High-Content Imaging and RNAi Screens for Investigating Kinase Network Plasticity

Abstract

High-content imaging connects the information-rich method of microscopy with the systematic objective principles of software-driven analysis. Suited to automation and, therefore, considerable scale-up of study size, this approach can deliver multiparametric data over cell populations or at the level of the individual cell and has found considerable utility in reverse genetic and pharmacological screens. Here we present a method to screen small interfering RNA (siRNA) libraries allowing subsequent observation of the impact of each knockdown on two interlinked, high-content, G1-/S-phase cell cycle transition assays related to cyclin-dependent kinase (CDK) 2 activity. We show how plasticity within the network governing the activity of this kinase can be detected by combining modifier siRNAs with a siRNA library. The method uses fluorescent immunostaining of a nuclear antigen, CyclinA, following cell fixation while also preserving the fluorescence of a stably expressed fluorescent protein-tagged reporter for CDK2 activity. We provide methodology for data extraction and handling including an R-script that converts the multidimensional data into four simple binary outcomes, on which a hit-mining strategy can be built. The workflow described can in principle be adopted to yield quantitative single-cell-resolved data and mining for outcomes relating to a broad range of other similar readouts and signaling contexts.