The Bioinformatics Image Core (BIONIC) links biology with mathematics, image processing, computer science and statistics to develop computational tools for image analysis.
We use PerkinElmer Opera and Phenix high-content screening microscopes, ImageJ analysis software and the statistical program R with Bioconductor packages such as CellHTS2, in monolayer and 3D tissue culture.
Key methodologies include:
- Applied image segmentation
- Thresholding
- Morphological operations
- Filtering
- Feature extractions
- High-throughput statistical analysis
- Algorithm development
- Selected publications
Wilson GA et al (2023). Active growth signaling promotes senescence and cancer cell sensitivity to CDK7 inhibition. Molecular Cell 83(22):4078-4092.e6 doi: 10.1016/j.molcel.2023.10.017
Papandreou A et al (2023). Automated High-Content Imaging in iPSC-derived Neuronal Progenitors. SLAS Discovery doi: 10.1016/j.slasd.2022.12.002
Ketteler R, Kriston-Vizi J (2022). High-Content Screening in Cell Biology in: Encyclopedia of Cell Biology: Volume 1-6, Second Edition: 472-483 doi: 10.1016/B978-0-12-821618-7.00032-8
Kriston-Vizi J et al (2022). Salmonella Exhibit Altered Cellular Localization in the Presence of HLA-B27 and Codistribute with Endo-Reticular Membrane. Journal of Immunology Research 2022:1-9 doi: 10.1155/2022/9493019