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.
Advances in microscopy automation and image analysis have given biologists the tools to attempt large scale systems-level experiments on biological systems using microscope image readout. Fluorescence microscopy has become a standard tool for assaying gene function in RNAi knockdown screens and protein localization studies in eukaryotic systems. Similar high throughput studies can be attempted in prokaryotes, though the difficulties surrounding work at the diffraction limit pose challenges, and targeting essential genes in a high throughput way can be difficult. Here we will discuss efforts to make live-cell fluorescent microscopy based experiments using genetically encoded fluorescent reporters an automated, high throughput, and quantitative endeavor amenable to systems-level experiments in bacteria. We emphasize a quantitative data reduction approach, using simulation to help develop biologically relevant cell measurements that completely characterize the cell image. We give an example of how this type of data can be directly exploited by statistical learning algorithms to discover functional pathways.
The diffraction limit makes high-throughput fluorescence microscopy more challenging in prokaryotes, but approaches such as quantitative data reduction now allow systems-level analysis of bacteria by this technique.
With recent advances in fluorescence microscopy imaging techniques and methods of gene knock down by RNA interference (RNAi), genome-scale high-content screening (HCS) has emerged as a powerful approach to systematically identify all parts of complex biological processes. However, a critical barrier preventing fulfillment of the success is the lack of efficient and robust methods for automating RNAi image analysis and quantitative evaluation of the gene knock down effects on huge volume of HCS data. Facing such opportunities and challenges, we have started investigation of automatic methods towards the development of a fully automatic RNAi-HCS system. Particularly important are reliable approaches to cellular phenotype classification and image-based gene function estimation.
We have developed a HCS analysis platform that consists of two main components: fluorescence image analysis and image scoring. For image analysis, we used a two-step enhanced watershed method to extract cellular boundaries from HCS images. Segmented cells were classified into several predefined phenotypes based on morphological and appearance features. Using statistical characteristics of the identified phenotypes as a quantitative description of the image, a score is generated that reflects gene function. Our scoring model integrates fuzzy gene class estimation and single regression models. The final functional score of an image was derived using the weighted combination of the inference from several support vector-based regression models. We validated our phenotype classification method and scoring system on our cellular phenotype and gene database with expert ground truth labeling.
We built a database of high-content, 3-channel, fluorescence microscopy images of Drosophila Kc167 cultured cells that were treated with RNAi to perturb gene function. The proposed informatics system for microscopy image analysis is tested on this database. Both of the two main components, automated phenotype classification and image scoring system, were evaluated. The robustness and efficiency of our system were validated in quantitatively predicting the biological relevance of genes.
High-content screening; Image score inference
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.
active contour; automatic image segmentation; constraint factor; fluorescent microscopy; genome-wide screening; graph cut; morphological algorithm; RNAi
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.
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.
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) 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
RNA interference (RNAi) has emerged as a powerful tool to generate loss-of-function phenotypes in a variety of organisms. Combined with the sequence information of almost completely annotated genomes, RNAi technologies have opened new avenues to conduct systematic genetic screens for every annotated gene in the genome. As increasing large datasets of RNAi-induced phenotypes become available, an important challenge remains the systematic integration and annotation of functional information. Genome-wide RNAi screens have been performed both in Caenorhabditis elegans and Drosophila for a variety of phenotypes and several RNAi libraries have become available to assess phenotypes for almost every gene in the genome. These screens were performed using different types of assays from visible phenotypes to focused transcriptional readouts and provide a rich data source for functional annotation across different species. The GenomeRNAi database provides access to published RNAi phenotypes obtained from cell-based screens and maps them to their genomic locus, including possible non-specific regions. The database also gives access to sequence information of RNAi probes used in various screens. It can be searched by phenotype, by gene, by RNAi probe or by sequence and is accessible at
In many eukaryotic cells, double-stranded RNA (dsRNA) triggers RNA interference (RNAi), the specific degradation of RNA of homologous sequence. RNAi is now a major tool for reverse-genetics projects, including large-scale high-throughput screens. Recent reports have questioned the specificity of RNAi, raising problems in interpretation of RNAi-based experiments.
Using the protozoan Trypanosoma brucei as a model, we designed a functional complementation assay to ascertain that phenotypic effect(s) observed upon RNAi were due to specific silencing of the targeted gene. This was applied to a cytoskeletal gene encoding the paraflagellar rod protein 2 (TbPFR2), whose product is essential for flagellar motility. We demonstrate the complementation of TbPFR2, silenced via dsRNA targeting its UTRs, through the expression of a tagged RNAi-resistant TbPFR2 encoding a protein that could be immunolocalized in the flagellum. Next, we performed a functional complementation of TbPFR2, silenced via dsRNA targeting its coding sequence, through heterologous expression of the TbPFR2 orthologue gene from Trypanosoma cruzi: the flagellum regained its motility.
This work shows that functional complementation experiments can be readily performed in order to ascertain that phenotypic effects observed upon RNAi experiments are indeed due to the specific silencing of the targetted gene. Further, the results described here are of particular interest when reverse genetics studies cannot be easily achieved in organisms not amenable to RNAi. In addition, our strategy should constitute a firm basis to elaborate functional-dissection studies of genes from other organisms.
RNA interference (RNAi) screens have enabled the systematic analysis of many biological processes in cultured cells and whole organisms. The success of such screens and the interpretation of the data depend on the stringent design of RNAi libraries. We describe and validate NEXT-RNAi, a software for the automated design and evaluation of RNAi sequences on a genome-wide scale. NEXT-RNAi is implemented as open-source software and is accessible at http://www.nextrnai.org/.
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.
The software guides the user through the configuration steps that are required for the analysis of single or multi-channel experiments. The web-application provides options for various standardization and normalization methods, annotation of data sets and a comprehensive HTML report of the screening data analysis, including a ranked hit list. Sessions can be saved and restored for later re-analysis. The web frontend for the cellHTS2 R/Bioconductor package interacts with it through an R-server implementation that enables highly parallel analysis of screening data sets. web cellHTS2 further provides a file import and configuration module for common file formats.
The implemented web-application facilitates the analysis of high-throughput data sets and provides a user-friendly interface. web cellHTS2 is accessible online at http://web-cellHTS2.dkfz.de. A standalone version as a virtual appliance and source code for platforms supporting Java 1.5.0 can be downloaded from the web cellHTS2 page. web cellHTS2 is freely distributed under GPL.
A large set of high-content RNAi screens investigating mammalian virus infection and multiple cellular activities is analysed to reveal the impact of population context on phenotypic variability and to identify indirect RNAi effects.
Cell population context determines phenotypes in RNAi screens of multiple cellular activities (including virus infection, cell size regulation, endocytosis, and lipid homeostasis), which can be accounted for by a combination of novel image analysis and multivariate statistical methods.Accounting for cell population context-mediated effects strongly changes the reproducibility and consistency of RNAi screens across cell lines as well as of siRNAs targeting the same gene.Such analyses can identify the perturbed regulation of population context dependent cell-to-cell variability, a novel perturbation phenotype.Overall, these methods advance the use of large-scale RNAi screening for a systems-level understanding of cellular processes.
Isogenic cells in culture show strong variability, which arises from dynamic adaptations to the microenvironment of individual cells. Here we study the influence of the cell population context, which determines a single cell's microenvironment, in image-based RNAi screens. We developed a comprehensive computational approach that employs Bayesian and multivariate methods at the single-cell level. We applied these methods to 45 RNA interference screens of various sizes, including 7 druggable genome and 2 genome-wide screens, analysing 17 different mammalian virus infections and four related cell physiological processes. Analysing cell-based screens at this depth reveals widespread RNAi-induced changes in the population context of individual cells leading to indirect RNAi effects, as well as perturbations of cell-to-cell variability regulators. We find that accounting for indirect effects improves the consistency between siRNAs targeted against the same gene, and between replicate RNAi screens performed in different cell lines, in different labs, and with different siRNA libraries. In an era where large-scale RNAi screens are increasingly performed to reach a systems-level understanding of cellular processes, we show that this is often improved by analyses that account for and incorporate the single-cell microenvironment.
cell-to-cell variability; image analysis; population context; RNAi; virus infection
RNA interference (RNAi) becomes an increasingly important and effective genetic tool to study the function of target genes by suppressing specific genes of interest. This system approach helps identify signaling pathways and cellular phase types by tracking intensity and/or morphological changes of cells. The traditional RNAi screening scheme, in which one siRNA is designed to knockdown one specific mRNA target, needs a large library of siRNAs and turns out to be time-consuming and expensive.
In this paper, we propose a conceptual model, called compressed sensing RNAi (csRNAi), which employs a unique combination of group of small interfering RNAs (siRNAs) to knockdown a much larger size of genes. This strategy is based on the fact that one gene can be partially bound with several small interfering RNAs (siRNAs) and conversely, one siRNA can bind to a few genes with distinct binding affinity. This model constructs a multi-to-multi correspondence between siRNAs and their targets, with siRNAs much fewer than mRNA targets, compared with the conventional scheme. Mathematically this problem involves an underdetermined system of equations (linear or nonlinear), which is ill-posed in general. However, the recently developed compressed sensing (CS) theory can solve this problem. We present a mathematical model to describe the csRNAi system based on both CS theory and biological concerns. To build this model, we first search nucleotide motifs in a target gene set. Then we propose a machine learning based method to find the effective siRNAs with novel features, such as image features and speech features to describe an siRNA sequence. Numerical simulations show that we can reduce the siRNA library to one third of that in the conventional scheme. In addition, the features to describe siRNAs outperform the existing ones substantially.
This csRNAi system is very promising in saving both time and cost for large-scale RNAi screening experiments which may benefit the biological research with respect to cellular processes and pathways.
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.
Molecular Biology; Issue 60; C. elegans; fluorescent reporter; Biosort; LIMS; innate immunity; Drechmeria coniospora
Cell migration is a complex phenomenon that requires the coordination of numerous cellular processes. Investigation of cell migration and its underlying biology is of interest to basic scientists and those in search of therapeutics. Current migration assays for screening small molecules, siRNAs, or other perturbations are difficult to perform in parallel at the scale required to screen large libraries.
We have adapted the commonly used scratch wound healing assay of tissue-culture cell monolayers to a 384 well plate format. By mechanically scratching the cell substrate with a pin array, we are able to create characteristically sized wounds in all wells of a 384 well plate. Imaging of the healing wounds with an automated fluorescence microscope allows us to distinguish perturbations that affect cell migration, morphology, and division. Readout requires ~1 hr per plate but is high in information content i.e. high content. We compare readouts using different imaging technologies, automated microscopy, scanners and a fluorescence macroscope, and evaluate the trade-off between information content and data acquisition rate.
The adaptation of a wound healing assay to a 384 well format facilitates the study of aspects of cell migration, tissue reorganization, cell division, and other processes that underlie wound healing. This assay allows greater than 10,000 perturbations to be screened per day with a quantitative, high-content readout, and can also be used to characterize small numbers of perturbations in detail.
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.
RNA interference (RNAi) is commonly applied in genome-scale gene functional screens. However, a one-on-one RNAi analysis that targets each gene is cost-ineffective and laborious. Previous studies have indicated that siRNAs can also affect RNAs that are near-perfectly complementary, and this phenomenon has been termed an off-target effect. This phenomenon implies that it is possible to silence several genes simultaneously with a carefully designed siRNA.
We propose a strategy that is combined with a heuristic algorithm to design suitable siRNAs that can target multiple genes and a group testing method that would reduce the number of required RNAi experiments in a large-scale RNAi analysis. To verify the efficacy of our strategy, we used the Orchid expressed sequence tag data as a case study to screen the putative transcription factors that are involved in plant disease responses. According to our computation, 94 qualified siRNAs were sufficient to examine all of the predicated 229 transcription factors. In addition, among the 94 computer-designed siRNAs, an siRNA that targets both TF15 (a previously identified transcription factor that is involved in the plant disease-response pathway) and TF21 was introduced into orchids. The experimental results showed that this siRNA can simultaneously silence TF15 and TF21, and application of our strategy successfully confirmed that TF15 is involved in plant defense responses. Interestingly, our second-round analysis, which used an siRNA specific to TF21, indicated that TF21 is a previously unidentified transcription factor that is related to plant defense responses.
Our computational results showed that it is possible to screen all genes with fewer experiments than would be required for the traditional one-on-one RNAi screening. We also verified that our strategy is capable of identifying genes that are involved in a specific phenotype.
RNA interference; RNAi screening; SiRNA design; Gene functional analysis; Group testing
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 recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens.
Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos [Additional files 1, 2], dataset for cell cycle phase identification using HeLa cells [Additional files 1, 3, 4] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%–90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms.
We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens.
Systems biology aims to describe the complex interplays between cellular building blocks which, in their concurrence, give rise to the emergent properties observed in cellular behaviors and responses. This approach tries to determine the molecular players and the architectural principles of their interactions within the genetic networks that control certain biological processes. Large-scale loss-of-function screens, applicable in various different model systems, have begun to systematically interrogate entire genomes to identify the genes that contribute to a certain cellular response. In particular, RNA interference (RNAi)-based high-throughput screens have been instrumental in determining the composition of regulatory systems and paired with integrative data analyses have begun to delineate the genetic networks that control cell biological and developmental processes. Through the creation of tools for both, in vitro and in vivo genome-wide RNAi screens, Drosophila melanogaster has emerged as one of the key model organisms in systems biology research and over the last years has massively contributed to and hence shaped this discipline.
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.
Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA interference (RNAi). Cell population-based studies deviate from conventional microscopy studies on a few cells, and could provide stronger statistical power for drawing experimental observations and conclusions. However, it is challenging to manually extract and quantify phenotypic changes from the large amounts of complex image data generated. Thus, bioimage informatics approaches are needed to rapidly and objectively quantify and analyze the image data. This paper provides an overview of the bioimage informatics challenges and approaches in image-based studies for drug and target discovery. The concepts and capabilities of image-based screening are first illustrated by a few practical examples investigating different kinds of phenotypic changes caEditorsused by drugs, compounds, or RNAi. The bioimage analysis approaches, including object detection, segmentation, and tracking, are then described. Subsequently, the quantitative features, phenotype identification, and multidimensional profile analysis for profiling the effects of drugs and targets are summarized. Moreover, a number of publicly available software packages for bioimage informatics are listed for further reference. It is expected that this review will help readers, including those without bioimage informatics expertise, understand the capabilities, approaches, and tools of bioimage informatics and apply them to advance their own studies.
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.
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
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.
RNAi screen; transcription factor; reporter; localization; post-translational modification
RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery and, in both of these areas, large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow.