Phaedra, a protocol-driven system for analysis and validation of high-content imaging and flow cytometry

Area of Science:

• Bioinformatics
• Computational Biology
• Drug Discovery Informatics

Background:

• High-content screening (HCS) generates complex datasets for detailed cellular analysis and phenotype detection.
• Effective informatics tools are essential for quality control, data reduction, and data mining of HCS data.
• Existing tools often lack the user-friendliness required by the scientific community.

Purpose of the Study:

• To develop an innovative informatics application, Phaedra, to address the challenges in analyzing HCS data.
• To support drug screening and target discovery workflows with advanced analytical capabilities.
• To integrate diverse data types and analysis methods within a user-friendly platform.

Main Methods:

• Phaedra was developed as a modular and flexible application with a role-tunable interface.
• It supports data from high-content imaging, multicolor flow cytometry, and traditional high-throughput screening.
• Integration with MATLAB for image analysis and KNIME for data mining enhances its analytical power.

Main Results:

• Phaedra offers user-friendly data visualization and reduction tools specifically for HCS.
• The application provides efficient JPEG2000 compression and drill-down capabilities from dose-response curves to individual cells.
• Features include cell classification, statistical quality controls, annotation, and reporting functionalities.

Conclusions:

• Phaedra effectively reconciles complex analysis methods with user-friendliness for HCS data.
• It serves as a versatile platform for drug screening and target discovery, enhancing data interpretation and reliability.
• The tool's modularity and integration capabilities make it adaptable to various research needs in cellular assays.