phylogenetically higher organisms such as a true vasculature and central nervous system.


Basically any phenotypic change that can be visually observed (in untreated or stained worms and fish) can also be imaged. However, visual assessment of phenotypic variation is tedious and prone to error as well as observer bias. Screening in high throughput limits image resolution and time-lapse information. Still, the images are typically rich in information and the number of images for a standard screen often exceeds 100 000, ruling out visual inspection. Generation of automated image analysis platforms will increase the throughout of data analysis, improve the robustness of phenotype scoring, and allow for reliable application of statistical metrics for evaluating assay performance and identifying active compounds.


We have developed a platform for automated analysis of C. elegans assays, and are currently developing tools for analysis of zebrafish embryos. Our worm analysis tools, collected in the WormToolbox, can identify individual worms also as they cross and overlap, and quantify a large number of features, including mapping of reporter protein expression patterns to the worm anatomy. We have evaluated the tools on screens for novel treatments of infectious disease and genetic perturbations affecting fat metabolism. The WormToolbox is part of the free and open source CellProfiler software, also including methods for image assay quality control and feature selection by machine learning.

Place, publisher, year, edition, pages
Dresden, Germany, 2012. 45-45 p.
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:uu:diva-188374OAI: oai:DiVA.org:uu-188374DiVA: diva2:577794
BioImage Informatics 2012

Invited talk.

Available from: 2012-12-17 Created: 2012-12-17 Last updated: 2013-01-07

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