RefCell: Multi-dimensional analysis of image-based high-throughput screens based on ‘typical cells’
BackgroundImage-based high-throughput screening (HTS) reveals a high level of heterogeneity in single cells and multiple cellular states may be observed within a single population. Cutting-edge high-dimensional analysis methods are successful in characterizing cellular heterogeneity, but they suffer from the \"curse of dimensionality\" and non-standardized outputs.\n\nResultsHere we introduce RefCell, a multi-dimensional analysis pipeline for image-based HTS that reproducibly captures cells with typical combinations of features in reference states, and uses these \"typical cells\" as a reference for classification and weighting of metrics. RefCell quantitatively assesses the heterogeneous deviations from typical behavior for each analyzed perturbation or sample.\n\nConclusionsWe apply RefCell to the analysis of data from a high-throughput imaging screen of a library of 320 ubiquitin protein targeted siRNAs selected to gain insights into the mechanisms of premature aging (progeria). RefCell yields results comparable to a more complex clustering based single cell analysis method, which both reveal more potential hits than conventional average based analysis.