Abstract
RNA interference and morphological profiling—the measurement of thousands of phenotypes from individual cells by microscopy and image analysis—are a potentially powerful combination. We show that morphological profiles of RNAi-induced knockdown using the Cell Painting assay are in fact highly sensitive and reproducible. However, we find that the magnitude and prevalence of off-target effects via the RNAi seed-based mechanism make morphological profiles of RNAi reagents targeting the same gene look no more similar than reagents targeting different genes. Pairs of RNAi reagents that share the same seed sequence produce image-based profiles that are much more similar to each other than profiles from pairs designed to target the same gene, a phenomenon previously observed in small-scale gene-expression profiling experiments. Various strategies have been used to enrich on-target versus off-target effects in the context of RNAi screening where a narrow set of phenotypes are measured, mostly based on comparing multiple sequences targeting the same gene; however, new approaches will be needed to make RNAi morphological profiling (that is, comparing multi-dimensional phenotypes) viable. We have shared our raw data and computational pipelines to facilitate research.
Highlights
The systematic perturbation of genes by RNA interference has been used to identify novel players in many biological processes and pathways [1,2,3]
We targeted a set of 41 genes in the U2OS cell line using short-hairpin RNAs in arrayed, 384-well format
While the presence of seed-sequence—driven off-target effects is not surprising, as they were discovered more than a decade ago [3,41,48], the magnitude of the problem had not previously been quantified across multiple genes in the context of high-dimensional RNAi profiling, when using imaging
Summary
The systematic perturbation of genes by RNA interference has been used to identify novel players in many biological processes and pathways [1,2,3]. Off-target effects of RNA interference reagents are known to complicate such experiments [4,5,6], many findings have been thoroughly validated. Only one or two morphological features of cells are measured from images in order to score samples [10]. Much more information can be extracted from images, making it a “high-content” data source. Multiple stains can be employed to visualize a range of cellular components, enabling hundreds of cellular morphology features to be measured at a single-cell level in a single assay
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