The transforming growth factor-β (TGF-β) family of cytokines figures prominently in regulation of embryonic development and adult tissue homeostasis from Drosophila to mammals. Genetic defects affecting TGF-β signaling underlie developmental disorders and diseases such as cancer in human. Therefore, delineating the molecular mechanism by which TGF-β regulates cell biology is critical for understanding normal biology and disease mechanisms. Forward genetic screens in model organisms and biochemical approaches in mammalian tissue culture were instrumental in initial characterization of the TGF-β signal transduction pathway. With complete sequence information of the genomes and the advent of RNA interference (RNAi) technology, genome-wide RNAi screening emerged as a powerful functional genomics approach to systematically delineate molecular components of signal transduction pathways. Here, we describe a protocol for image-based whole-genome RNAi screening aimed at identifying molecules required for TGF-β signaling into the nucleus. Using this protocol we examined >90 % of annotated Drosophila open reading frames (ORF) individually and successfully uncovered several novel factors serving critical roles in the TGF-β pathway. Thus cell-based high-throughput functional genomics can uncover new mechanistic insights on signaling pathways beyond what the classical genetics had revealed.

Our understanding of the genetic mechanisms that underlie biological processes has relied extensively on loss-of-function (LOF) analyses. LOF methods target DNA, RNA or protein to reduce or to ablate gene function. By analysing the phenotypes that are caused by these perturbations the wild-type function of genes can be elucidated. Although all LOF methods reduce gene activity, the choice of approach (for example, mutagenesis, CRISPR-based gene editing, RNA interference, morpholinos or pharmacological inhibition) can have a major effect on phenotypic outcomes. Interpretation of the LOF phenotype must take into account the biological process that is targeted by each method. The practicality and efficiency of LOF methods also vary considerably between model systems. We describe parameters for choosing the optimal combination of method and system, and for interpreting phenotypes within the constraints of each method.

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.

This chapter describes the method used to conduct high-throughput screening (HTs) by RNA interference in Drosophila tissue culture cells. It covers four main topics: (1) a brief description of the existing platforms to conduct RNAi-screens in cell-based assays; (2) a table of the Drosophila cell lines available for these screens and a brief mention of the need to establish other cell lines as well as cultures of primary cells; (3) a discussion of the considerations and protocols involved in establishing assays suitable for HTS in a 384-well format; and (A) a summary of the various ways of handling raw data from an ongoing screen, with special emphasis on how to apply normalization for experimental variation and statistical filters to sort out noise from signals.

The evaluation of specific endogenous transcript levels is important for understanding transcriptional regulation. More specifically, it is useful for independent confirmation of results obtained by the use of microarray analysis or RNA-seq and for evaluating RNA interference (RNAi)-mediated gene knockdown. Designing specific and effective primers for high-quality, moderate-throughput evaluation of transcript levels, i.e., quantitative, real-time PCR (qPCR), is nontrivial. To meet community needs, predefined qPCR primer pairs for mammalian genes have been designed and sequences made available, e.g., via PrimerBank. In this work, we adapted and refined the algorithms used for the mammalian PrimerBank to design 45,417 primer pairs for 13,860 Drosophila melanogaster genes, with three or more primer pairs per gene. We experimentally validated primer pairs for ~300 randomly selected genes expressed in early Drosophila embryos, using SYBR Green-based qPCR and sequence analysis of products derived from conventional PCR. All relevant information, including primer sequences, isoform specificity, spatial transcript targeting, and any available validation results and/or user feedback, is available from an online database (www.flyrnai.org/flyprimerbank). At FlyPrimerBank, researchers can retrieve primer information for fly genes either one gene at a time or in batch mode. Importantly, we included the overlap of each predicted amplified sequence with RNAi reagents from several public resources, making it possible for researchers to choose primers suitable for knockdown evaluation of RNAi reagents (i.e., to avoid amplification of the RNAi reagent itself). We demonstrate the utility of this resource for validation of RNAi reagents in vivo.

RNA interference has re-energized the field of functional genomics by enabling genome-scale loss-of-function screens in cultured cells. Looking back on the lessons that have been learned from the first wave of technology developments and applications in this exciting field, we provide both a user's guide for newcomers to the field and a detailed examination of some more complex issues, particularly concerning optimization and quality control, for more advanced users. From a discussion of cell lines, screening paradigms, reagent types and read-out methodologies, we explore in particular the complexities of designing optimal controls and normalization strategies for these challenging but extremely powerful studies.

Drosophila melanogaster has become a system of choice for functional genomic studies. Many resources, including online databases and software tools, are now available to support design or identification of relevant fly stocks and reagents or analysis and mining of existing functional genomic, transcriptomic, proteomic, etc. datasets. These include large community collections of fly stocks and plasmid clones, "meta" information sites like FlyBase and FlyMine, and an increasing number of more specialized reagents, databases, and online tools. Here, we introduce key resources useful to plan large-scale functional genomics studies in Drosophila and to analyze, integrate, and mine the results of those studies in ways that facilitate identification of highest-confidence results and generation of new hypotheses. We also discuss ways in which existing resources can be used and might be improved and suggest a few areas of future development that would further support large- and small-scale studies in Drosophila and facilitate use of Drosophila information by the research community more generally.

This protocol describes the various steps and considerations involved in planning and carrying out RNA interference (RNAi) genome-wide screens in cultured Drosophila cells. We focus largely on the procedures that have been modified as a result of our experience over the past 3 years and of our better understanding of the underlying technology. Specifically, our protocol offers a set of suggestions and considerations for screen optimization and a step-by-step description of the procedures successfully used at the Drosophila RNAi Screening Center for screen implementation, data collection and analysis to identify potential hits. In addition, this protocol briefly covers postscreen analysis approaches that are often needed to finalize the hit list. Depending on the scope of the screen and subsequent analysis and validation involved, the full protocol can take anywhere from 3 months to 2 years to complete.