RNAi screening reveals proteasome- and Cullin3-dependent stages in vaccinia virus infection.

A two-step, automated, high-throughput RNAi silencing screen was used to identify host cell factors required during vaccinia virus infection. Validation and analysis of clustered hits revealed previously unknown processes during virus entry, including a mechanism for genome uncoating. Viral core proteins were found to be already ubiquitinated during virus assembly. After entering the cytosol of an uninfected cell, the viral DNA was released from the core through the activity of the cell's proteasomes. Next, a Cullin3-based ubiquitin ligase mediated a further round of ubiquitination and proteasome action. This was needed in order to initiate viral DNA replication. The results accentuate the value of large-scale RNAi screens in providing directions for detailed cell biological investigation of complex pathways. The list of cell functions required during poxvirus infection will, moreover, provide a resource for future virus-host cell interaction studies and for the discovery of antivirals.

Single-cell analysis of population context advances RNAi screening at multiple levels.

Isogenic cells in culture show strong variability, which arises from dynamic adaptations to the microenvironment of individual cells. Here we study the influence of the cell population context, which determines a single cell's microenvironment, in image-based RNAi screens. We developed a comprehensive computational approach that employs Bayesian and multivariate methods at the single-cell level. We applied these methods to 45 RNA interference screens of various sizes, including 7 druggable genome and 2 genome-wide screens, analysing 17 different mammalian virus infections and four related cell physiological processes. Analysing cell-based screens at this depth reveals widespread RNAi-induced changes in the population context of individual cells leading to indirect RNAi effects, as well as perturbations of cell-to-cell variability regulators. We find that accounting for indirect effects improves the consistency between siRNAs targeted against the same gene, and between replicate RNAi screens performed in different cell lines, in different labs, and with different siRNA libraries. In an era where large-scale RNAi screens are increasingly performed to reach a systems-level understanding of cellular processes, we show that this is often improved by analyses that account for and incorporate the single-cell microenvironment.

RNAi screen of Salmonella invasion shows role of COPI in membrane targeting of cholesterol and Cdc42.

The pathogen Salmonella Typhimurium is a common cause of diarrhea and invades the gut tissue by injecting a cocktail of virulence factors into epithelial cells, triggering actin rearrangements, membrane ruffling and pathogen entry. One of these factors is SopE, a G-nucleotide exchange factor for the host cellular Rho GTPases Rac1 and Cdc42. How SopE mediates cellular invasion is incompletely understood. Using genome-scale RNAi screening we identified 72 known and novel host cell proteins affecting SopE-mediated entry. Follow-up assays assigned these 'hits' to particular steps of the invasion process; i.e., binding, effector injection, membrane ruffling, membrane closure and maturation of the Salmonella-containing vacuole. Depletion of the COPI complex revealed a unique effect on virulence factor injection and membrane ruffling. Both effects are attributable to mislocalization of cholesterol, sphingolipids, Rac1 and Cdc42 away from the plasma membrane into a large intracellular compartment. Equivalent results were obtained with the vesicular stomatitis virus. Therefore, COPI-facilitated maintenance of lipids may represent a novel, unifying mechanism essential for a wide range of pathogens, offering opportunities for designing new drugs.

CellClassifier: supervised learning of cellular phenotypes.

UNLABELLED: CellClassifier is a tool for classifying single-cell phenotypes in microscope images. It includes several unique and user-friendly features for classification using multiclass support vector machines AVAILABILITY: Source code, user manual and SaveObjectSegmentation CellProfiler module available for download at www.cellclassifier.ethz.ch under the GPL license (implemented in Matlab).

Population context determines cell-to-cell variability in endocytosis and virus infection.

Single-cell heterogeneity in cell populations arises from a combination of intrinsic and extrinsic factors. This heterogeneity has been measured for gene transcription, phosphorylation, cell morphology and drug perturbations, and used to explain various aspects of cellular physiology. In all cases, however, the causes of heterogeneity were not studied. Here we analyse, for the first time, the heterogeneous patterns of related cellular activities, namely virus infection, endocytosis and membrane lipid composition in adherent human cells. We reveal correlations with specific cellular states that are defined by the population context of a cell, and we derive probabilistic models that can explain and predict most cellular heterogeneity of these activities, solely on the basis of each cell's population context. We find that accounting for population-determined heterogeneity is essential for interpreting differences between the activity levels of cell populations. Finally, we reveal that synergy between two molecular components, focal adhesion kinase and the sphingolipid GM1, enhances the population-determined pattern of simian virus 40 (SV40) infection. Our findings provide an explanation for the origin of heterogeneity patterns of cellular activities in adherent cell populations.

Lessons from genetics: interpreting complex phenotypes in RNAi screens.

Mammalian cell biology is witnessing a new era in which cellular processes are explained through dynamic networks of interacting cellular components. In this fast-pacing field, where image-based RNAi screening is taking a central role, there is a strong need to improve ways to capture such interactions in space and time. Cell biologists traditionally depict these events by confining themselves to the level of a single cell, or to many population-averaged cells. Similarly, classical geneticists observe and interpret phenotypes in a single organism to delineate signaling processes, but have also described genetic phenomena in populations of organisms. The analogy in the two approaches inspired us to draw parallels with, and take lessons from concepts in classical genetics.