An Automated High-Content Screening Image Analysis Pipeline for the Identification of Selective Autophagic Inducers in Human Cancer Cell Lines

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 al...

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Bibliographic Details
Published inJournal of biomolecular screening Vol. 15; no. 7; pp. 869 - 881
Main Authors Kriston-Vizi, Janos, Lim, Ching Aeng, Condron, Peter, Chua, Kelvin, Wasser, Martin, Flotow, Horst
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.08.2010
Subjects
Automation - methods
autophagy
Autophagy - drug effects
Cell Line, Tumor
Cell Nucleus - metabolism
cellular high-content screening
HCS
High-Throughput Screening Assays - methods
Humans
image processing
Image Processing, Computer-Assisted - methods
LC3
MDC
phenotypic assay
Quality Control
Reproducibility of Results
Vacuoles - metabolism
image processing
autophagy
cellular high-content screening
HCS
phenotypic assay
MDC
LC3
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Summary: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.
ISSN:2472-5552
2472-5560
1552-454X
DOI:10.1177/1087057110373393