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Subcell Biochem,
2012]
Caenorhabditis elegans provides a simplified, in vivo model system in which to study adherens junctions (AJs) and their role in morphogenesis. The core AJ components-HMR-1/E-cadherin, HMP-2/-catenin and HMP-1/-catenin-were initially identified through genetic screens for mutants with body axis elongation defects. In early embryos, AJ proteins are found at sites of contact between blastomeres, and in epithelial cells AJ proteins localize to the multifaceted apical junction (CeAJ)-a single structure that combines the adhesive and barrier functions of vertebrate adherens and tight junctions. The apically localized polarity proteins PAR-3 and PAR-6 mediate formation and maturation of junctions, while the basolaterally localized regulator LET-413/Scribble ensures that junctions remain apically positioned. AJs promote robust adhesion between epithelial cells and provide mechanical resistance for the physical strains of morphogenesis. However, in contrast to vertebrates, C. elegans AJ proteins are not essential for general cell adhesion or for epithelial cell polarization. A combination of conserved and novel proteins localizes to the CeAJ and works together with AJ proteins to mediate adhesion.
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Biochim Biophys Acta,
2017]
The development of new microscopy techniques for super-resolved, long-term monitoring of cellular and subcellular dynamics in living organisms is revealing new fundamental aspects of tissue development and repair. However, new microscopy approaches present several challenges. In addition to unprecedented requirements for data storage, the analysis of high resolution, time-lapse images is too complex to be done manually. Machine learning techniques are ideally suited for the (semi-)automated analysis of multidimensional image data. In particular, support vector machines (SVMs), have emerged as an efficient method to analyze microscopy images obtained from animals. Here, we discuss the use of SVMs to analyze in vivo microscopy data. We introduce the mathematical framework behind SVMs, and we describe the metrics used by SVMs and other machine learning approaches to classify image data. We discuss the influence of different SVM parameters in the context of an algorithm for cell segmentation and tracking. Finally, we describe how the application of SVMs has been critical to study protein localization in yeast screens, for lineage tracing in C. elegans, or to determine the developmental stage of Drosophila embryos to investigate gene expression dynamics. We propose that SVMs will become central tools in the analysis of the complex image data that novel microscopy modalities have made possible. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.