Image analysis for High Content Screening (HCS) is a labor-intensive and error prone process, with multiple data hand-overs and operational complexity. As early drug discovery increasingly relies on complex phenotypic assays as biologically relevant model systems, the consequences of employing a time-consuming and repetitive analysis process are magnified.
During this tutorial, we show the future of HCS image analysis enabled by Genedata Imagence, and how this solution efficiently automates HCS image analysis. An outstanding feature of this new solution is that it does not require technical expertise beyond that of an assay biologist to annotate images, and our deep learning-based workflow makes this an extremely efficient process enabling higher throughput and improved quality. We show how one can, unhindered by IT-related issues, rapidly detect and define all cellular phenotypes in a high-content screen, with the final goal to precisely quantify relevant pharmacology.
The software dramatically reduces the time and costs usually associated with manually optimizing the image analysis to produce quality results for a new screening experiment, going from weeks to just hours or minutes. Genedata Imagence fully automates HCS image analysis, enabling organizations to focus on the pharmacology and biology of their research rather than on technical details and ultimately increase their ROI with a much more cost-efficient drug screening.