web cellHTS2: A web-application for the analysis of high-throughput screening data

Background  

The analysis of high-throughput screening data sets is an expanding field in bioinformatics. High-throughput screens by RNAi generate large primary data sets which need to be analyzed and annotated to identify relevant phenotypic hits. Large-scale RNAi screens are frequently used to identify novel factors that influence a broad range of cellular processes, including signaling pathway activity, cell proliferation, and host cell infection. Here, we present a web-based application utility for the end-to-end analysis of large cell-based screening experiments by cellHTS2.     

Automated genome-wide visual profiling of cellular proteins involved in HIV infection

Recent genome-wide RNAi screens have identified >842 human genes that affect the human immunodeficiency virus (HIV) cycle. The list of genes implicated in infection differs between screens, and there is minimal overlap. A reason for this variance is the interdependence of HIV infection and host cell function, producing a multitude of indirect or pleiotropic cellular effects affecting the viral infection during RNAi screening. To overcome this, the authors devised a 15-dimensional phenotypic profile to define the viral infection block induced by CD4 silencing in HeLa cells. They demonstrate that this phenotypic profile excludes nonspecific, RNAi-based side effects and viral replication defects mediated by silencing of housekeeping genes. To achieve statistical robustness, the authors used automatically annotated RNAi arrays for seven independent genome-wide RNAi screens. This identified 56 host genes, which reliably reproduced CD4-like phenotypes upon HIV infection. The factors include 11 known HIV interactors and 45 factors previously not associated with HIV infection. As proof of concept, the authors confirmed that silencing of PAK1, Ku70, and RNAseH2A impaired HIV replication in Jurkat cells. In summary, multidimensional, visual profiling can identify genes required for HIV infection.     

High-throughput RNAi screening to dissect cellular pathways: A how-to guide

RNA interference (RNAi) has become a powerful tool to dissect cellular pathways and characterize gene functions. The availability of genome-wide RNAi libraries for various model organisms and mammalian cells has enabled high-throughput RNAi screenings. These RNAi screens successfully identified key components that had previously been missed in classical forward genetic screening approaches and allowed the assessment of combined loss-of-function phenotypes. Crucially, the quality of RNAi screening results depends on quantitative assays and the choice of the right biological context. In this review, we provide an overview on the design and application of high-throughput RNAi screens as well as data analysis and candidate validation strategies.     

Drosophila RNAi screening in a postgenomic world

Drosophila melanogaster has a long history as a model organism with several unique features that make it an ideal research tool for the study of the relationship between genotype and phenotype. Importantly fundamental genetic principles as well as key human disease genes have been uncovered through the use of Drosophila. The contribution of the fruit fly to science and medicine continues in the postgenomic era as cell-based Drosophila RNAi screens are a cost-effective and scalable enabling technology that can be used to quantify the contribution of different genes to diverse cellular processes. Drosophila high-throughput screens can also be used as integral part of systems-level approaches to describe the architecture and dynamics of cellular networks.     

Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets

Background  

RNA-mediated interference (RNAi)-based functional genomics is a systems-level approach to identify novel genes that control biological phenotypes. Existing computational approaches can identify individual genes from RNAi datasets that regulate a given biological process. However, currently available methods cannot identify which RNAi screen "hits" are novel components of well-characterized biological pathways known to regulate the interrogated phenotype. In this study, we describe a method to identify genes from RNAi datasets that are novel components of known biological pathways. We experimentally validate our approach in the context of a recently completed RNAi screen to identify novel regulators of melanogenesis.     

Cellular phenotype recognition for high-content RNA interference genome-wide screening

Genome-wide, cell-based screens using high-content screening (HCS) techniques and automated fluorescence microscopy generate thousands of high-content images that contain an enormous wealth of cell biological information. Such screens are key to the analysis of basic cell biological principles, such as control of cell cycle and cell morphology. However, these screens will ultimately only shed light on human disease mechanisms and potential cures if the analysis can keep up with the generation of data. A fundamental step toward automated analysis of high-content screening is to construct a robust platform for automatic cellular phenotype identification. The authors present a framework, consisting of microscopic image segmentation and analysis components, for automatic recognition of cellular phenotypes in the context of the Rho family of small GTPases. To implicate genes involved in Rac signaling, RNA interference (RNAi) was used to perturb gene functions, and the corresponding cellular phenotypes were analyzed for changes. The data used in the experiments are high-content, 3-channel, fluorescence microscopy images of Drosophila Kc167 cultured cells stained with markers that allow visualization of DNA, polymerized actin filaments, and the constitutively activated Rho protein Rac(V12). The performance of this approach was tested using a cellular database that contained more than 1000 samples of 3 predefined cellular phenotypes, and the generalization error was estimated using a cross-validation technique. Moreover, the authors applied this approach to analyze the whole high-content fluorescence images of Drosophila cells for further HCS-based gene function analysis.     

Loss of the putative RNA-directed RNA polymerase RRF-3 makes C. elegans hypersensitive to RNAi

RNA interference (RNAi) is a broadly used reverse genetics method in C. elegans. Unfortunately, RNAi does not inhibit all genes. We show that loss of function of a putative RNA-directed RNA polymerase (RdRP) of C. elegans, RRF-3, results in a substantial enhancement of sensitivity to RNAi in diverse tissues. This is particularly striking in the nervous system; neurons that are generally refractory to RNAi in a wild-type genetic background can respond effectively to interference in an rrf-3 mutant background. These data provide the first indication of physiological negative modulation of the RNAi response and implicate an RdRP-related factor in this effect. The rrf-3 strain can be useful to study genes that, in wild-type, do not show a phenotype after RNAi, and it is probably the strain of choice for genome-wide RNAi screens.     

Validation of RNAi silencing specificity using synthetic genes: salicylic acid-binding protein 2 is required for innate immunity in plants

RNA interference (RNAi) is widely used to specifically silence the expression of any gene to study its function and to identify and validate therapeutic targets. Despite the popularity of this technology, recent studies have shown that RNAi may also silence non-targeted genes. Here we demonstrate the utility of a quick, efficient and robust approach to directly validate the specificity of RNAi as an alternative to indirect validation of RNAi through gene expression profiling. Our approach involves reversing (complementing) the RNAi-induced phenotype by introducing a synthetic version of the target gene that is designed to escape silencing. This synthetic gene complementation approach can also be used for mutational analysis of the target gene, or to provide a functional version of a defective protein after silencing the defective gene by RNAi. Using this approach we demonstrate that the loss of systemic acquired resistance, a form of innate immunity in plants, is indeed due to the silencing of salicylic acid-binding protein 2 rather than to off-target effects.     

RNAi screening to identify postembryonic phenotypes in C. elegans

C. elegans has proven to be a valuable model system for the discovery and functional characterization of many genes and gene pathways. More sophisticated tools and resources for studies in this system are facilitating continued discovery of genes with more subtle phenotypes or roles. Here we present a generalized protocol we adapted for identifying C. elegans genes with postembryonic phenotypes of interest using RNAi. This procedure is easily modified to assay the phenotype of choice, whether by light or fluorescence optics on a dissecting or compound microscope. This screening protocol capitalizes on the physical assets of the organism and molecular tools the C. elegans research community has produced. As an example, we demonstrate the use of an integrated transgene that expresses a fluorescent product in an RNAi screen to identify genes required for the normal localization of this product in late stage larvae and adults. First, we used a commercially available genomic RNAi library with full-length cDNA inserts. This library facilitates the rapid identification of multiple candidates by RNAi reduction of the candidate gene product. Second, we generated an integrated transgene that expresses our fluorecently tagged protein of interest in an RNAi-sensitive background. Third, by exposing hatched animals to RNAi, this screen permits identification of gene products that have a vital embryonic role that would otherwise mask a post-embryonic role in regulating the protein of interest. Lastly, this screen uses a compound microscope equipped for single cell resolution.     

Identification of Drosophila mutants altering defense of and endurance to Listeria monocytogenes infection

We extended the use of Drosophila beyond being a model for signaling pathways required for pattern recognition immune signaling and show that the fly can be used to identify genes required for pathogenesis and host-pathogen interactions. We performed a forward genetic screen to identify Drosophila mutations altering sensitivity to the intracellular pathogen Listeria monocytogenes. We recovered 18 mutants with increased susceptibility to infection, none of which were previously shown to function in a Drosophila immune response. Using secondary screens, we divided these mutants into two groups: In the first group, mutants have reduced endurance to infections but show no change in bacterial growth. This is a new fly immunity phenotype that is not commonly studied. In the second group, mutants have a typical defense defect in which bacterial growth is increased and survival is decreased. By further challenging mutant flies with L. monocytogenes mutants, we identified subgroups of fly mutants that affect specific stages of the L. monocytogenes life cycle, exit from the vacuole, or actin-based movement. There is no overlap between our genes and the hundreds of genes identified in Drosophila S2 cells fighting L. monocytogenes infection, using genomewide RNAi screens in vitro. By using a whole-animal model and screening for host survival, we revealed genes involved in physiologies different from those that were found in previous screens, which all had defects in defensive immune signaling.     

Empirical evaluation of data transformations and ranking statistics for microarray analysis

There are many options in handling microarray data that can affect study conclusions, sometimes drastically. Working with a two-color platform, this study uses ten spike-in microarray experiments to evaluate the relative effectiveness of some of these options for the experimental goal of detecting differential expression. We consider two data transformations, background subtraction and intensity normalization, as well as six different statistics for detecting differentially expressed genes. Findings support the use of an intensity-based normalization procedure and also indicate that local background subtraction can be detrimental for effectively detecting differential expression. We also verify that robust statistics outperform t-statistics in identifying differentially expressed genes when there are few replicates. Finally, we find that choice of image analysis software can also substantially influence experimental conclusions.     

Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation

The advent of genome-wide RNA interference (RNAi)–based screens puts us in the position to identify genes for all functions human cells carry out. However, for many functions, assay complexity and cost make genome-scale knockdown experiments impossible. Methods to predict genes required for cell functions are therefore needed to focus RNAi screens from the whole genome on the most likely candidates. Although different bioinformatics tools for gene function prediction exist, they lack experimental validation and are therefore rarely used by experimentalists. To address this, we developed an effective computational gene selection strategy that represents public data about genes as graphs and then analyzes these graphs using kernels on graph nodes to predict functional relationships. To demonstrate its performance, we predicted human genes required for a poorly understood cellular function—mitotic chromosome condensation—and experimentally validated the top 100 candidates with a focused RNAi screen by automated microscopy. Quantitative analysis of the images demonstrated that the candidates were indeed strongly enriched in condensation genes, including the discovery of several new factors. By combining bioinformatics prediction with experimental validation, our study shows that kernels on graph nodes are powerful tools to integrate public biological data and predict genes involved in cellular functions of interest.     

A large-scale RNAi screen identifies functional classes of genes shaping synaptic development and maintenance

Neuronal circuit development and function require proper synapse formation and maintenance. Genetic screens are one powerful method to identify the mechanisms shaping synaptic development and stability. However, genes with essential roles in non-neural tissues may be missed in traditional loss-of-function screens. In an effort to circumvent this limitation, we used neuron-specific RNAi knock down in Drosophila and assayed the formation, growth, and maintenance of the neuromuscular junction (NMJ). We examined 1970 Drosophila genes, each of which has a conserved ortholog in mammalian genomes. Knock down of 158 genes in post-mitotic neurons led to abnormalities in the neuromuscular system, including misapposition of active zone components opposite postsynaptic glutamate receptors, synaptic terminal overgrowth and undergrowth, abnormal accumulation of synaptic material within the axon, and retraction of synaptic terminals from their postsynaptic targets. Bioinformatics analysis demonstrates that genes with overlapping annotated function are enriched within the hits for each phenotype, suggesting that the shared biological function is important for that aspect of synaptic development. For example, genes for proteasome subunits and mitotic spindle organizers are enriched among the genes whose knock down leads to defects in synaptic apposition and NMJ stability. Such genes play essential roles in all cells, however the use of tissue- and temporally-restricted RNAi indicates that the proteasome and mitotic spindle organizers participate in discrete aspects of synaptic development. In addition to identifying functional classes of genes shaping synaptic development, this screen also identifies candidate genes whose role at the synapse can be validated by traditional loss-of-function analysis. We present one such example, the dynein-interacting protein NudE, and demonstrate that it is required for proper axonal transport and synaptic maintenance. Thus, this screen has identified both functional classes of genes as well as individual candidate genes that are critical for synaptic development and will be a useful resource for subsequent mechanistic analysis of synapse formation and maintenance.     

Genetic Annotation of Gain-Of-Function Screens Using RNA Interference and in Situ Hybridization of Candidate Genes in the Drosophila Wing

Gain-of-function screens in Drosophila are an effective method with which to identify genes that affect the development of particular structures or cell types. It has been found that a fraction of 2–10% of the genes tested, depending on the particularities of the screen, results in a discernible phenotype when overexpressed. However, it is not clear to what extent a gain-of-function phenotype generated by overexpression is informative about the normal function of the gene. Thus, very few reports attempt to correlate the loss- and overexpression phenotype for collections of genes identified in gain-of-function screens. In this work we use RNA interference and in situ hybridization to annotate a collection of 123 P-GS insertions that in combination with different Gal4 drivers affect the size and/or patterning of the wing. We identify the gene causing the overexpression phenotype by expressing, in a background of overexpression, RNA interference for the genes affected by each P-GS insertion. Then, we compare the loss and gain-of-function phenotypes obtained for each gene and relate them to its expression pattern in the wing disc. We find that 52% of genes identified by their overexpression phenotype are required during normal development. However, only in 9% of the cases analyzed was there some complementarity between the gain- and loss-of-function phenotype, suggesting that, in general, the overexpression phenotypes would not be indicative of the normal requirements of the gene.     

Analysis of cell-based RNAi screens

RNA interference (RNAi) screening is a powerful technology for functional characterization of biological pathways. Interpretation of RNAi screens requires computational and statistical analysis techniques. We describe a method that integrates all steps to generate a scored phenotype list from raw data. It is implemented in an open-source Bioconductor/R package, cellHTS (). The method is useful for the analysis and documentation of individual RNAi screens. Moreover, it is a prerequisite for the integration of multiple experiments.     

Genome-scale loss-of-function screening with a lentiviral RNAi library

The discovery that RNA interference (RNAi) is functional in mammalian cells led us to form The RNAi Consortium (TRC) with the goal of enabling large-scale loss-of-function screens through the development of genome-scale RNAi libraries and methodologies for their use. These resources form the basis of a loss-of-function screening platform created at the Broad Institute. Our human and mouse libraries currently contain >135,000 lentiviral clones targeting 27,000 genes. Initial screening efforts have demonstrated that these libraries and methods are practical and powerful tools for high-throughput lentivirus RNAi screens.     

Systematic approaches to dissect biological processes in stem cells by image-based screening

High-throughput RNAi or small molecule screens have proven to be powerful methodologies for the systematic dissection of cellular processes. In model organisms and cell lines, large-scale screens have identified key components of many cellular pathways and helped to identify novel targets in disease-relevant pathways. Image-based high-content screening has become an increasingly important tool in high-throughput screening, enabling changes in phenotype characteristics, such as cell morphology and cell differentiation, to be monitored. In this review, we discuss the use of image-based screening approaches to explore the behavior of adult, embryonic, and induced pluripotent stem cells. First, we review how current pluripotency and differentiation assays can be adapted to high-throughput formats. We then describe general aspects of image-based screening of cells and present an outlook on challenges for screening stem cells.