High-throughput screening (HTS) is a key process used in drug discovery to identify hits from compound libraries that may become leads for medicinal chemistry optimization. This updated overview discusses the utilization of compound libraries, compounds derived from combinatorial and parallel synthesis campaigns and natural product sources; creation of mother and daughter plates; and compound storage, handling, and bar coding in HTS. The unit also presents an overview of established and emerging assay technologies (i.e., time-resolved fluorescence, fluorescence polarization, fluorescence-correlation spectroscopy, functional whole cell assays, and high-content assays) and their integration in automation hardware and IT systems. This revised unit provides updated descriptions of state-of-the-art instrumentation and technologies in this rapidly changing environment. The section on assay methodologies now also covers enzyme complementation assays and methods for high-throughput screening of ion channel activities. Finally, a section on criteria for assay robustness is included discussing the Z'-factor, which is now a widely accepted criterion for evaluation and validation of high throughput screening assays.

Automated image processing is a critical and often rate-limiting step in high-content screening (HCS) workflows. The authors describe an open-source imaging-statistical framework with emphasis on segmentation to identify novel selective pharmacological inducers of autophagy. They screened a human alveolar cancer cell line and evaluated images by both local adaptive and global segmentation. At an individual cell level, region-growing segmentation was compared with histogramderived segmentation. The histogram approach allowed segmentation of a sporadic-pattern foreground and hence the attainment of pixel-level precision. Single-cell phenotypic features were measured and reduced after assessing assay quality control. Hit compounds selected by machine learning corresponded well to the subjective threshold-based hits determined by expert analysis. Histogram-derived segmentation displayed robustness against image noise, a factor adversely affecting region growing segmentation. (Journal of Biomolecular Screening 2010:869-881)

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