Method or protocol

2013
Xingjie Ren, Jin Sun, Benjamin E Housden, Yanhui Hu, Charles Roesel, Shuailiang Lin, Lu-Ping Liu, Zhihao Yang, Decai Mao, Lingzhu Sun, Qujie Wu, Jun-Yuan Ji, Jianzhong Xi, Stephanie E Mohr, Jiang Xu, Norbert Perrimon, and Jian-Quan Ni. 2013. “Optimized gene editing technology for Drosophila melanogaster using germ line-specific Cas9.” Proc Natl Acad Sci U S A, 110, 47, Pp. 19012-7.Abstract

The ability to engineer genomes in a specific, systematic, and cost-effective way is critical for functional genomic studies. Recent advances using the CRISPR-associated single-guide RNA system (Cas9/sgRNA) illustrate the potential of this simple system for genome engineering in a number of organisms. Here we report an effective and inexpensive method for genome DNA editing in Drosophila melanogaster whereby plasmid DNAs encoding short sgRNAs under the control of the U6b promoter are injected into transgenic flies in which Cas9 is specifically expressed in the germ line via the nanos promoter. We evaluate the off-targets associated with the method and establish a Web-based resource, along with a searchable, genome-wide database of predicted sgRNAs appropriate for genome engineering in flies. Finally, we discuss the advantages of our method in comparison with other recently published approaches.

2013_PNAS_Ren.pdf Supplement.pdf
Arunachalam Vinayagam, Yanhui Hu, Meghana Kulkarni, Charles Roesel, Richelle Sopko, Stephanie E Mohr, and Norbert Perrimon. 2013. “Protein complex-based analysis framework for high-throughput data sets.” Sci Signal, 6, 264, Pp. rs5.Abstract

Analysis of high-throughput data increasingly relies on pathway annotation and functional information derived from Gene Ontology. This approach has limitations, in particular for the analysis of network dynamics over time or under different experimental conditions, in which modules within a network rather than complete pathways might respond and change. We report an analysis framework based on protein complexes, which are at the core of network reorganization. We generated a protein complex resource for human, Drosophila, and yeast from the literature and databases of protein-protein interaction networks, with each species having thousands of complexes. We developed COMPLEAT (http://www.flyrnai.org/compleat), a tool for data mining and visualization for complex-based analysis of high-throughput data sets, as well as analysis and integration of heterogeneous proteomics and gene expression data sets. With COMPLEAT, we identified dynamically regulated protein complexes among genome-wide RNA interference data sets that used the abundance of phosphorylated extracellular signal-regulated kinase in cells stimulated with either insulin or epidermal growth factor as the output. The analysis predicted that the Brahma complex participated in the insulin response.

2013_Sci Sig_Vinayagam.pdf Supplemental Files.zip
Yanhui Hu, Charles Roesel, Ian Flockhart, Lizabeth Perkins, Norbert Perrimon, and Stephanie E Mohr. 2013. “UP-TORR: online tool for accurate and Up-to-Date annotation of RNAi Reagents.” Genetics, 195, 1, Pp. 37-45.Abstract

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.

2013_Genetics_Hu.pdf Supplement.pdf
2012
Marcelo Perez-Pepe, Victoria Slomiansky, Mariela Loschi, Luciana Luchelli, Maximiliano Neme, María Gabriela Thomas, and Graciela Lidia Boccaccio. 2012. “BUHO: a MATLAB script for the study of stress granules and processing bodies by high-throughput image analysis.” PLoS One, 7, 12, Pp. e51495.Abstract

The spontaneous and reversible formation of foci and filaments that contain proteins involved in different metabolic processes is common in both the nucleus and the cytoplasm. Stress granules (SGs) and processing bodies (PBs) belong to a novel family of cellular structures collectively known as mRNA silencing foci that harbour repressed mRNAs and their associated proteins. SGs and PBs are highly dynamic and they form upon stress and dissolve thus releasing the repressed mRNAs according to changes in cell physiology. In addition, aggregates containing abnormal proteins are frequent in neurodegenerative disorders. In spite of the growing relevance of these supramolecular aggregates to diverse cellular functions a reliable automated tool for their systematic analysis is lacking. Here we report a MATLAB Script termed BUHO for the high-throughput image analysis of cellular foci. We used BUHO to assess the number, size and distribution of distinct objects with minimal deviation from manually obtained parameters. BUHO successfully addressed the induction of both SGs and PBs in mammalian and insect cells exposed to different stress stimuli. We also used BUHO to assess the dynamics of specific mRNA-silencing foci termed Smaug 1 foci (S-foci) in primary neurons upon synaptic stimulation. Finally, we used BUHO to analyze the role of candidate genes on SG formation in an RNAi-based experiment. We found that FAK56D, GCN2 and PP1 govern SG formation. The role of PP1 is conserved in mammalian cells as judged by the effect of the PP1 inhibitor salubrinal, and involves dephosphorylation of the translation factor eIF2α. All these experiments were analyzed manually and by BUHO and the results differed in less than 5% of the average value. The automated analysis by this user-friendly method will allow high-throughput image processing in short times by providing a robust, flexible and reliable alternative to the laborious and sometimes unfeasible visual scrutiny.

2012_PLOS One_Perez-Pepe.pdf Supplemental Files.zip
2011
Matthew Booker, Anastasia A Samsonova, Young Kwon, Ian Flockhart, Stephanie E Mohr, and Norbert Perrimon. 2011. “False negative rates in Drosophila cell-based RNAi screens: a case study.” BMC Genomics, 12, Pp. 50.Abstract

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.

2011_BMCGenomics_Booker.pdf Supplement 1.xls Supplement 2.xls
Yanhui Hu, Ian Flockhart, Arunachalam Vinayagam, Clemens Bergwitz, Bonnie Berger, Norbert Perrimon, and Stephanie E Mohr. 2011. “An integrative approach to ortholog prediction for disease-focused and other functional studies.” BMC Bioinformatics, 12, Pp. 357.Abstract

BACKGROUND: Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward. RESULTS: We report a simple but effective tool, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; http://www.flyrnai.org/diopt), for rapid identification of orthologs. DIOPT integrates existing approaches, facilitating rapid identification of orthologs among human, mouse, zebrafish, C. elegans, Drosophila, and S. cerevisiae. As compared to individual tools, DIOPT shows increased sensitivity with only a modest decrease in specificity. Moreover, the flexibility built into the DIOPT graphical user interface allows researchers with different goals to appropriately 'cast a wide net' or limit results to highest confidence predictions. DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. This helps users identify the most appropriate matches among multiple possible orthologs. To facilitate using model organisms for functional analysis of human disease-associated genes, we used DIOPT to predict high-confidence orthologs of disease genes in Online Mendelian Inheritance in Man (OMIM) and genes in genome-wide association study (GWAS) data sets. The results are accessible through the DIOPT diseases and traits query tool (DIOPT-DIST; http://www.flyrnai.org/diopt-dist). CONCLUSIONS: DIOPT and DIOPT-DIST are useful resources for researchers working with model organisms, especially those who are interested in exploiting model organisms such as Drosophila to study the functions of human disease genes.

2011_BMC Bioinfo_Hu.pdf Supplemental Files.zip
Norbert Perrimon, Jonathan Zirin, and Jianwu Bai. 2011. “Primary cell cultures from Drosophila gastrula embryos.” J Vis Exp, 48.Abstract

Here we describe a method for preparing and culturing primary cells dissociated from Drosophila gastrula embryos. In brief, a large amount of staged embryos from young and healthy flies are collected, sterilized, and then physically dissociated into a single cell suspension using a glass homogenizer. After being plated on culture plates or chamber slides at an appropriate density in culture medium, these cells can further differentiate into several morphologically-distinct cell types, which can be identified by their specific cell markers. Furthermore, we present conditions for treating these cells with double stranded (ds) RNAs to elicit gene knockdown. Efficient RNAi in Drosophila primary cells is accomplished by simply bathing the cells in dsRNA-containing culture medium. The ability to carry out effective RNAi perturbation, together with other molecular, biochemical, cell imaging analyses, will allow a variety of questions to be answered in Drosophila primary cells, especially those related to differentiated muscle and neuronal cells.

2011_J Vis Exp_Perrimon.pdf
2010
Chaohong Wu, Joost Schulte, Katharine J Sepp, Troy J Littleton, and Pengyu Hong. 2010. “Automatic robust neurite detection and morphological analysis of neuronal cell cultures in high-content screening.” Neuroinformatics, 8, 2, Pp. 83-100.Abstract

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.

2010_Neuroinfor_Wu.pdf
Michael Schnall-Levin, Yong Zhao, Norbert Perrimon, and Bonnie Berger. 2010. “Conserved microRNA targeting in Drosophila is as widespread in coding regions as in 3'UTRs.” Proc Natl Acad Sci U S A, 107, 36, Pp. 15751-6.Abstract

MicroRNAs (miRNAs) are a class of short noncoding RNAs that regulate protein-coding genes posttranscriptionally. In animals, most known miRNA targeting occurs within the 3'UTR of mRNAs, but the extent of biologically relevant targeting in the ORF or 5'UTR of mRNAs remains unknown. Here, we develop an algorithm (MinoTar-miRNA ORF Targets) to identify conserved regulatory motifs within protein-coding regions and use it to estimate the number of preferentially conserved miRNA-target sites in ORFs. We show that, in Drosophila, preferentially conserved miRNA targeting in ORFs is as widespread as it is in 3'UTRs and that, while far less abundant, conserved targets in Drosophila 5'UTRs number in the hundreds. Using our algorithm, we predicted a set of high-confidence ORF targets and selected seven miRNA-target pairs from among these for experimental validation. We observed down-regulation by the miRNA in five out of seven cases, indicating our approach can recover functional sites with high confidence. Additionally, we observed additive targeting by multiple sites within a single ORF. Altogether, our results demonstrate that the scale of biologically important miRNA targeting in ORFs is extensive and that computational tools such as ours can aid in the identification of such targets. Further evidence suggests that our results extend to mammals, but that the extent of ORF and 5'UTR targeting relative to 3'UTR targeting may be greater in Drosophila.

2010_PNAS_Schnall-Levin.pdf Supplement.pdf
2009
Jianwu Bai, Katharine J Sepp, and Norbert Perrimon. 2009. “Culture of Drosophila primary cells dissociated from gastrula embryos and their use in RNAi screening.” Nat Protoc, 4, 10, Pp. 1502-12.Abstract

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.

2009_Nat Prot_Bai.pdf
Dashnamoorthy Ravi, Amy M Wiles, Selvaraj Bhavani, Jianhua Ruan, Philip Leder, and Alexander JR Bishop. 2009. “A network of conserved damage survival pathways revealed by a genomic RNAi screen.” PLoS Genet, 5, 6, Pp. e1000527.Abstract

Damage initiates a pleiotropic cellular response aimed at cellular survival when appropriate. To identify genes required for damage survival, we used a cell-based RNAi screen against the Drosophila genome and the alkylating agent methyl methanesulphonate (MMS). Similar studies performed in other model organisms report that damage response may involve pleiotropic cellular processes other than the central DNA repair components, yet an intuitive systems level view of the cellular components required for damage survival, their interrelationship, and contextual importance has been lacking. Further, by comparing data from different model organisms, identification of conserved and presumably core survival components should be forthcoming. We identified 307 genes, representing 13 signaling, metabolic, or enzymatic pathways, affecting cellular survival of MMS-induced damage. As expected, the majority of these pathways are involved in DNA repair; however, several pathways with more diverse biological functions were also identified, including the TOR pathway, transcription, translation, proteasome, glutathione synthesis, ATP synthesis, and Notch signaling, and these were equally important in damage survival. Comparison with genomic screen data from Saccharomyces cerevisiae revealed no overlap enrichment of individual genes between the species, but a conservation of the pathways. To demonstrate the functional conservation of pathways, five were tested in Drosophila and mouse cells, with each pathway responding to alkylation damage in both species. Using the protein interactome, a significant level of connectivity was observed between Drosophila MMS survival proteins, suggesting a higher order relationship. This connectivity was dramatically improved by incorporating the components of the 13 identified pathways within the network. Grouping proteins into "pathway nodes" qualitatively improved the interactome organization, revealing a highly organized "MMS survival network." We conclude that identification of pathways can facilitate comparative biology analysis when direct gene/orthologue comparisons fail. A biologically intuitive, highly interconnected MMS survival network was revealed after we incorporated pathway data in our interactome analysis.

2009_PLOS Gen_Dashnamoorthy.pdf Supplemental Files.zip
2008
Jianwu Bai, Richard Binari, Jian-Quan Ni, Marina Vijayakanthan, Hong-Sheng Li, and Norbert Perrimon. 2008. “RNA interference screening in Drosophila primary cells for genes involved in muscle assembly and maintenance.” Development, 135, 8, Pp. 1439-49.Abstract

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.

2008_Dev_Bai.pdf Supplement.pdf Movie S1.mov Movie S2.mov
Jian-Quan Ni, Michele Markstein, Richard Binari, Barret Pfeiffer, Lu-Ping Liu, Christians Villalta, Matthew Booker, Lizabeth Perkins, and Norbert Perrimon. 2008. “Vector and parameters for targeted transgenic RNA interference in Drosophila melanogaster.” Nat Methods, 5, 1, Pp. 49-51.Abstract

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.

2008_Nat Meth_Ni.pdf Supplement.pdf
2007
Ramanuj DasGupta, Kent Nybakken, Matthew Booker, Bernard Mathey-Prevot, Foster Gonsalves, Binita Changkakoty, and Norbert Perrimon. 2007. “A case study of the reproducibility of transcriptional reporter cell-based RNAi screens in Drosophila.” Genome Biol, 8, 9, Pp. R203.Abstract

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.

Nadire Ramadan, Ian Flockhart, Matthew Booker, Norbert Perrimon, and Bernard Mathey-Prevot. 2007. “Design and implementation of high-throughput RNAi screens in cultured Drosophila cells.” Nat Protoc, 2, 9, Pp. 2245-64.Abstract

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.

2007_Nat Prot_Ramadan.pdf
2005
Susan Armknecht, Michael Boutros, Amy Kiger, Kent Nybakken, Bernard Mathey-Prevot, and Norbert Perrimon. 2005. “High-throughput RNA interference screens in Drosophila tissue culture cells.” Methods Enzymol, 392, Pp. 55-73.Abstract

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.

2005_Methods Enzym_Armknecht.pdf

Pages