Related Articles

RNA interference is a powerful method to understand gene function, especially when conducted at a whole-genome scale and in a quantitative context. In C. elegans, gene function can be knocked down simply and efficiently by feeding worms with bacteria expressing a dsRNA corresponding to a specific gene 1. While the creation of libraries of RNAi clones covering most of the C. elegans genome 2,3 opened the way for true functional genomic studies (see for example 4-7), most established methods are laborious. Moy and colleagues have developed semi-automated protocols that facilitate genome-wide screens 8. The approach relies on microscopic imaging and image analysis.

Here we describe an alternative protocol for a high-throughput genome-wide screen, based on robotic handling of bacterial RNAi clones, quantitative analysis using the COPAS Biosort (Union Biometrica (UBI)), and an integrated software: the MBioLIMS (Laboratory Information Management System from Modul-Bio) a technology that provides increased throughput for data management and sample tracking. The method allows screens to be conducted on solid medium plates. This is particularly important for some studies, such as those addressing host-pathogen interactions in C. elegans, since certain microbes do not efficiently infect worms in liquid culture.

We show how the method can be used to quantify the importance of genes in anti-fungal innate immunity in C. elegans. In this case, the approach relies on the use of a transgenic strain carrying an epidermal infection-inducible fluorescent reporter gene, with GFP under the control of the promoter of the antimicrobial peptide gene nlp 29 and a red fluorescent reporter that is expressed constitutively in the epidermis. The latter provides an internal control for the functional integrity of the epidermis and nonspecific transgene silencing9. When control worms are infected by the fungus they fluoresce green. Knocking down by RNAi a gene required for nlp 29 expression results in diminished fluorescence after infection. Currently, this protocol allows more than 3,000 RNAi clones to be tested and analyzed per week, opening the possibility of screening the entire genome in less than 2 months.

Summary

Image-based, high throughput genome-wide RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Automated screening of such experiments generates a large number of images with great variations in image quality, which makes manual analysis unreasonably time-consuming. Therefore, effective techniques for automatic image analysis are urgently needed, in which segmentation is one of the most important steps. This paper proposes a fully automatic method for cells segmentation in genome-wide RNAi screening images. The method consists of two steps: nuclei and cytoplasm segmentation. Nuclei are extracted and labelled to initialize cytoplasm segmentation. Since the quality of RNAi image is rather poor, a novel scale-adaptive steerable filter is designed to enhance the image in order to extract long and thin protrusions on the spiky cells. Then, constraint factor GCBAC method and morphological algorithms are combined to be an integrated method to segment tight clustered cells. Compared with the results obtained by using seeded watershed and the ground truth, that is, manual labelling results by experts in RNAi screening data, our method achieves higher accuracy. Compared with active contour methods, our method consumes much less time. The positive results indicate that the proposed method can be applied in automatic image analysis of multi-channel image screening data.

Background

The recently developed RNA interference (RNAi) technology has created an unprecedented opportunity which allows the function of individual genes in whole organisms or cell lines to be interrogated at genome-wide scale. However, multiple issues, such as off-target effects or low efficacies in knocking down certain genes, have produced RNAi screening results that are often noisy and that potentially yield both high rates of false positives and false negatives. Therefore, integrating RNAi screening results with other information, such as protein-protein interaction (PPI), may help to address these issues.

Results

By analyzing 24 genome-wide RNAi screens interrogating various biological processes in Drosophila, we found that RNAi positive hits were significantly more connected to each other when analyzed within a protein-protein interaction network, as opposed to random cases, for nearly all screens. Based on this finding, we developed a network-based approach to identify false positives (FPs) and false negatives (FNs) in these screening results. This approach relied on a scoring function, which we termed NePhe, to integrate information obtained from both PPI network and RNAi screening results. Using a novel rank-based test, we compared the performance of different NePhe scoring functions and found that diffusion kernel-based methods generally outperformed others, such as direct neighbor-based methods. Using two genome-wide RNAi screens as examples, we validated our approach extensively from multiple aspects. We prioritized hits in the original screens that were more likely to be reproduced by the validation screen and recovered potential FNs whose involvements in the biological process were suggested by previous knowledge and mutant phenotypes. Finally, we demonstrated that the NePhe scoring system helped to biologically interpret RNAi results at the module level.

Conclusion

By comprehensively analyzing multiple genome-wide RNAi screens, we conclude that network information can be effectively integrated with RNAi results to produce suggestive FPs and FNs, and to bring biological insight to the screening results.

While genetic screens have identified many genes essential for neurite outgrowth, they have been limited in their ability to identify neural genes that also have earlier critical roles in the gastrula, or neural genes for which maternally contributed RNA compensates for gene mutations in the zygote. To address this, we developed methods to screen the Drosophila genome using RNA-interference (RNAi) on primary neural cells and present the results of the first full-genome RNAi screen in neurons. We used live-cell imaging and quantitative image analysis to characterize the morphological phenotypes of fluorescently labelled primary neurons and glia in response to RNAi-mediated gene knockdown. From the full genome screen, we focused our analysis on 104 evolutionarily conserved genes that when downregulated by RNAi, have morphological defects such as reduced axon extension, excessive branching, loss of fasciculation, and blebbing. To assist in the phenotypic analysis of the large data sets, we generated image analysis algorithms that could assess the statistical significance of the mutant phenotypes. The algorithms were essential for the analysis of the thousands of images generated by the screening process and will become a valuable tool for future genome-wide screens in primary neurons. Our analysis revealed unexpected, essential roles in neurite outgrowth for genes representing a wide range of functional categories including signalling molecules, enzymes, channels, receptors, and cytoskeletal proteins. We also found that genes known to be involved in protein and vesicle trafficking showed similar RNAi phenotypes. We confirmed phenotypes of the protein trafficking genes Sec61alpha and Ran GTPase using Drosophila embryo and mouse embryonic cerebral cortical neurons, respectively. Collectively, our results showed that RNAi phenotypes in primary neural culture can parallel in vivo phenotypes, and the screening technique can be used to identify many new genes that have important functions in the nervous system.

Author Summary

Development and function of the brain requires the coordinated action of thousands of genes, and currently we understand the roles of only a small fraction of them. Recent advances in genomics, such as the sequencing of entire genomes and the discovery of RNA-interference as a means of testing the effects of gene loss, have opened up the possibility to systematically analyze the function of all known and predicted genes in an organism. Until now, this type of functional genomics approach has not been applied to the study of very complex cells, such as the brain's neurons, on a full-genome scale. In this work, we developed techniques to test all genes, one by one in a rapid manner, for their potential role in neuronal development using neurons isolated from fruit fly embryos. These results yielded a global perspective of what types of genes are necessary for brain development; importantly, they show that a large variety of genes can be studied in this way.

Background

A systems biology interpretation of genome-scale RNA interference (RNAi) experiments is complicated by scope, experimental variability and network signaling robustness. Over representation approaches (ORA), such as the Hypergeometric or z-score, are an established statistical framework used to associate RNA interference effectors to biologically annotated gene sets or pathways. These methods, however, do not directly take advantage of our growing understanding of the interactome. Furthermore, these methods can miss partial pathway activation and may be biased by protein complexes. Here we present a novel ORA, protein interaction permutation analysis (PIPA), that takes advantage of canonical pathways and established protein interactions to identify pathways enriched for protein interactions connecting RNAi hits.

Results

We use PIPA to analyze genome-scale siRNA cell growth screens performed in HeLa and TOV cell lines. First we show that interacting gene pair siRNA hits are more reproducible than single gene hits. Using protein interactions, PIPA identifies enriched pathways not found using the standard Hypergeometric analysis including the FAK cytoskeletal remodeling pathway. Different branches of the FAK pathway are distinctly essential in HeLa versus TOV cell lines while other portions are uneffected by siRNA perturbations. Enriched hits belong to protein interactions associated with cell cycle regulation, anti-apoptosis, and signal transduction.

Conclusion

PIPA provides an analytical framework to interpret siRNA screen data by merging biologically annotated gene sets with the human interactome. As a result we identify pathways and signaling hypotheses that are statistically enriched to effect cell growth in human cell lines. This method provides a complementary approach to standard gene set enrichment that utilizes the additional knowledge of specific interactions within biological gene sets.

RNA interference (RNAi) is a powerful method to unravel the role of a given gene in eukaryotic cells. The development of high throughput assay platforms such as fluorescence plate readers and high throughput microscopy has allowed the design of genome wide RNAi screens to systemically discern members of regulatory networks around various cellular processes. Here we summarize the different strategies employed in RNAi screens to reveal regulators of transcriptional networks. We focus our discussion in experimental approaches designed to uncover regulatory interactions modulating transcription factor activity.

FLIGHT (http://flight.icr.ac.uk/) is an online resource compiling data from high-throughput Drosophila in vivo and in vitro RNAi screens. FLIGHT includes details of RNAi reagents and their predicted off-target effects, alongside RNAi screen hits, scores and phenotypes, including images from high-content screens. The latest release of FLIGHT is designed to enable users to upload, analyze, integrate and share their own RNAi screens. Users can perform multiple normalizations, view quality control plots, detect and assign screen hits and compare hits from multiple screens using a variety of methods including hierarchical clustering. FLIGHT integrates RNAi screen data with microarray gene expression as well as genomic annotations and genetic/physical interaction datasets to provide a single interface for RNAi screen analysis and datamining in Drosophila.

Background

Systematic, large-scale RNA interference (RNAi) approaches are very valuable to systematically investigate biological processes in cell culture or in tissues of organisms such as Drosophila. A notorious pitfall of all RNAi technologies are potential false positives caused by unspecific knock-down of genes other than the intended target gene. The ultimate proof for RNAi specificity is a rescue by a construct immune to RNAi, typically originating from a related species.

Methodology/Principal Findings

We show that primary sequence divergence in areas targeted by Drosophila melanogaster RNAi hairpins in five non-melanogaster species is sufficient to identify orthologs for 81% of the genes that are predicted to be RNAi refractory. We use clones from a genomic fosmid library of Drosophila pseudoobscura to demonstrate the rescue of RNAi phenotypes in Drosophila melanogaster muscles. Four out of five fosmid clones we tested harbour cross-species functionality for the gene assayed, and three out of the four rescue a RNAi phenotype in Drosophila melanogaster.

Conclusions/Significance

The Drosophila pseudoobscura fosmid library is designed for seamless cross-species transgenesis and can be readily used to demonstrate specificity of RNAi phenotypes in a systematic manner.

An automated, image-based RNAi screen for cell shape reveals roles for membrane secretion factors in cell spreading.

Recent technological advances in microscopy have enabled cell-based whole genome screens, but the analysis of the vast amount of image data generated by such screens usually proves to be rate limiting. In this study, we performed a whole genome RNA interference (RNAi) screen to uncover genes that affect spreading of Drosophila melanogaster S2 cells using several computational methods for analyzing the image data in an automated manner. Expected genes in the Scar-Arp2/3 actin nucleation pathway were identified as well as casein kinase I, which had a similar morphological RNAi signature. A distinct nonspreading morphological phenotype was identified for genes involved in membrane secretion or synthesis. In this group, we identified a new secretory peptide and investigated the functions of two poorly characterized endoplasmic reticulum proteins that have roles in secretion. Thus, this genome-wide screen succeeded in identifying known and unexpected proteins that are important for cell spreading, and the computational tools developed in this study should prove useful for other types of automated whole genome screens.

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.

Results

In this study, we utilize a PPI network topology-based approach to identify targets within our RNAi dataset that may be components of known melanogenesis regulatory pathways. Our computational approach identifies a set of screen targets that cluster topologically in a human PPI network with the known pigment regulator Endothelin receptor type B (EDNRB). Validation studies reveal that these genes impact pigment production and EDNRB signaling in pigmented melanoma cells (MNT-1) and normal melanocytes.

Conclusions

We present an approach that identifies novel components of well-characterized biological pathways from functional genomics datasets that could not have been identified by existing statistical and computational approaches.

RNA interference (RNAi) has become a popular tool for analyzing gene function in cancer research. The feasibility of using RNAi in cellular and animal models as an alternative to conventional gene knock out approaches has been demonstrated. Although these studies show that RNAi can recapitulate phenotypes seen in knock out animals and their derived cell lines, a systematic study rigorously comparing downstream effector genes between RNAi and gene knock out has not been performed. Here we present data contrasting the phenotypic and genotypic changes that occur with either stable knock down via RNAi of the cyclin dependent kinase inhibitor p21 versus its somatic cell knock out counterpart in the human mammary epithelial cell line MCF-10A. Our results demonstrate that p21 knock down clones display a growth proliferative response upon exposure to Transforming Growth Factor-Beta Type 1 (TGFβ) similar to p21 knock out clones. However, gene expression profiles were significantly different in p21 knock down cells versus p21 knock out clones. Importantly p21 knock down clones did not display increased gene expression of interleukin-1α (IL-1α), a critical effector of this growth response previously validated in p21 knock out cells. We conclude that gene knock out can yield additional vital information that may be missed with gene knock down strategies.

Recently, High-content screening (HCS) has been combined with RNA interference (RNAi) to become an essential image-based high-throughput method for studying genes and biological networks through RNAi-induced cellular phenotype analyses. However, a genome-wide RNAi-HCS screen typically generates tens of thousands of images, most of which remain uncategorized due to the inadequacies of existing HCS image analysis tools. Until now, it still requires highly trained scientists to browse a prohibitively large RNAi-HCS image database and produce only a handful of qualitative results regarding cellular morphological phenotypes. For this reason we have developed intelligent interfaces to facilitate the application of the HCS technology in biomedical research. Our new interfaces empower biologists with computational power not only to effectively and efficiently explore large-scale RNAi-HCS image databases, but also to apply their knowledge and experience to interactive mining of cellular phenotypes using Content-Based Image Retrieval (CBIR) with Relevance Feedback (RF) techniques.

Tribolium resembles C. elegans in showing a robust systemic RNAi response, but does not have C. elegans-type RNAi mechanisms; insect systemic RNAi probably uses a different mechanism.

Background

RNA interference (RNAi) is a highly conserved cellular mechanism. In some organisms, such as Caenorhabditis elegans, the RNAi response can be transmitted systemically. Some insects also exhibit a systemic RNAi response. However, Drosophila, the leading insect model organism, does not show a robust systemic RNAi response, necessitating another model system to study the molecular mechanism of systemic RNAi in insects.

Results

We used Tribolium, which exhibits robust systemic RNAi, as an alternative model system. We have identified the core RNAi genes, as well as genes potentially involved in systemic RNAi, from the Tribolium genome. Both phylogenetic and functional analyses suggest that Tribolium has a somewhat larger inventory of core component genes than Drosophila, perhaps allowing a more sensitive response to double-stranded RNA (dsRNA). We also identified three Tribolium homologs of C. elegans sid-1, which encodes a possible dsRNA channel. However, detailed sequence analysis has revealed that these Tribolium homologs share more identity with another C. elegans gene, tag-130. We analyzed tag-130 mutants, and found that this gene does not have a function in systemic RNAi in C. elegans. Likewise, the Tribolium sid-like genes do not seem to be required for systemic RNAi. These results suggest that insect sid-1-like genes have a different function than dsRNA uptake. Moreover, Tribolium lacks homologs of several genes important for RNAi in C. elegans.

Conclusion

Although both Tribolium and C. elegans show a robust systemic RNAi response, our genome-wide survey reveals significant differences between the RNAi mechanisms of these organisms. Thus, insects may use an alternative mechanism for the systemic RNAi response. Understanding this process would assist with rendering other insects amenable to systemic RNAi, and may influence pest control approaches.

An important application of the RNA interference (RNAi) pathway is its use as a small RNA-based regulatory system commonly exploited to suppress expression of target genes to test their function in vivo. In several published experiments, RNAi has been used to inactivate components of the RNAi pathway itself, a procedure termed recursive RNAi in this report. The theoretical basis of recursive RNAi is unclear since the procedure could potentially be self-defeating, and in practice the effectiveness of recursive RNAi in published experiments is highly variable. A mathematical model for recursive RNAi was developed and used to investigate the range of conditions under which the procedure should be effective. The model predicts that the effectiveness of recursive RNAi is strongly dependent on the efficacy of RNAi at knocking down target gene expression. This efficacy is known to vary highly between different cell types, and comparison of the model predictions to published experimental data suggests that variation in RNAi efficacy may be the main cause of discrepancies between published recursive RNAi experiments in different organisms. The model suggests potential ways to optimize the effectiveness of recursive RNAi both for screening of RNAi components as well as for improved temporal control of gene expression in switch off–switch on experiments.

Author Summary

RNA interference is a gene regulatory system in which small RNA molecules turn off genes that have similar sequences to the small RNAs. This has become a powerful tool because a researcher can use RNA interference to turn off any gene of interest in order to test its function. There is great interest in identifying the genes required for the RNA interference pathway, and one approach to identifying such genes has been to use RNA interference to turn off potential RNA interference genes and to ask whether RNA interference still functions when these genes are turned off. The goal of our report is to ask how it is possible for RNA interference to turn itself off, using a mathematical model of the system. The results show that RNA interference cannot turn itself off if the RNA interference pathway is too effective to start with, so that experiments in which RNA interference acts on itself will only work in systems having a low efficiency. The results of our model suggest possible ways to improve the self-inactivation of RNA interference.

The phenomenon that is known as RNA mediated interference (RNAi) was first observed in the nematode C. elegans. The application of RNAi has now been widely disseminated and the mechanisms underlying the pathway have been uncovered using both genetics and biochemistry. In the worm, it has been demonstrated that RNAi is easily adapted to high throughput analysis and screening protocols. Hence, given the availability of whole genome sequences, RNAi has been used extensively as a tool for annotating gene function. Genetic screens performed with C. elegans have also led to the identification of genes that are essential for RNAi or that modulate the RNAi process. The identification of such genes has made it possible to manipulate and enhance the RNAi response. Moreover, many of the genes identified in C. elegans have been conserved in other organisms. Thus, opportunities are available for researchers to take advantage of the insights gained from the worm and apply them to their own systems in order to improve the efficiency and potency of the RNAi response.

Background

RNA interference (RNAi) is an efficient reverse genetics technique for investigating gene function in eukaryotes. The method has been widely used in model organisms, such as the free-living nematode Caenorhabditis elegans, where it has been deployed in genome-wide high throughput screens to identify genes involved in many cellular and developmental processes. However, RNAi techniques have not translated efficiently to animal parasitic nematodes that afflict humans, livestock and companion animals across the globe, creating a dependency on data tentatively inferred from C. elegans.

Results

We report improved and effective in vitro RNAi procedures we have developed using heterogeneous short interfering RNA (hsiRNA) mixtures that when coupled with optimized immunostaining techniques yield detailed analysis of cytological defects in the human parasitic nematode, Brugia malayi. The cellular disorganization observed in B. malayi embryos following RNAi targeting the genes encoding γ-tubulin, and the polarity determinant protein, PAR-1, faithfully phenocopy the known defects associated with gene silencing of their C. elegans orthologs. Targeting the B. malayi cell junction protein, AJM-1 gave a similar but more severe phenotype than that observed in C. elegans. Cellular phenotypes induced by our in vitro RNAi procedure can be observed by immunofluorescence in as little as one week.

Conclusions

We observed cytological defects following RNAi targeting all seven B. malayi transcripts tested and the phenotypes mirror those documented for orthologous genes in the model organism C. elegans. This highlights the reliability, effectiveness and specificity of our RNAi and immunostaining procedures. We anticipate that these techniques will be widely applicable to other important animal parasitic nematodes, which have hitherto been mostly refractory to such genetic analysis.

RNA interference (RNAi) leads to sequence-specific knockdown of gene function. The approach can be used in large-scale screens to interrogate function in various model organisms and an increasing number of other species. Genome-scale RNAi screens are routinely performed in cultured or primary cells or in vivo in organisms such as C. elegans. High-throughput RNAi screening is benefitting from the development of sophisticated new instrumentation and software tools for collecting and analyzing data, including high-content image data. The results of large-scale RNAi screens have already proved useful, leading to new understandings of gene function relevant to topics such as infection, cancer, obesity and aging. Nevertheless, important caveats apply and should be taken into consideration when developing or interpreting RNAi screens. Some level of false discovery is inherent to high-throughput approaches and specific to RNAi screens, false discovery due to off-target effects (OTEs) of RNAi reagents remains a problem. The need to improve our ability to use RNAi to elucidate gene function at large scale and in additional systems continues to be addressed through improved RNAi library design, development of innovative computational and analysis tools and other approaches.

Fluorescence microscopy is one of the most powerful tools to investigate complex cellular processes such as cell division, cell motility, or intracellular trafficking. The availability of RNA interference (RNAi) technology and automated microscopy has opened the possibility to perform cellular imaging in functional genomics and other large-scale applications. Although imaging often dramatically increases the content of a screening assay, it poses new challenges to achieve accurate quantitative annotation and therefore needs to be carefully adjusted to the specific needs of individual screening applications. In this review, we discuss principles of assay design, large-scale RNAi, microscope automation, and computational data analysis. We highlight strategies for imaging-based RNAi screening adapted to different library and assay designs.

Targeted gene silencing by RNA interference allows the study of gene function in plants and animals. In cell culture and small animal models, genetic screens can be performed—even tissue-specifically in Drosophila—with genome-wide RNAi libraries. However, a major problem with the use of RNAi approaches is the unavoidable false-positive error caused by off-target effects. Until now, this is minimized by computational RNAi design, comparing RNAi to the mutant phenotype if known, and rescue with a presumed ortholog. The ultimate proof of specificity would be to restore expression of the same gene product in vivo. Here, we present a simple and efficient method to rescue the RNAi-mediated knockdown of two independent genes in Drosophila. By exploiting the degenerate genetic code, we generated Drosophila RNAi Escape Strategy Construct (RESC) rescue proteins containing frequent silent mismatches in the complete RNAi target sequence. RESC products were no longer efficiently silenced by RNAi in cell culture and in vivo. As a proof of principle, we rescue the RNAi-induced loss of function phenotype of the eye color gene white and tracheal defects caused by the knockdown of the heparan sulfate proteoglycan syndecan. Our data suggest that RESC is widely applicable to rescue and validate ubiquitous or tissue-specific RNAi and to perform protein structure–function analysis.

Background

Automated, image based high-content screening is a fundamental tool for discovery in biological science. Modern robotic fluorescence microscopes are able to capture thousands of images from massively parallel experiments such as RNA interference (RNAi) or small-molecule screens. As such, efficient computational methods are required for automatic cellular phenotype identification capable of dealing with large image data sets. In this paper we investigated an efficient method for the extraction of quantitative features from images by combining second order statistics, or Haralick features, with curvelet transform. A random subspace based classifier ensemble with multiple layer perceptron (MLP) as the base classifier was then exploited for classification. Haralick features estimate image properties related to second-order statistics based on the grey level co-occurrence matrix (GLCM), which has been extensively used for various image processing applications. The curvelet transform has a more sparse representation of the image than wavelet, thus offering a description with higher time frequency resolution and high degree of directionality and anisotropy, which is particularly appropriate for many images rich with edges and curves. A combined feature description from Haralick feature and curvelet transform can further increase the accuracy of classification by taking their complementary information. We then investigate the applicability of the random subspace (RS) ensemble method for phenotype classification based on microscopy images. A base classifier is trained with a RS sampled subset of the original feature set and the ensemble assigns a class label by majority voting.

Results

Experimental results on the phenotype recognition from three benchmarking image sets including HeLa, CHO and RNAi show the effectiveness of the proposed approach. The combined feature is better than any individual one in the classification accuracy. The ensemble model produces better classification performance compared to the component neural networks trained. For the three images sets HeLa, CHO and RNAi, the Random Subspace Ensembles offers the classification rates 91.20%, 98.86% and 91.03% respectively, which compares sharply with the published result 84%, 93% and 82% from a multi-purpose image classifier WND-CHARM which applied wavelet transforms and other feature extraction methods. We investigated the problem of estimation of ensemble parameters and found that satisfactory performance improvement could be brought by a relative medium dimensionality of feature subsets and small ensemble size.

Conclusions

The characteristics of curvelet transform of being multiscale and multidirectional suit the description of microscopy images very well. It is empirically demonstrated that the curvelet-based feature is clearly preferred to wavelet-based feature for bioimage descriptions. The random subspace ensemble of MLPs is much better than a number of commonly applied multi-class classifiers in the investigated application of phenotype recognition.

RNA interference (RNAi) is being used in large-scale genomic studies as a rapid way to obtain in vivo functional information associated with specific genes. How best to archive and mine the complex data derived from these studies provides a series of challenges associated with both the methods used to elicit the RNAi response and the functional data gathered. RNAiDB (RNAi Database; http://www.rnai.org) has been created for the archival, distribution and analysis of phenotypic data from large-scale RNAi analyses in Caenorhabditis elegans. The database contains a compendium of publicly available data and provides information on experimental methods and phenotypic results, including raw data in the form of images and streaming time-lapse movies. Phenotypic summaries together with graphical displays of RNAi to gene mappings allow quick intuitive comparison of results from different RNAi assays and visualization of the gene product(s) potentially inhibited by each RNAi experiment based on multiple sequence analysis methods. RNAiDB can be searched using combinatorial queries and using the novel tool PhenoBlast, which ranks genes according to their overall phenotypic similarity. RNAiDB could serve as a model database for distributing and navigating in vivo functional information from large-scale systematic phenotypic analyses in different organisms.

Gene silencing by transient or stable RNA-interference (RNAi) is used for the study of apoptosis with an assumption that apoptotic events will not influence RNAi. However, we recently reported that stable RNAi, i.e., a permanent gene-knockdown mediated by shRNA-generating DNA vectors that are integrated in the genome, fails rapidly after induction of apoptosis due to caspase-3-mediated cleavage and inactivation of the endoribonuclease Dicer-1 that is required for conversion of shRNA to siRNA. Since apoptosis studies also increasingly employ transient RNAi models in which apoptosis is induced immediately after a gene is temporarily knocked down within a few days of transfection with RNAi-inducing agents, we examined the impact of apoptosis on various models of transient RNAi. We report here that unlike the stable RNAi, all forms of transient RNAi, whether Dicer-1-independent (by 21mer dsRNA) or Dicer-1-dependent (by 27mer dsRNA or shRNA-generating DNA vector), whether for an exogenous gene GFP or an endogenous gene poly(ADP-ribose) polymerase-1, do not fail for 2–3 days after onset of apoptosis. Our results reflect the differences in dynamics of achieving and maintaining RNAi during the early phase after transfection in the transient RNAi model and the late steady-state phase of gene-knockdown in stable RNAi model. Our results also sound a cautionary note that RNAi status should be frequently validated in the studies involving apoptosis and that while stable RNAi can be safely used for the study of early apoptotic events, transient RNAi is more suitable for the study of both early and late apoptotic events.

High-throughput genome-wide RNA interference (RNAi) screening is emerging as an essential tool to assist biologists in understanding complex cellular processes. The large number of images produced in each study make manual analysis intractable; hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. In this paper, a fully automatic method for segmentation of cells from genome-wide RNAi screening images is proposed. Nuclei are first extracted from the DNA channel by using a modified watershed algorithm. Cells are then extracted by modeling the interaction between them as well as combining both gradient and region information in the Actin and Rac channels. A new energy functional is formulated based on a novel interaction model for segmenting tightly clustered cells with significant intensity variance and specific phenotypes. The energy functional is minimized by using a multiphase level set method, which leads to a highly effective cell segmentation method. Promising experimental results demonstrate that automatic segmentation of high-throughput genome-wide multichannel screening can be achieved by using the proposed method, which may also be extended to other multichannel image segmentation problems.

RNA interference (RNAi) is a powerful tool to analyze gene function in mammalian cells. However, the interpretation of RNAi knock-down phenotypes can be hampered by off-target effects or compound phenotypes, as many proteins combine multiple functions within one molecule and coordinate the assembly of multimolecular complexes. Replacing the endogenous protein with ectopic wild-type or mutant forms can exclude off-target effects, preserve complexes and unravel specific roles of domains or modifications. Therefore, we developed a rapid-knock-down–knock-in system for mammalian cells. Stable polyclonal cell lines were generated within 2 weeks by simultaneous selection of two episomal vectors. Together these vectors mediated reconstitution and knock-down in a doxycycline-dependent manner to allow the analysis of essential genes. Depletion was achieved by an artificial miRNA-embedded siRNA targeting the untranslated region of the endogenous, but not the ectopic mRNA. To prove effectiveness, we tested 17 mutants of WDR12, a factor essential for ribosome biogenesis and cell proliferation. Loss-off function phenotypes were rescued by the wild-type and six mutant forms, but not by the remaining mutants. Thus, our system is suitable to exclude off-target effects and to functionally analyze mutants in cells depleted for the endogenous protein.

RNA interference (RNAi) is an effective tool for genome-scale, high-throughput analysis of gene function. In the past five years, a number of genome-scale RNAi high-throughput screens (HTSs) have been done in both Drosophila and mammalian cultured cells to study diverse biological processes, including signal transduction, cancer biology, and host cell responses to infection. Results from these screens have led to the identification of new components of these processes and, importantly, have also provided insights into the complexity of biological systems, forcing new and innovative approaches to understanding functional networks in cells. Here, we review the main findings that have emerged from RNAi HTS and discuss technical issues that remain to be improved, in particular the verification of RNAi results and validation of their biological relevance. Furthermore, we discuss the importance of multiplexed and integrated experimental data analysis pipelines to RNAi HTS.