Single Object Profiles Regression Analysis (SOPRA): A novel method for analyzing high content cell-based screens

Abstract

AbstractMotivationHigh content screening (HCS) experiments generate complex data from multiple object features for each cell within a treated population. Usually these data are analyzed by using population-averaged values of the features of interest, increasing the amount of false positives and the need for intensive follow-up validation. Therefore, there is a strong need for novel approaches with reproducible hit prediction by identifying significantly altered cell populations.ResultsHere we describe SOPRA, a workflow for analyzing image-based HCS data based on regression analysis of non-averaged object features from cell populations, which can be run on hundreds of samples using different cell features. Following plate-wise normalization the values are counted within predetermined binning intervals, generating unique frequency distribution profiles (histograms) for each population, which are then normalized to control populations. Statistically significant differences are identified using a regression model approach. Significantly changed profiles can be used to generate a heatmap from which altered cell populations with similar phenotypes are identified, enabling detection of siRNAs and compounds with the same ‘on-target’ profile, reducing the number of false positive hits. A screen for cell cycle progression was used to validate the workflow, which identified statistically significant changes induced by siRNA-mediated gene perturbations and chemical inhibitors of different cell cycle stages.