The way in which cells adopt different morphologies is not fully understood. Cell shape could be a continuous variable or restricted to a set of discrete forms. We developed quantitative methods to describe cell shape and show that Drosophila haemocytes in culture are a heterogeneous mixture of five discrete morphologies. In an RNAi screen of genes affecting the morphological complexity of heterogeneous cell populations, we found that most genes regulate the transition between discrete shapes rather than generating new morphologies. In particular, we identified a subset of genes, including the tumour suppressor PTEN, that decrease the heterogeneity of the population, leading to populations enriched in rounded or elongated forms. We show that these genes have a highly conserved function as regulators of cell shape in both mouse and human metastatic melanoma cells.

RNA interference (RNAi) is a widely adopted tool for loss-of-function studies but RNAi results only have biological relevance if the reagents are appropriately mapped to genes. Several groups have designed and generated RNAi reagent libraries for studies in cells or in vivo for Drosophila and other species. At first glance, matching RNAi reagents to genes appears to be a simple problem, as each reagent is typically designed to target a single gene. In practice, however, the reagent-gene relationship is complex. Although the sequences of oligonucleotides used to generate most types of RNAi reagents are static, the reference genome and gene annotations are regularly updated. Thus, at the time a researcher chooses an RNAi reagent or analyzes RNAi data, the most current interpretation of the RNAi reagent-gene relationship, as well as related information regarding specificity (e.g., predicted off-target effects), can be different from the original interpretation. Here, we describe a set of strategies and an accompanying online tool, UP-TORR (for Updated Targets of RNAi Reagents; www.flyrnai.org/up-torr), useful for accurate and up-to-date annotation of cell-based and in vivo RNAi reagents. Importantly, UP-TORR automatically synchronizes with gene annotations daily, retrieving the most current information available, and for Drosophila, also synchronizes with the major reagent collections. Thus, UP-TORR allows users to choose the most appropriate RNAi reagents at the onset of a study, as well as to perform the most appropriate analyses of results of RNAi-based studies.

When cells swell in hypo-osmotic solutions, chloride-selective ion channels (Cl(swell)) activate to reduce intracellular osmolality and prevent catastrophic cell rupture. Despite intensive efforts to assign a molecular identity to the mammalian Cl(swell) channel, it remains unknown. In an unbiased genome-wide RNA interference (RNAi) screen of Drosophila cells stably expressing an anion-sensitive fluorescent indicator, we identify Bestrophin 1 (dBest1) as the Drosophila Cl(swell) channel. Of the 23 screen hits with mammalian homologs and predicted transmembrane domains, only RNAi specifically targeting dBest1 eliminated the Cl(swell) current (I(Clswell)). We further demonstrate the essential contribution of dBest1 to Drosophila I(Clswell) with the introduction of a human Bestrophin disease-associated mutation (W94C). Overexpression of the W94C construct in Drosophila cells significantly reduced the endogenous I(Clswell). We confirm that exogenous expression of dBest1 alone in human embryonic kidney (HEK293) cells creates a clearly identifiable Drosophila-like I(Clswell). In contrast, activation of mouse Bestrophin 2 (mBest2), the closest mammalian ortholog of dBest1, is swell-insensitive. The first 64 residues of dBest1 conferred swell activation to mBest2. The chimera, however, maintains mBest2-like pore properties, strongly indicating that the Bestrophin protein forms the Cl(swell) channel itself rather than functioning as an essential auxiliary subunit. dBest1 is an anion channel clearly responsive to swell; this activation depends upon its N-terminus.

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.

The pairing of homologous chromosomes is a fundamental feature of the meiotic cell. In addition, a number of species exhibit homolog pairing in nonmeiotic, somatic cells as well, with evidence for its impact on both gene regulation and double-strand break (DSB) repair. An extreme example of somatic pairing can be observed in Drosophila melanogaster, where homologous chromosomes remain aligned throughout most of development. However, our understanding of the mechanism of somatic homolog pairing remains unclear, as only a few genes have been implicated in this process. In this study, we introduce a novel high-throughput fluorescent in situ hybridization (FISH) technology that enabled us to conduct a genome-wide RNAi screen for factors involved in the robust somatic pairing observed in Drosophila. We identified both candidate "pairing promoting genes" and candidate "anti-pairing genes," providing evidence that pairing is a dynamic process that can be both enhanced and antagonized. Many of the genes found to be important for promoting pairing are highly enriched for functions associated with mitotic cell division, suggesting a genetic framework for a long-standing link between chromosome dynamics during mitosis and nuclear organization during interphase. In contrast, several of the candidate anti-pairing genes have known interphase functions associated with S-phase progression, DNA replication, and chromatin compaction, including several components of the condensin II complex. In combination with a variety of secondary assays, these results provide insights into the mechanism and dynamics of somatic pairing.

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.

While the 26S proteasome is a key proteolytic complex, little is known about how proteasome levels are maintained in higher eukaryotic cells. Here we describe an RNA interference (RNAi) screen of Drosophila melanogaster that was used to identify transcription factors that may play a role in maintaining levels of the 26S proteasome. We used an RNAi library against 993 Drosophila transcription factor genes to identify genes whose suppression in Schneider 2 cells stabilized a ubiquitin-green fluorescent protein reporter protein. This screen identified Cnc (cap 'n' collar [CNC]; basic region leucine zipper) as a candidate transcriptional regulator of proteasome component expression. In fact, 20S proteasome activity was reduced in cells depleted of cnc. Immunoblot assays against proteasome components revealed a general decline in both 19S regulatory complex and 20S proteasome subunits after RNAi depletion of this transcription factor. Transcript-specific silencing revealed that the longest of the seven transcripts for the cnc gene, cnc-C, was needed for proteasome and p97 ATPase production. Quantitative reverse transcription-PCR confirmed the role of Cnc-C in activation of transcription of genes encoding proteasome components. Expression of a V5-His-tagged form of Cnc-C revealed that the transcription factor is itself a proteasome substrate that is stabilized when the proteasome is inhibited. We propose that this single cnc gene in Drosophila resembles the ancestral gene family of mammalian nuclear factor erythroid-derived 2-related transcription factors, which are essential in regulating oxidative stress and proteolysis.

BACKGROUND: High-throughput screening using RNAi is a powerful gene discovery method but is often complicated by false positive and false negative results. Whereas false positive results associated with RNAi reagents has been a matter of extensive study, the issue of false negatives has received less attention. RESULTS: We performed a meta-analysis of several genome-wide, cell-based Drosophila RNAi screens, together with a more focused RNAi screen, and conclude that the rate of false negative results is at least 8%. Further, we demonstrate how knowledge of the cell transcriptome can be used to resolve ambiguous results and how the number of false negative results can be reduced by using multiple, independently-tested RNAi reagents per gene. CONCLUSIONS: RNAi reagents that target the same gene do not always yield consistent results due to false positives and weak or ineffective reagents. False positive results can be partially minimized by filtering with transcriptome data. RNAi libraries with multiple reagents per gene also reduce false positive and false negative outcomes when inconsistent results are disambiguated carefully.

To identify Huntington's Disease therapeutics, we conducted high-content small molecule and RNAi suppressor screens using a Drosophila primary neural culture Huntingtin model. Drosophila primary neurons offer a sensitive readout for neurotoxicty, as their neurites develop dysmorphic features in the presence of mutant polyglutamine-expanded Huntingtin compared to nonpathogenic Huntingtin. By tracking the subcellular distribution of mRFP-tagged pathogenic Huntingtin and assaying neurite branch morphology via live-imaging, we identified suppressors that could reduce Huntingtin aggregation and/or prevent the formation of dystrophic neurites. The custom algorithms we used to quantify neurite morphologies in complex cultures provide a useful tool for future high-content screening approaches focused on neurodegenerative disease models. Compounds previously found to be effective aggregation inhibitors in mammalian systems were also effective in Drosophila primary cultures, suggesting translational capacity between these models. However, we did not observe a direct correlation between the ability of a compound or gene knockdown to suppress aggregate formation and its ability to rescue dysmorphic neurites. Only a subset of aggregation inhibitors could revert dysmorphic cellular profiles. We identified lkb1, an upstream kinase in the mTOR/Insulin pathway, and four novel drugs, Camptothecin, OH-Camptothecin, 18β-Glycyrrhetinic acid, and Carbenoxolone, that were strong suppressors of mutant Huntingtin-induced neurotoxicity. Huntingtin neurotoxicity suppressors identified through our screen also restored viability in an in vivo Drosophila Huntington's Disease model, making them attractive candidates for further therapeutic evaluation.

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. WIREs Syst Biol Med 2011 3 471-478 DOI: 10.1002/wsbm.127

Cell-based high content screening (HCS) is becoming an important and increasingly favored approach in therapeutic drug discovery and functional genomics. In HCS, changes in cellular morphology and biomarker distributions provide an information-rich profile of cellular responses to experimental treatments such as small molecules or gene knockdown probes. One obstacle that currently exists with such cell-based assays is the availability of image processing algorithms that are capable of reliably and automatically analyzing large HCS image sets. HCS images of primary neuronal cell cultures are particularly challenging to analyze due to complex cellular morphology. Here we present a robust method for quantifying and statistically analyzing the morphology of neuronal cells in HCS images. The major advantages of our method over existing software lie in its capability to correct non-uniform illumination using the contrast-limited adaptive histogram equalization method; segment neuromeres using Gabor-wavelet texture analysis; and detect faint neurites by a novel phase-based neurite extraction algorithm that is invariant to changes in illumination and contrast and can accurately localize neurites. Our method was successfully applied to analyze a large HCS image set generated in a morphology screen for polyglutamine-mediated neuronal toxicity using primary neuronal cell cultures derived from embryos of a Drosophila Huntington's Disease (HD) model.

Genetic screens in the yeast Saccharomyces cerevisiae have identified many proteins involved in the secretory pathway, most of which have orthologues in higher eukaryotes. To investigate whether there are additional proteins that are required for secretion in metazoans but are absent from yeast, we used genome-wide RNA interference (RNAi) to look for genes required for secretion of recombinant luciferase from Drosophila S2 cells. This identified two novel components of the secretory pathway that are conserved from humans to plants. Gryzun is distantly related to, but distinct from, the Trs130 subunit of the TRAPP complex but is absent from S. cerevisiae. RNAi of human Gryzun (C4orf41) blocks Golgi exit. Kish is a small membrane protein with a previously uncharacterised orthologue in yeast. The screen also identified Drosophila orthologues of almost 60% of the yeast genes essential for secretion. Given this coverage, the small number of novel components suggests that contrary to previous indications the number of essential core components of the secretory pathway is not much greater in metazoans than in yeasts.

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.

RNA interference (RNAi) provides a powerful reverse genetics approach to analyze gene functions both in tissue culture and in vivo. Because of its widespread applicability and effectiveness it has become an essential part of the tool box kits of model organisms such as Caenorhabditis elegans, Drosophila, and the mouse. In addition, the use of RNAi in animals in which genetic tools are either poorly developed or nonexistent enables a myriad of fundamental questions to be asked. Here, we review the methods and applications of in vivo RNAi to characterize gene functions in model organisms and discuss their impact to the study of developmental as well as evolutionary questions. Further, we discuss the applications of RNAi technologies to crop improvement, pest control and RNAi therapeutics, thus providing an appreciation of the potential for phenomenal applications of RNAi to agriculture and medicine.

Biological networks are highly complex systems, consisting largely of enzymes that act as molecular switches to activate/inhibit downstream targets via post-translational modification. Computational techniques have been developed to perform signaling network inference using some high-throughput data sources, such as those generated from transcriptional and proteomic studies, but comparable methods have not been developed to use high-content morphological data, which are emerging principally from large-scale RNAi screens, to these ends. Here, we describe a systematic computational framework based on a classification model for identifying genetic interactions using high-dimensional single-cell morphological data from genetic screens, apply it to RhoGAP/GTPase regulation in Drosophila, and evaluate its efficacy. Augmented by knowledge of the basic structure of RhoGAP/GTPase signaling, namely, that GAPs act directly upstream of GTPases, we apply our framework for identifying genetic interactions to predict signaling relationships between these proteins. We find that our method makes mediocre predictions using only RhoGAP single-knockdown morphological data, yet achieves vastly improved accuracy by including original data from a double-knockdown RhoGAP genetic screen, which likely reflects the redundant network structure of RhoGAP/GTPase signaling. We consider other possible methods for inference and show that our primary model outperforms the alternatives. This work demonstrates the fundamental fact that high-throughput morphological data can be used in a systematic, successful fashion to identify genetic interactions and, using additional elementary knowledge of network structure, to infer signaling relations.

We provide a detailed protocol for the mass culturing of primary cells dissociated from Drosophila embryos. The advantage of this protocol over others is that we have optimized it for a robust large-scale performance that is suitable for screening. More importantly, we further present conditions to treat these cells with double stranded (ds) RNAs for gene knockdown. Efficient RNAi in Drosophila primary cells is accomplished by simply bathing the cells in dsRNA-containing culture medium. This method provides the basis for functional genomic screens in differentiated cells, such as neurons and muscles, using RNAi or small molecules. The entire protocol takes approximately 14 d, whereas the preparation of primary cells from Drosophila embryos only requires 2-4 h.

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.

Multiple centrosomes in tumor cells create the potential for multipolar divisions that can lead to aneuploidy and cell death. Nevertheless, many cancer cells successfully divide because of mechanisms that suppress multipolar mitoses. A genome-wide RNAi screen in Drosophila S2 cells and a secondary analysis in cancer cells defined mechanisms that suppress multipolar mitoses. In addition to proteins that organize microtubules at the spindle poles, we identified novel roles for the spindle assembly checkpoint, cortical actin cytoskeleton, and cell adhesion. Using live cell imaging and fibronectin micropatterns, we found that interphase cell shape and adhesion pattern can determine the success of the subsequent mitosis in cells with extra centrosomes. These findings may identify cancer-selective therapeutic targets: HSET, a normally nonessential kinesin motor, was essential for the viability of certain extra centrosome-containing cancer cells. Thus, morphological features of cancer cells can be linked to unique genetic requirements for survival.

The conditional expression of hairpin constructs in Drosophila melanogaster has emerged in recent years as a method of choice in functional genomic studies. To date, upstream activating site-driven RNA interference constructs have been inserted into the genome randomly using P-element-mediated transformation, which can result in false negatives due to variable expression. To avoid this problem, we have developed a transgenic RNA interference vector based on the phiC31 site-specific integration method.

RNA interference (RNAi) in tissue culture cells has emerged as an excellent methodology for identifying gene functions systematically and in an unbiased manner. Here, we describe how RNAi high-throughput screening (HTS) in Drosophila cells are currently being performed and emphasize the strengths and weaknesses of the approach. Further, to demonstrate the versatility of the technology, we provide examples of the various applications of the method to problems in signal transduction and cell and developmental biology. Finally, we discuss emerging technological advances that will extend RNAi-based screening methods.