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J Biomol Screen. 2010 Aug;15(7):869-81. doi: 10.1177/1087057110373393. Epub 2010 Jun 14.

An automated high-content screening image analysis pipeline for the identification of selective autophagic inducers in human cancer cell lines.

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Bioinformatics Institute (A*STAR), Singapore.


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 histogram-derived 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.

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