High-throughput RNA interference (RNAi) screens have been used to find genes that, when silenced, result in sensitivity to certain chemotherapy drugs. Researchers therefore can further identify drug-sensitive targets and novel drug combinations that sensitize cancer cells to chemotherapeutic drugs. Considerable uncertainty exists about the efficiency and accuracy of statistical approaches used for RNAi hit selection in drug sensitivity studies. Researchers require statistical methods suitable for analyzing high-throughput RNAi screening data that will reduce false-positive and false-negative rates.
In this study, we carried out a simulation study to evaluate four types of statistical approaches (fold-change/ratio, parametric tests/statistics, sensitivity index, and linear models) with different scenarios of RNAi screenings for drug sensitivity studies. With the simulated datasets, the linear model resulted in significantly lower false-negative and false-positive rates. Based on the results of the simulation study, we then make recommendations of statistical analysis methods for high-throughput RNAi screening data in different scenarios. We assessed promising methods using real data from a loss-of-function RNAi screen to identify hits that modulate paclitaxel sensitivity in breast cancer cells. High-confidence hits with specific inhibitors were further analyzed for their ability to inhibit breast cancer cell growth. Our analysis identified a number of gene targets with inhibitors known to enhance paclitaxel sensitivity, suggesting other genes identified may merit further investigation.
RNAi screening can identify druggable targets and novel drug combinations that can sensitize cancer cells to chemotherapeutic drugs. However, applying an inappropriate statistical method or model to the RNAi screening data will result in decreased power to detect the true hits and increase false positive and false negative rates, leading researchers to draw incorrect conclusions. In this paper, we make recommendations to enable more objective selection of statistical analysis methods for high-throughput RNAi screening data.
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.
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.
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.
Large-scale RNAi-based screens are playing a critical role in defining sets of genes that regulate specific cellular processes. Numerous screens have been completed and in some cases more than one screen has examined the same cellular process, enabling a direct comparison of the genes identified in separate screens. Surprisingly, the overlap observed between the results of similar screens is low, suggesting that RNAi screens have relatively high levels of false positives, false negatives, or both.
We re-examined genes that were identified in two previous RNAi-based cell cycle screens to identify potential false positives and false negatives. We were able to confirm many of the originally observed phenotypes and to reveal many likely false positives. To identify potential false negatives from the previous screens, we used protein interaction networks to select genes for re-screening. We demonstrate cell cycle phenotypes for a significant number of these genes and show that the protein interaction network is an efficient predictor of new cell cycle regulators. Combining our results with the results of the previous screens identified a group of validated, high-confidence cell cycle/cell survival regulators. Examination of the subset of genes from this group that regulate the G1/S cell cycle transition revealed the presence of multiple members of three structurally related protein complexes: the eukaryotic translation initiation factor 3 (eIF3) complex, the COP9 signalosome, and the proteasome lid. Using a combinatorial RNAi approach, we show that while all three of these complexes are required for Cdk2/Cyclin E activity, the eIF3 complex is specifically required for some other step that limits the G1/S cell cycle transition.
Our results show that false positives and false negatives each play a significant role in the lack of overlap that is observed between similar large-scale RNAi-based screens. Our results also show that protein network data can be used to minimize false negatives and false positives and to more efficiently identify comprehensive sets of regulators for a process. Finally, our data provides a high confidence set of genes that are likely to play key roles in regulating the cell cycle or cell survival.
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.
RNAi; high-throughput screens; high-content imaging; cell-based assays
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.
High-throughput RNA interference (RNAi) screening has become a widely used approach to elucidating gene functions. However, analysis and annotation of large data sets generated from these screens has been a challenge for researchers without a programming background. Over the years, numerous data analysis methods were produced for plate quality control and hit selection and implemented by a few open-access software packages. Recently, strictly standardized mean difference (SSMD) has become a widely used method for RNAi screening analysis mainly due to its better control of false negative and false positive rates and its ability to quantify RNAi effects with a statistical basis. We have developed GUItars to enable researchers without a programming background to use SSMD as both a plate quality and a hit selection metric to analyze large data sets.
The software is accompanied by an intuitive graphical user interface for easy and rapid analysis workflow. SSMD analysis methods have been provided to the users along with traditionally-used z-score, normalized percent activity, and t-test methods for hit selection. GUItars is capable of analyzing large-scale data sets from screens with or without replicates. The software is designed to automatically generate and save numerous graphical outputs known to be among the most informative high-throughput data visualization tools capturing plate-wise and screen-wise performances. Graphical outputs are also written in HTML format for easy access, and a comprehensive summary of screening results is written into tab-delimited output files.
With GUItars, we demonstrated robust SSMD-based analysis workflow on a 3840-gene small interfering RNA (siRNA) library and identified 200 siRNAs that increased and 150 siRNAs that decreased the assay activities with moderate to stronger effects. GUItars enables rapid analysis and illustration of data from large- or small-scale RNAi screens using SSMD and other traditional analysis methods. The software is freely available at http://sourceforge.net/projects/guitars/.
Genome-wide RNAi screening is a powerful, yet relatively immature technology that allows investigation into the role of individual genes in a process of choice. Most RNAi screens identify a large number of genes with a continuous gradient in the assessed phenotype. Screeners must then decide whether to examine just those genes with the most robust phenotype or to examine the full gradient of genes that cause an effect and how to identify the candidate genes to be validated. We have used RNAi in Drosophila cells to examine viability in a 384-well plate format and compare two screens, untreated control and treatment. We compare multiple normalization methods, which take advantage of different features within the data, including quantile normalization, background subtraction, scaling, cellHTS2 1, and interquartile range measurement. Considering the false-positive potential that arises from RNAi technology, a robust validation method was designed for the purpose of gene selection for future investigations. In a retrospective analysis, we describe the use of validation data to evaluate each normalization method. While no normalization method worked ideally, we found that a combination of two methods, background subtraction followed by quantile normalization and cellHTS2, at different thresholds, captures the most dependable and diverse candidate genes. Thresholds are suggested depending on whether a few candidate genes are desired or a more extensive systems level analysis is sought. In summary, our normalization approaches and experimental design to perform validation experiments are likely to apply to those high-throughput screening systems attempting to identify genes for systems level analysis.
RNAi; high-throughput screen; normalization; validation
RNAi is a convenient, widely used tool for screening for genes of interest. We have recently used this technology to screen roughly 750 candidate genes, in C. elegans, for potential roles in regulating muscle protein degradation in vivo. To maximize confidence and assess reproducibility, we have only used previously validated RNAi constructs and have included time courses and replicates. To maximize mechanistic understanding, we have examined multiple sub-cellular phenotypes in multiple compartments in muscle. We have also tested knockdowns of putative regulators of degradation in the context of mutations or drugs that were previously shown to inhibit protein degradation by diverse mechanisms. Here we discuss how assaying multiple phenotypes, multiplexing RNAi screens with use of mutations and drugs, and use of bioinformatics can provide more data on rates of potential false positives and negatives as well as more mechanistic insight than simple RNAi screening.
RNAi; Systems Biology; Network Biology; Functional Genomics; Muscle; Proteolysis; C. elegans
C. elegans is an important model for genetic studies relevant to human biology and disease. We sought to assess the orthology between C. elegans and human genes to understand better the relationship between their genomes and to generate a compelling list of candidates to streamline RNAi-based screens in this model.
We performed a meta-analysis of results from four orthology prediction programs and generated a compendium, “OrthoList”, containing 7,663 C. elegans protein-coding genes. Various assessments indicate that OrthoList has extensive coverage with low false-positive and false-negative rates. Part of this evaluation examined the conservation of components of the receptor tyrosine kinase, Notch, Wnt, TGF-ß and insulin signaling pathways, and led us to update compendia of conserved C. elegans kinases, nuclear hormone receptors, F-box proteins, and transcription factors. Comparison with two published genome-wide RNAi screens indicated that virtually all of the conserved hits would have been obtained had just the OrthoList set (∼38% of the genome) been targeted. We compiled Ortholist by InterPro domains and Gene Ontology annotation, making it easy to identify C. elegans orthologs of human disease genes for potential functional analysis.
We anticipate that OrthoList will be of considerable utility to C. elegans researchers for streamlining RNAi screens, by focusing on genes with apparent human orthologs, thus reducing screening effort by ∼60%. Moreover, we find that OrthoList provides a useful basis for annotating orthology and reveals more C. elegans orthologs of human genes in various functional groups, such as transcription factors, than previously described.
FlyRNAi (http://www.flyrnai.org), the database and website of the Drosophila RNAi Screening Center (DRSC) at Harvard Medical School, serves a dual role, tracking both production of reagents for RNA interference (RNAi) screening in Drosophila cells and RNAi screen results. The database and website is used as a platform for community availability of protocols, tools, and other resources useful to researchers planning, conducting, analyzing or interpreting the results of Drosophila RNAi screens. Based on our own experience and user feedback, we have made several changes. Specifically, we have restructured the database to accommodate new types of reagents; added information about new RNAi libraries and other reagents; updated the user interface and website; and added new tools of use to the Drosophila community and others. Overall, the result is a more useful, flexible and comprehensive website and database.
A second generation dsRNA library was used to re-assess factors that influence the outcome of transcriptional reporter-based whole-genome RNAi screens for the Wnt/Wingless (wg) and Hedgehog (hh)-signaling pathways.
Off-target effects have been demonstrated to be a major source of false-positives in RNA interference (RNAi) high-throughput screens. In this study, we re-assess the previously published transcriptional reporter-based whole-genome RNAi screens for the Wingless and Hedgehog signaling pathways using second generation double-stranded RNA libraries. Furthermore, we investigate other factors that may influence the outcome of such screens, including cell-type specificity, robustness of reporters, and assay normalization, which determine the efficacy of RNAi-knockdown of target genes.
Genome-scale RNA-interference (RNAi) screens are becoming ever more common gene discovery tools. However, whilst every screen identifies interacting genes, less attention has been given to how factors such as library design and post-screening bioinformatics may be effecting the data generated.
Here we present a new genome-wide RNAi screen of the Drosophila JAK/STAT signalling pathway undertaken in the Sheffield RNAi Screening Facility (SRSF). This screen was carried out using a second-generation, computationally optimised dsRNA library and analysed using current methods and bioinformatic tools. To examine advances in RNAi screening technology, we compare this screen to a biologically very similar screen undertaken in 2005 with a first-generation library. Both screens used the same cell line, reporters and experimental design, with the SRSF screen identifying 42 putative regulators of JAK/STAT signalling, 22 of which verified in a secondary screen and 16 verified with an independent probe design. Following reanalysis of the original screen data, comparisons of the two gene lists allows us to make estimates of false discovery rates in the SRSF data and to conduct an assessment of off-target effects (OTEs) associated with both libraries. We discuss the differences and similarities between the resulting data sets and examine the relative improvements in gene discovery protocols.
Our work represents one of the first direct comparisons between first- and second-generation libraries and shows that modern library designs together with methodological advances have had a significant influence on genome-scale RNAi screens.
Genome screening; RNAi; Off-target effect; JAK/STAT pathway; Functional genomics; dsRNA
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.
RNAi; database; integration; bioinformatics; phenotype
The GenomeRNAi database (http://www.genomernai.org/) contains phenotypes from published cell-based RNA interference (RNAi) screens in Drosophila and Homo sapiens. The database connects observed phenotypes with annotations of targeted genes and information about the RNAi reagent used for the perturbation experiment. The availability of phenotypes from Drosophila and human screens also allows for phenotype searches across species. Besides reporting quantitative data from genome-scale screens, the new release of GenomeRNAi also enables reporting of data from microscopy experiments and curated phenotypes from published screens. In addition, the database provides an updated resource of RNAi reagents and their predicted quality that are available for the Drosophila and the human genome. The new version also facilitates the integration with other genomic data sets and contains expression profiling (RNA-Seq) data for several cell lines commonly used in RNAi experiments.
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.
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.
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.
Motivation: Off-target activity commonly exists in RNA interference (RNAi) screens and often generates false positives. Existing analytic methods for addressing the off-target effects are demonstrably inadequate in RNAi confirmatory screens.
Results: Here, we present an analytic method assessing the collective activity of multiple short interfering RNAs (siRNAs) targeting a gene. Using this method, we can not only reduce the impact of off-target activities, but also evaluate the specific effect of an siRNA, thus providing information about potential off-target effects. Using in-house RNAi screens, we demonstrate that our method obtains more reasonable and sensible results than current methods such as the redundant siRNA activity (RSA) method, the RNAi gene enrichment ranking (RIGER) method, the frequency approach and the t-test.
Supplementary information: Supplementary data are available at Bioinformatics online.
Cell-based microarrays were first described by Ziauddin and Sabatini in 2001 as a powerful new approach for performing high throughput screens of gene function. An important application of cell-based microarrays is in screening for proteins that modulate gene networks. To this end, cells are grown over the surface of arrays of RNAi or expression reagents. Cells growing in the immediate vicinity of the arrayed reagents are transfected and the arrays can then be scanned for cells showing localised changes in function. Here we describe the construction of a large-scale microarray using expression plasmids containing human genes, its use in screening for genes that induce apoptosis when over-expressed and the characterisation of a number of these genes by following the transcriptional response of cell cultures during their induction of apoptosis.
High-density cell-based arrays were successfully fabricated using 1,959 un-tagged open reading frames (ORFs) taken from the Mammalian Gene Collection (MGC) in mammalian expression vectors. The arrays were then used to screen for genes inducing apoptosis in Human Embryonic Kidney (HEK293T) cells. Using this approach, 10 genes were clearly identified and confirmed to induce apoptosis. Some of these genes have previously been linked to apoptosis, others not. The mechanism of action of three of the 10 genes were then characterised further by following the transcriptional events associated with apoptosis induction using expression profiling microarrays. This data demonstrates a clear pro-apoptotic transcriptional response in cells undergoing apoptosis and also suggests the use of common apoptotic pathways regardless of the nature of the over-expressed protein triggering cell death.
This study reports the design and use of the first truly large-scale cell-based microarrays for over-expression studies. Ten genes were confirmed to induce apoptosis, some of which were not previously known to possess this activity. Transcriptome analysis on three of the 10 genes demonstrated their use of similar pathways to invoke apoptosis.
RNA interference (RNAi) is a collection of small RNA directed mechanisms that result in sequence specific inhibition of gene expression. The notion that RNAi could lead to a new class of therapeutics caught the attention of many investigators soon after its discovery. The field of applied RNAi therapeutics has moved very quickly from lab to bedside. The RNAi approach has been widely used for drug development and several phase I and II clinical trials are under way. However, there are still some concerns and challenges to overcome for therapeutic applications. These include the potential for off-target effects, triggering innate immune responses and most importantly obtaining specific delivery into the cytoplasm of target cells. This review focuses on the current status of RNAi-based therapeutics, the challenges it faces and how to overcome them.
RNAi; delivery; siRNA; therapeutics; shRNA
Motivation: Yeast two-hybrid screens are an important method to map pairwise protein interactions. This method can generate spurious interactions (false discoveries), and true interactions can be missed (false negatives). Previously, we reported a capture–recapture estimator for bait-specific precision and recall. Here, we present an improved method that better accounts for heterogeneity in bait-specific error rates.
Result: For yeast, worm and fly screens, we estimate the overall false discovery rates (FDRs) to be 9.9%, 13.2% and 17.0% and the false negative rates (FNRs) to be 51%, 42% and 28%. Bait-specific FDRs and the estimated protein degrees are then used to identify protein categories that yield more (or fewer) false positive interactions and more (or fewer) interaction partners. While membrane proteins have been suggested to have elevated FDRs, the current analysis suggests that intrinsic membrane proteins may actually have reduced FDRs. Hydrophobicity is positively correlated with decreased error rates and fewer interaction partners. These methods will be useful for future two-hybrid screens, which could use ultra-high-throughput sequencing for deeper sampling of interacting bait–prey pairs.
Availability: All software (C source) and datasets are available as supplemental files and at http://www.baderzone.org under the Lesser GPL v. 3 license.
Supplementary information: Supplementary data are available at Bioinformatics online.
Since its discovery a decade ago, RNA interference (RNAi) has been developed not only into powerful experimental tools but also into promising novel therapeutics. In contrast to conventional antiepileptic drugs that target specific proteins such as ion channels or receptors, RNAi –based therapeutics exploit an endogenous regulatory mechanism of gene expression and thereby are poised to prevent or reverse pathogenetic mechanisms involved in seizure development. Therapeutic RNAi has been widely explored for dominant targets involved in neurodegenerative diseases; however, their use for epilepsy therapy has received less attention. This review will discuss potential RNAi-based targets that are of interest for epilepsy therapy, including adenosine kinase (ADK), the key negative regulator of the brain’s endogenous anticonvulsant adenosine. Overexpression of ADK, and the resulting adenosine deficiency, are pathological hallmarks of the sclerotic epileptic brain, and have been implicated in seizure generation. Therefore, RNAi-strategies aimed at reducing ADK (and increasing adenosine) are based on a direct neurochemical rationale that has recently been explored experimentally using ex vivo and in vivo gene therapy approaches. Technical issues and challenges remain before those promising tools can be developed into future therapeutics for epilepsy.
adenosine; RNA interference; RNAi; gene therapy; glia
Gene perturbation experiments in combination with fluorescence time-lapse cell imaging are a powerful tool in reverse genetics. High content applications require tools for the automated processing of the large amounts of data. These tools include in general several image processing steps, the extraction of morphological descriptors, and the grouping of cells into phenotype classes according to their descriptors. This phenotyping can be applied in a supervised or an unsupervised manner. Unsupervised methods are suitable for the discovery of formerly unknown phenotypes, which are expected to occur in high-throughput RNAi time-lapse screens.
We developed an unsupervised phenotyping approach based on Hidden Markov Models (HMMs) with multivariate Gaussian emissions for the detection of knockdown-specific phenotypes in RNAi time-lapse movies. The automated detection of abnormal cell morphologies allows us to assign a phenotypic fingerprint to each gene knockdown. By applying our method to the Mitocheck database, we show that a phenotypic fingerprint is indicative of a gene’s function.
Our fully unsupervised HMM-based phenotyping is able to automatically identify cell morphologies that are specific for a certain knockdown. Beyond the identification of genes whose knockdown affects cell morphology, phenotypic fingerprints can be used to find modules of functionally related genes.
RNA interference (RNAi) represents a powerful method to systematically study loss-of-function phenotypes on a large scale with a wide variety of biological assays, constituting a rich source for the assignment of gene function. The GenomeRNAi database (http://www.genomernai.org) makes available RNAi phenotype data extracted from the literature for human and Drosophila. It also provides RNAi reagent information, along with an assessment as to their efficiency and specificity. This manuscript describes an update of the database previously featured in the NAR Database Issue. The new version has undergone a complete re-design of the user interface, providing an intuitive, flexible framework for additional functionalities. Screen information and gene-reagent-phenotype associations are now available for download. The integration with other resources has been improved by allowing in-links via GenomeRNAi screen IDs, or external gene or reagent identifiers. A distributed annotation system (DAS) server enables the visualization of the phenotypes and reagents in the context of a genome browser. We have added a page listing ‘frequent hitters’, i.e. genes that show a phenotype in many screens, which might guide on-going RNAi studies. Structured annotation guidelines have been established to facilitate consistent curation, and a submission template for direct submission by data producers is available for download.
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.
RNA interference (RNAi) is a powerful approach to study a gene function. Transgenic RNAi is an adaptation of this approach where suppression of a specific gene is achieved by expression of an RNA hairpin from a transgene. In somatic cells, where a long double-stranded RNA (dsRNA) longer than 30 base-pairs can induce a sequence-independent interferon response, short hairpin RNA (shRNA) expression is used to induce RNAi. In contrast, transgenic RNAi in the oocyte routinely employs a long RNA hairpin. Transgenic RNAi based on long hairpin RNA, although robust and successful, is restricted to a few cell types, where long double-stranded RNA does not induce sequence-independent responses. Transgenic RNAi in mouse oocytes based on a shRNA offers several potential advantages, including simple cloning of the transgenic vector and an ability to use the same targeting construct in any cell type.
Here we report our experience with shRNA-based transgenic RNAi in mouse oocytes. Despite optimal starting conditions for this experiment, we experienced several setbacks, which outweigh potential benefits of the shRNA system. First, obtaining an efficient shRNA is potentially a time-consuming and expensive task. Second, we observed that our transgene, which was based on a common commercial vector, was readily silenced in transgenic animals.
We conclude that, the long RNA hairpin-based RNAi is more reliable and cost-effective and we recommend it as a method-of-choice when a gene is studied selectively in the oocyte.
The completion of the genome sequencing for several organisms has
created a great demand for genomic tools that can systematically
analyze the growing wealth of data. In contrast to the classical
reverse genetics approach of creating specific knockout cell lines
or animals that is time-consuming and expensive, RNA-mediated
interference (RNAi) has emerged as a fast, simple, and
cost-effective technique for gene knockdown in large scale. Since
its discovery as a gene silencing response to double-stranded RNA
(dsRNA) with homology to endogenous genes in
Caenorhabditis elegans (C elegans),
RNAi technology has been adapted to various high-throughput
screens (HTS) for genome-wide loss-of-function (LOF) analysis.
Biochemical insights into the endogenous mechanism of
RNAi have led to advances in RNAi methodology including RNAi
molecule synthesis, delivery, and sequence design. In this
article, we will briefly review these various RNAi library designs
and discuss the benefits and drawbacks of each library strategy.