Drosophila larval skeletal muscles are single, multinucleated cells of different sizes that undergo tremendous growth within a few days. The mechanisms underlying this growth in concert with overall body growth are unknown. We find that the size of individual muscles correlates with the number of nuclei per muscle cell and with increasing nuclear ploidy during development. Inhibition of Insulin receptor (InR; Insulin-like receptor) signaling in muscles autonomously reduces muscle size and systemically affects the size of other tissues, organs and indeed the entire body, most likely by regulating feeding behavior. In muscles, InR/Tor signaling, Foxo and dMyc (Diminutive) are key regulators of endoreplication, which is necessary but not sufficient to induce growth. Mechanistically, InR/Foxo signaling controls cell cycle progression by modulating dmyc expression and dMyc transcriptional activity. Thus, maximal dMyc transcriptional activity depends on InR to control muscle mass, which in turn induces a systemic behavioral response to allocate body size and proportions.

In Drosophila melanogaster, widely used mitotic recombination-based strategies generate mosaic flies with positive readout for only one daughter cell after division. To differentially label both daughter cells, we developed the twin spot generator (TSG) technique, which through mitotic recombination generates green and red twin spots that are detectable after the first cell division as single cells. We propose wide applications of TSG to lineage and genetic mosaic studies.

A label-free quantification strategy including the development of in-house software (NakedQuant) to calculate the average TIC across all spectral counts in tandem affinity purification (TAP)-tagging liquid chromatography-mass spectrometry MS/MS (LC/MS/MS) experiments was applied to a large-scale study of protein complexes in the MAPK portion of the insulin signaling pathway from Drosophila cells. Dynamics were calculated under basal and stimulating conditions as fold changes. These experiments were performed in the context of a core service model with the user performing the TAP immunoprecipitation and the MS core performing the MS and informatics stops. The MS strategy showed excellent coverage of known components in addition to potentially novel interactions.

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

The specificity of RNAi pathways is determined by several classes of small RNAs, which include siRNAs, piRNAs, endo-siRNAs, and microRNAs (miRNAs). These small RNAs are invariably incorporated into large Argonaute (Ago)-containing effector complexes known as RNA-induced silencing complexes (RISCs), which they guide to silencing targets. Both genetic and biochemical strategies have yielded conserved molecular components of small RNA biogenesis and effector machineries. However, given the complexity of these pathways, there are likely to be additional components and regulators that remain to be uncovered. We have undertaken a comparative and comprehensive RNAi screen to identify genes that impact three major Ago-dependent small RNA pathways that operate in Drosophila S2 cells. We identify subsets of candidates that act positively or negatively in siRNA, endo-siRNA, and miRNA pathways. Our studies indicate that many components are shared among all three Argonaute-dependent silencing pathways, though each is also impacted by discrete sets of genes.

Drosophila endogenous small RNAs are categorized according to their mechanisms of biogenesis and the Argonaute protein to which they bind. MicroRNAs are a class of ubiquitously expressed RNAs of approximately 22 nucleotides in length, which arise from structured precursors through the action of Drosha-Pasha and Dicer-1-Loquacious complexes. These join Argonaute-1 to regulate gene expression. A second endogenous small RNA class, the Piwi-interacting RNAs, bind Piwi proteins and suppress transposons. Piwi-interacting RNAs are restricted to the gonad, and at least a subset of these arises by Piwi-catalysed cleavage of single-stranded RNAs. Here we show that Drosophila generates a third small RNA class, endogenous small interfering RNAs, in both gonadal and somatic tissues. Production of these RNAs requires Dicer-2, but a subset depends preferentially on Loquacious rather than the canonical Dicer-2 partner, R2D2 (ref. 14). Endogenous small interfering RNAs arise both from convergent transcription units and from structured genomic loci in a tissue-specific fashion. They predominantly join Argonaute-2 and have the capacity, as a class, to target both protein-coding genes and mobile elements. These observations expand the repertoire of small RNAs in Drosophila, adding a class that blurs distinctions based on known biogenesis mechanisms and functional roles.

A major obstacle to creating precisely expressed transgenes lies in the epigenetic effects of the host chromatin that surrounds them. Here we present a strategy to overcome this problem, employing a Gal4-inducible luciferase assay to systematically quantify position effects of host chromatin and the ability of insulators to counteract these effects at phiC31 integration loci randomly distributed throughout the Drosophila genome. We identify loci that can be exploited to deliver precise doses of transgene expression to specific tissues. Moreover, we uncover a previously unrecognized property of the gypsy retrovirus insulator to boost gene expression to levels severalfold greater than at most or possibly all un-insulated loci, in every tissue tested. These findings provide the first opportunity to create a battery of transgenes that can be reliably expressed at high levels in virtually any tissue by integration at a single locus, and conversely, to engineer a controlled phenotypic allelic series by exploiting several loci. The generality of our approach makes it adaptable to other model systems to identify and modify loci for optimal transgene expression.

A fundamental concept in development is that secreted molecules such as Wingless (Wg) and Hedgehog (Hh) generate pattern by inducing cell fate. By following markers of cellular identity posterior to the Wg- and Hh-expressing cells in the Drosophila dorsal embryonic epidermis, we provide evidence that neither Wg nor Hh specifies the identity of the cell types they pattern. Rather, they maintain pre-existing cellular identities that are otherwise unstable and progress stepwise towards a default fate. Wg and Hh therefore generate pattern by inhibiting specific switches in cell identity, showing that the specification and the patterning of a given cell are uncoupled. Sequential binary decisions without induction of cell identity give rise to both the groove cells and their posterior neighbors. The combination of independent progression of cell identity and arrest of progression by signals facilitates accurate patterning of an extremely plastic developing epidermis.

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.

We report a technique for fluorescence tomography that operates beyond the penetration limits of tissue-sectioning fluorescence microscopy. The method uses multi-projection illumination and photon transport description in opaque tissues. We demonstrate whole-body three-dimensional visualization of the morphogenesis of GFP-expressing salivary glands and wing imaginal discs in living Drosophila melanogaster pupae in vivo and over time.

Cellular signaling networks have evolved to enable swift and accurate responses, even in the face of genetic or environmental perturbation. Thus, genetic screens may not identify all the genes that regulate different biological processes. Moreover, although classical screening approaches have succeeded in providing parts lists of the essential components of signaling networks, they typically do not provide much insight into the hierarchical and functional relations that exist among these components. We describe a high-throughput screen in which we used RNA interference to systematically inhibit two genes simultaneously in 17,724 combinations to identify regulators of Drosophila JUN NH(2)-terminal kinase (JNK). Using both genetic and phosphoproteomics data, we then implemented an integrative network algorithm to construct a JNK phosphorylation network, which provides structural and mechanistic insights into the systems architecture of JNK signaling.

To facilitate the genetic analysis of muscle assembly and maintenance, we have developed a method for efficient RNA interference (RNAi) in Drosophila primary cells using double-stranded RNAs (dsRNAs). First, using molecular markers, we confirm and extend the observation that myogenesis in primary cultures derived from Drosophila embryonic cells follows the same developmental course as that seen in vivo. Second, we apply this approach to analyze 28 Drosophila homologs of human muscle disease genes and find that 19 of them, when disrupted, lead to abnormal muscle phenotypes in primary culture. Third, from an RNAi screen of 1140 genes chosen at random, we identify 49 involved in late muscle differentiation. We validate our approach with the in vivo analyses of three genes. We find that Fermitin 1 and Fermitin 2, which are involved in integrin-containing adhesion structures, act in a partially redundant manner to maintain muscle integrity. In addition, we characterize CG2165, which encodes a plasma membrane Ca2+-ATPase, and show that it plays an important role in maintaining muscle integrity. Finally, we discuss how Drosophila primary cells can be manipulated to develop cell-based assays to model human diseases for RNAi and small-molecule screens.

BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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.

Nearly 1.7 billion people are infected with Mycobacterium tuberculosis. Its ability to survive intracellularly is thought to be central to its success as a pathogen, but how it does this is poorly understood. Using a Drosophila model of infection, we identify three host cell activities, Rab7, CG8743, and the ESCRT machinery, that modulate the mycobacterial phagosome. In the absence of these factors the cell no longer restricts growth of the non-pathogen Mycobacterium smegmatis. Hence, we identify factors that represent unique vulnerabilities of the host cell, because manipulation of any one of them alone is sufficient to allow a nonpathogenic mycobacterial species to proliferate. Furthermore, we demonstrate that, in mammalian cells, the ESCRT machinery plays a conserved role in restricting bacterial growth.

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.

JAK/STAT signaling is essential for a wide range of developmental processes in Drosophila melanogaster. The mechanism by which the JAK/STAT pathway contributes to these processes has been the subject of recent investigation. However, a reporter that reflects activity of the JAK/STAT pathway in all Drosophila tissues has not yet been developed. By placing a fragment of the Stat92E target gene Socs36E, which contains at least two putative Stat92E binding sites, upstream of GFP, we generated three constructs that can be used to monitor JAK/STAT pathway activity in vivo. These constructs differ by the number of Stat92E binding sites and the stability of GFP. The 2XSTAT92E-GFP and 10XSTAT92E-GFP constructs contain 2 and 10 Stat92E binding sites, respectively, driving expression of enhanced GFP, while 10XSTAT92E-DGFP drives expression of destabilized GFP. We show that these reporters are expressed in the embryo in an overlapping pattern with Stat92E protein and in tissues where JAK/STAT signaling is required. In addition, these reporters accurately reflect JAK/STAT pathway activity at larval stages, as their expression pattern overlaps that of the activating ligand unpaired in imaginal discs. Moreover, the STAT92E-GFP reporters are activated by ectopic JAK/STAT signaling. STAT92E-GFP fluorescence is increased in response to ectopic upd in the larval eye disc and mis-expression of the JAK kinase hopscotch in the adult fat body. Lastly, these reporters are specifically activated by Stat92E, as STAT92E-GFP reporter expression is lost cell-autonomously in stat92E homozygous mutant tissue. In sum, we have generated in vivo GFP reporters that accurately reflect JAK/STAT pathway activation in a variety of tissues. These reporters are valuable tools to further investigate and understand the role of JAK/STAT signaling in Drosophila.

Although classical genetic and biochemical approaches have identified hundreds of proteins that function in the dynamic remodeling of cell shape in response to upstream signals, there is currently little systems-level understanding of the organization and composition of signaling networks that regulate cell morphology. We have developed quantitative morphological profiling methods to systematically investigate the role of individual genes in the regulation of cell morphology in a fast, robust, and cost-efficient manner. We analyzed a compendium of quantitative morphological signatures and described the existence of local signaling networks that act to regulate cell protrusion, adhesion, and tension.

Organization of actin filaments into a well-organized sarcomere structure is critical for muscle development and function. However, it is not completely understood how sarcomeric actin/thin filaments attain their stereotyped lengths. In an RNAi screen in Drosophila primary muscle cells, we identified a gene, sarcomere length short (sals), which encodes an actin-binding, WH2 domain-containing protein, required for proper sarcomere size. When sals is knocked down by RNAi, primary muscles display thin myofibrils with shortened sarcomeres and increased sarcomere number. Both loss- and gain-of-function analyses indicate that SALS may influence sarcomere lengths by promoting thin-filament lengthening from pointed ends. Furthermore, the complex localization of SALS and other sarcomeric proteins in myofibrils reveals that the full length of thin filaments is achieved in a two-step process, and that SALS is required for the second elongation phase, most likely because it antagonizes the pointed-end capping protein Tropomodulin.

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.