The nucleolus is a dynamic subnuclear compartment that has a number of different functions, but its primary role is to coordinate the production and assembly of ribosomes. For well over 100 years, pathologists have used changes in nucleolar number and size to stage diseases such as cancer. New information about the nucleolus' broader role within the cell is leading to the development of drugs which directly target its structure as therapies for disease. Traditionally, it has been difficult to develop high-throughput image analysis pipelines to measure nucleolar changes due to the broad range of morphologies observed. In this study, we describe a simple high-content image analysis algorithm using Harmony software (PerkinElmer), with a PhenoLOGIC™ machine-learning component, that can measure and classify three different nucleolar morphologies based on nucleolin and fibrillarin staining ("normal," "peri-nucleolar rings" and "dispersed"). We have utilized this algorithm to determine the changes in these classes of nucleolar morphologies over time with drugs known to alter nucleolar structure. This approach could be further adapted to include other parameters required for the identification of new therapies that directly target the nucleolus.
Keywords: high-content analysis; high-throughput screening; nucleolar disruption; nucleolar surveillance; nucleolus.