Serrano (sano) functions with the planar cell polarity genes to control tracheal tube length.

Epithelial tubes are the functional units of many organs, and proper tube geometry is crucial for organ function. Here, we characterize serrano (sano), a novel cytoplasmic protein that is apically enriched in several tube-forming epithelia in Drosophila, including the tracheal system. Loss of sano results in elongated tracheae, whereas Sano overexpression causes shortened tracheae with reduced apical boundaries. Sano overexpression during larval and pupal stages causes planar cell polarity (PCP) defects in several adult tissues. In Sano-overexpressing pupal wing cells, core PCP proteins are mislocalized and prehairs are misoriented; sano loss or overexpression in the eye disrupts ommatidial polarity and rotation. Importantly, Sano binds the PCP regulator Dishevelled (Dsh), and loss or ectopic expression of many known PCP proteins in the trachea gives rise to similar defects observed with loss or gain of sano, revealing a previously unrecognized role for PCP pathway components in tube size control.

An image score inference system for RNAi genome-wide screening based on fuzzy mixture regression modeling.

With recent advances in fluorescence microscopy imaging techniques and methods of gene knock down by RNA interference (RNAi), genome-scale high-content screening (HCS) has emerged as a powerful approach to systematically identify all parts of complex biological processes. However, a critical barrier preventing fulfillment of the success is the lack of efficient and robust methods for automating RNAi image analysis and quantitative evaluation of the gene knock down effects on huge volume of HCS data. Facing such opportunities and challenges, we have started investigation of automatic methods towards the development of a fully automatic RNAi-HCS system. Particularly important are reliable approaches to cellular phenotype classification and image-based gene function estimation. We have developed a HCS analysis platform that consists of two main components: fluorescence image analysis and image scoring. For image analysis, we used a two-step enhanced watershed method to extract cellular boundaries from HCS images. Segmented cells were classified into several predefined phenotypes based on morphological and appearance features. Using statistical characteristics of the identified phenotypes as a quantitative description of the image, a score is generated that reflects gene function. Our scoring model integrates fuzzy gene class estimation and single regression models. The final functional score of an image was derived using the weighted combination of the inference from several support vector-based regression models. We validated our phenotype classification method and scoring system on our cellular phenotype and gene database with expert ground truth labeling. We built a database of high-content, 3-channel, fluorescence microscopy images of Drosophila Kc(167) cultured cells that were treated with RNAi to perturb gene function. The proposed informatics system for microscopy image analysis is tested on this database. Both of the two main components, automated phenotype classification and image scoring system, were evaluated. The robustness and efficiency of our system were validated in quantitatively predicting the biological relevance of genes.

Cellular phenotype recognition for high-content RNA interference genome-wide screening.

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.

Organ positioning in Drosophila requires complex tissue-tissue interactions.

Positioning an organ with respect to other tissues is a complex process necessary for proper anatomical development and organ function. The local environment surrounding an organ can serve both as a substrate for migration and as a source of guidance cues that direct migration. Little is known about the factors guiding Drosophila salivary gland movement or about the contacts the glands establish along their migratory path. Here, we provide a detailed description of the spatial and temporal interactions between the salivary glands and surrounding tissues during embryogenesis. The glands directly contact five other tissues: the visceral mesoderm, gastric caecae, somatic mesoderm, fat body, and central nervous system. Mutational analysis reveals that all of the tissues tested in this study are important for normal salivary gland positioning; proper differentiation of the visceral and somatic mesoderm is necessary for the glands to attain their final correct position. We also provide evidence that the segment-polarity gene, gooseberry (gsb), controls expression of signals from the developing fat body that direct posterior migration of the glands. These data further the understanding of how organ morphology and position are determined by three-dimensional constraints and guidance cues provided by neighboring tissues.

Posterior migration of the salivary gland requires an intact visceral mesoderm and integrin function.

The final overall shape of an organ and its position within the developing embryo arise as a consequence of both its intrinsic properties and its interactions with surrounding tissues. Here, we focus on the role of directed cell migration in shaping and positioning the Drosophila salivary gland. We demonstrate that the salivary gland turns and migrates along the visceral mesoderm to become properly oriented with respect to the overall embryo. We show that salivary gland posterior migration requires the activities of genes that position the visceral mesoderm precursors, such as heartless, thickveins, and tinman, but does not require a differentiated visceral mesoderm. We also demonstrate a role for integrin function in salivary gland migration. Although the mutations affecting salivary gland motility and directional migration cause defects in the final positioning of the salivary gland, most do not affect the length or diameter of the salivary gland tube. These findings suggest that salivary tube dimensions may be an intrinsic property of salivary gland cells.