Drug discovery is the process by which potential new medicines are identified, repurposed and/or designed. This involves a wide range of scientific disciplines including, but not limited to, biology, chemistry, and pharmacology. With the latest developments in the fields of medicine and biotechnology, there is a growing need to develop advanced imaging methods, to identify the effect of drugs on cells with precision and accuracy. Molecular imaging involves noninvasive visualization, characterization and quantification of molecular and biochemical events that occur at the cellular or subcellular levels after drug treatment. By exploiting specific molecular probes/stains as well as intrinsic tissue characteristics, this approach provides an insight for analyzing earlier detection, disease characterization, and evaluation of treatment at the molecular level.
Cell Painting is a multistep process in which different cellular compartments are “painted” with different fluorescent bioprobes. The reason for applying cell painting is that we can visualize the aggregate expression of these markers and evaluate through high content imaging how the phenotype of these markers changes as a response to treatment. At Visikol, we have developed methods to visualize these molecular changes at the cellular level by Cell Painting1 after the drug treatment. For example, untreated/treated cells are stained with different fluorescent bioprobes (e.g., stains/dyes for plasma membrane, mitochondria, cytoskeleton, Golgi apparatus, lysosomes, nucleus etc.) in live or fixed cells.
When we leverage cell painting we use a Molecular Devices ImageXpress high content confocal imager to visualize our samples after labeling different cellular compartments. Based on the image analysis of the different cellular compartments (i.e. nucleus, cytoskeleton, etc.) we can quantitively profile multiple phenotypic parameters to better understand the effect of different drugs, chemical compounds, or genes on the cells.
Statistical analysis of this highly dimensional image data allows for clustering of similar phenotypic profiles that may suggest a mechanism or mode of action (MOA) for unknown compounds with reference to compounds of known MOA. Our future perspectives include incorporating high-throughput robotics and automation to seed cells and deliver compounds to microplates, improving high throughput technology in imaging and its computational image processing pipeline by integrating the data generated with AI and machine learning tools. Our approach aids pharmaceutical research in solving drug discovery problems with high specificity and sensitivity with high temporal and spatial resolutions.
If you are interested in utilizing this cell painting approach for your drug discovery projects, please reach out to our team to discuss your project. We are always interested to work together with our Clients to develop customized assays to best suit their needs.