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  • 1
    Keywords: CANCER ; PATHWAY ; TOXICITY ; NUCLEUS ; INHIBITORS ; DYSFUNCTION ; SYNTHETIC LETHALITY ; MUTANT HUNTINGTIN ; HUNTINGTONS-DISEASE MUTATION
    Abstract: Cell-based high-throughput RNAi screening has become a powerful research tool in addressing a variety of biological questions. In RNAi screening, one of the most commonly applied assay system is measuring the fitness of cells that is usually quantified using fluorescence, luminescence and absorption-based readouts. These methods, typically implemented and scaled to large-scale screening format, however often only yield limited information on the cell fitness phenotype due to evaluation of a single and indirect physiological indicator. To address this problem, we have established a cell fitness multiplexing assay which combines a biochemical approach and two fluorescence-based assaying methods. We applied this assay in a large-scale RNAi screening experiment with siRNA pools targeting the human kinome in different modified HEK293 cell lines. Subsequent analysis of ranked fitness phenotypes assessed by the different assaying methods revealed average phenotype intersections of 50.7 +/- 2.3%-58.7 +/- 14.4% when two indicators were combined and 40-48% when a third indicator was taken into account. From these observations we conclude that combination of multiple fitness measures may decrease false-positive rates and increases confidence for hit selection. Our robust experimental and analytical method improves the classical approach in terms of time, data comprehensiveness and cost
    Type of Publication: Journal article published
    PubMed ID: 22162763
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  • 2
    Keywords: IN-VITRO ; PATHWAY ; TOXICITY ; GENE-TRANSFER ; MAMMALIAN-CELLS ; SMALL INTERFERING RNAS ; SHORT HAIRPIN RNAS ; MICRORNA EXPRESSION ; DICER ; VIRUS-REPLICATION
    Abstract: As the only mammalian Argonaute protein capable of directly cleaving mRNAs in a small RNA-guided manner, Argonaute-2 (Ago2) is a keyplayer in RNA interference (RNAi) silencing via small interfering (si) or short hairpin (sh) RNAs. It is also a rate-limiting factor whose saturation by si/shRNAs limits RNAi efficiency and causes numerous adverse side effects. Here, we report a set of versatile tools and widely applicable strategies for transient or stable Ago2 co-expression, which overcome these concerns. Specifically, we engineered plasmids and viral vectors to co-encode a codon-optimized human Ago2 cDNA along with custom shRNAs. Furthermore, we stably integrated this Ago2 cDNA into a panel of standard human cell lines via plasmid transfection or lentiviral transduction. Using various endo- or exogenous targets, we demonstrate the potential of all three strategies to boost mRNA silencing efficiencies in cell culture by up to 10-fold, and to facilitate combinatorial knockdowns. Importantly, these robust improvements were reflected by augmented RNAi phenotypes and accompanied by reduced off-targeting effects. We moreover show that Ago2/shRNA-co-encoding vectors can enhance and prolong transgene silencing in livers of adult mice, while concurrently alleviating hepatotoxicity. Our customizable reagents and avenues should broadly improve future in vitro and in vivo RNAi experiments in mammalian systems.
    Type of Publication: Journal article published
    PubMed ID: 24049077
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  • 3
    Keywords: EXPRESSION ; PATHWAY ; PROTEIN ; COMPONENTS ; PHOSPHORYLATION ; IDENTIFICATION ; CANCER-CELLS ; INTERFERENCE ; AUTOMATED MICROSCOPY ; GENOME-WIDE RNAI
    Abstract: Background: Discovering functional relationships of genes through cell-based phenotyping has become an important approach in functional genomics. High-throughput imaging offers the ability to quantitatively assess complex phenotypes after perturbation by RNA interference (RNAi). Such image-based high-throughput RNAi screening studies have facilitated the discovery of novel components of gene networks and their interactions. Images generated by automated microscopy are typically analyzed by extracting quantitative features of individual cells, resulting in large multidimensional data sets. Robust and sensitive methods to interpret these data sets and to derive biologically relevant information in a high-throughput and unbiased manner remain to be developed. Results: Here we propose a new analysis method, PhenoDissim, which computes the phenotypic dissimilarity between cell populations via Support Vector Machine classification and cross validation. Applying this method to a kinome RNAi screening data set, we demonstrate that the proposed method shows a good replicate reproducibility, separation of controls and clustering quality, and we are able to identify siRNA phenotypes and discover potential functional links between genes. Conclusions: PhenoDissim is a novel analysis method for image-based high-throughput screen, relying on two parameters which can be automatically optimized without a priori knowledge. PhenoDissim is freely available as an R package.
    Type of Publication: Journal article published
    PubMed ID: 24256072
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