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 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.
Kriston-Vizi
Janos
J
Bioinformatics Institute (A*STAR), Singapore. j.kriston@ucl.ac.uk
Lim
Ching Aeng
CA
Condron
Peter
P
Chua
Kelvin
K
Wasser
Martin
M
Flotow
Horst
H
D001331:Automation / Q000379:methods*; D001343:Autophagy / Q000187:drug effects*; D045744:Cell Line, Tumor; D002467:Cell Nucleus / Q000378:metabolism; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D011786:Quality Control; D015203:Reproducibility of Results; D014617:Vacuoles / Q000378:metabolism