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1.  Serrano (Sano) Functions with the Planar Cell Polarity Genes to Control Tracheal Tube Length 
PLoS Genetics  2009;5(11):e1000746.
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.
Author Summary
Tubular organ formation is a ubiquitous process required to sustain life in multicellular organisms. In this study, we focused on the tracheal system of the fruit fly, Drosophila melanogaster, and identified Serrano (Sano) as a novel protein expressed in several embryonic tubular organs, including trachea. sano loss results in over-elongated trachea, whereas Sano overexpression causes shortened trachea, suggesting that sano is required for proper tracheal tube length. Interestingly, Sano overexpression results in typical planar cell polarity (PCP) defects in many adult tissues and pupal wing cells. The PCP pathway is highly conserved from flies to mammals and it has been known to control cell polarity within the plane of epithelial tissues. Importantly, we found that Sano binds Dishevelled (Dsh), a key PCP regulator, and loss or ectopic expression of many known PCP proteins in the trachea give rise to similar defects observed with loss or gain of sano, suggesting a new role for the PCP genes in tube length control. Interestingly, the changes in tube length and PCP defects in the wing were linked to changes in apical domain size, suggesting that Sano and the PCP components affect either membrane recycling and/or the linkage of the membrane to the cytoskeleton.
PMCID: PMC2776533  PMID: 19956736
2.  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 Kc167 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.
PMCID: PMC2763194  PMID: 18547870
High-content screening; Image score inference

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