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 multi-parametric 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 an siRNA library. The method uses fluorescent immunostaining of a nuclear antigen, Cyclin A, 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 multi-dimensional 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 signalling contexts.