Journal of Biomolecular Screening 17(4) 496–508 © 2012 Society for Laboratory Automation and Screening DOI: 10.1177/1087057111432885 http://jbx.sagepub.com Introduction High-Content Landscape High-throughput screening of chemical libraries against predefined targets is an established method to generate hits from which lead compounds can be derived. 1,2 For the dis- covery of drugs with innovative mechanisms of action, or of new targets, there appear to be important advantages to phenotypic in vitro screening using small-molecule or RNAi libraries. 3,4 In today’s drug discovery process, we use cells and cell lines that exhibit normal and disease phe- notypes that occur in large multicellular organisms. Despite the unavoidable limitations of such models, the expecta- tion exists that they can improve the success rate of the drug discovery process. 5 This approach requires technologies that generate rich data sets from a cell-based assay, with several values for each individual analyzed cell, possibly with the addition of a time dimension. Microscopy-based high-content screening (HCS) is a flexible technology that can be used to perform fundamentally simple assays such as cell proliferation but also supports assays of high complexity. This includes the use of morphological parameters and fluorescent probes to characterize cellular phenotypes, the screening of co-cultures and primary cells, and the culture of cells in three-dimensional models. 6 Complex assays present a much greater challenge of data interpretation and quality control 7 because dealing with high-dimensional data requires more sophisticated tools and more complex math. Traditional quality control tools for screening are defined for univariate data sets. The 432885JBX XX X 10.1177/1087057111432885Corn elissen et al.Journal of Biomolecular Screening 1 Janssen Research & Development, a Division of Janssen Pharmaceutica NV, Beerse, Belgium Received Sep 21, 2011, and in revised form Nov 14, 2011. Accepted for publication Nov 14, 2011. Supplementary material for this article is available on the Journal of Biomolecular Screening Web site at http://jbx.sagepub.com/supplemental. Corresponding Author: Frans Cornelissen, Janssen Research & Development, a Division of Janssen Pharmaceutica NV, Turnhoutseweg 30, Beerse 2340, Belgium Email: fcorneli@its.jnj.com Phaedra, a Protocol-Driven System for Analysis and Validation of High-Content Imaging and Flow Cytometry Frans Cornelissen 1 , Miroslav Cik 1 , and Emmanuel Gustin 1 Abstract High-content screening has brought new dimensions to cellular assays by generating rich data sets that characterize cell populations in great detail and detect subtle phenotypes. To derive relevant, reliable conclusions from these complex data, it is crucial to have informatics tools supporting quality control, data reduction, and data mining. These tools must reconcile the complexity of advanced analysis methods with the user-friendliness demanded by the user community. After review of existing applications, we realized the possibility of adding innovative new analysis options. Phaedra was developed to support workflows for drug screening and target discovery, interact with several laboratory information management systems, and process data generated by a range of techniques including high-content imaging, multicolor flow cytometry, and traditional high-throughput screening assays. The application is modular and flexible, with an interface that can be tuned to specific user roles. It offers user-friendly data visualization and reduction tools for HCS but also integrates Matlab for custom image analysis and the Konstanz Information Miner (KNIME) framework for data mining. Phaedra features efficient JPEG2000 compression and full drill-down functionality from dose-response curves down to individual cells, with exclusion and annotation options, cell classification, statistical quality controls, and reporting. Keywords image analysis, high-content screening, high-throughput polychromatic flow cytometry, JPEG2000, data mining software