Vinegoni C, Pitsouli C, Razansky D, Perrimon N, Ntziachristos V.
In vivo imaging of Drosophila melanogaster pupae with mesoscopic fluorescence tomography. Nat Methods. 2008;5 (1) :45-7.
AbstractWe 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.
2008_Nat Meth_Vinegoni.pdf Supplement.pdf Bakal C, Linding R, Llense F, Heffern E, Martin-Blanco E, Pawson T, et al. Phosphorylation networks regulating JNK activity in diverse genetic backgrounds. Science. 2008;322 (5900) :453-6.
Abstract
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.
2008_Science_Bakal.pdf Supplement.pdf Bai J, Binari R, Ni J-Q, Vijayakanthan M, Li H-S, Perrimon N.
RNA interference screening in Drosophila primary cells for genes involved in muscle assembly and maintenance. Development. 2008;135 (8) :1439-49.
AbstractTo 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_Development_Bai.pdf Supplemental Files.zip Yin Z, Zhou X, Bakal C, Li F, Sun Y, Perrimon N, et al. Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens. BMC Bioinformatics. 2008;9 :264.
AbstractBACKGROUND: 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.
2008_BMC Bioinfo_Yin.pdf Supplement.pdf Philips JA, Porto MC, Wang H, Rubin EJ, Perrimon N.
ESCRT factors restrict mycobacterial growth. Proc Natl Acad Sci U S A. 2008;105 (8) :3070-5.
Abstract
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.
2008_PNAS_Philips.pdf Supplemental Files.zip Ni J-Q, Markstein M, Binari R, Pfeiffer B, Liu L-P, Villalta C, et al. Vector and parameters for targeted transgenic RNA interference in Drosophila melanogaster. Nat Methods. 2008;5 (1) :49-51.
AbstractThe 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