High-Content Screening for Quantitative Cell Biology

HCS combines automated microscopy with quantitative image analysis. Recent hardware advances and innovations in software for automated image analysis now allow researchers to rapidly screen and analyze hundreds of thousands of images. In contrast to early analysis of high-throughput imaging data, which often involved testing for deviation of a single parameter, machine learning, both supervised and unsupervised, allows high-dimensional data analysis. The image analysis pipeline must be designed simultaneously with the development of the biological assay. HCS has been used to identify genes and activities required for a specific biological process and in various disease models, to identify proteome-wide changes in response to chemical or genetic perturbations, and in chemical and genetic profiling. High-content screening (HCS), which combines automated fluorescence microscopy with quantitative image analysis, allows the acquisition of unbiased multiparametric data at the single cell level. This approach has been used to address diverse biological questions and identify a plethora of quantitative phenotypes of varying complexity in numerous different model systems. Here, we describe some recent applications of HCS, ranging from the identification of genes required for specific biological processes to the characterization of genetic interactions. We review the steps involved in the design of useful biological assays and automated image analysis, and describe major challenges associated with each. Additionally, we highlight emerging technologies and future challenges, and discuss how the field of HCS might be enhanced in the future. High-content screening (HCS), which combines automated fluorescence microscopy with quantitative image analysis, allows the acquisition of unbiased multiparametric data at the single cell level. This approach has been used to address diverse biological questions and identify a plethora of quantitative phenotypes of varying complexity in numerous different model systems. Here, we describe some recent applications of HCS, ranging from the identification of genes required for specific biological processes to the characterization of genetic interactions. We review the steps involved in the design of useful biological assays and automated image analysis, and describe major challenges associated with each. Additionally, we highlight emerging technologies and future challenges, and discuss how the field of HCS might be enhanced in the future.