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

Regulation of the NF-kB transcriptional program triggered by toll like receptor 4

Ph.DDOCTOR OF PHILOSOPH

Genome-wide Mapping of RELA(p65) Binding Identifies E2F1 as a Transcriptional Activator Recruited by NF-κB upon TLR4 Activation

10.1016/j.molcel.2007.06.038Molecular Cell274622-635MOCE

A core Klf circuitry regulates self-renewal of embryonic stem cells

10.1038/ncb1698Nature Cell Biology103353-360NCBI