Image-based high-content screening in drug discovery. Issue 8 (August 2020) - Lin, Sean; Schorpp, Kenji; Rothenaigner, Ina; Hadian, Kamyar
Highlights: Complex phenotypic imaging assays aim at mimicking the biology of interest. Cellular profiling in high-content assays improve description of compound actions. Assessing cellular fingerprints of compounds can transform drug development. Machine learning applications enable analysis of multidimensional datasets. Abstract : While target-based drug discovery strategies rely on the precise knowledge of the identity and function of the drug targets, phenotypic drug discovery (PDD) approaches allow the identification of novel drugs based on knowledge of a distinct phenotype. Image-based high-content screening (HCS) is a potent PDD strategy that characterizes small-molecule effects through the quantification of features that depict cellular changes among or within cell populations, thereby generating valuable data sets for subsequent data analysis. However, these data can be complex, making image analysis from large HCS campaigns challenging. Technological advances in image acquisition, processing, and analysis as well as machine-learning (ML) approaches for the analysis of multidimensional data sets have rendered HCS as a viable technology for small-molecule drug discovery. Here, we discuss HCS concepts, current workflows as well as opportunities and challenges of image-based phenotypic screening and data analysis. Teaser: This review highlights concepts of high-content screening and associated image analysis workflows, including multidimensional analyses using machine learning.