![]() elegans Gene Knockout Project at the Oklahoma Medical Research Foundation, and git-1(tm1962), generated by the National Bioresource Project, Tokyo, Japan. elegans knockout consortium for the git-1(ok1848) deletion allele generated by the C. įunding: This work was supported by the Israel Science Foundation (grant 2751/20 to TS and 257/17 to BP). Computer code available at: Microscopy images, videos and figure data available at. All relevant data are within the paper and its Supporting Information files. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The authors confirm that all data underlying the findings are fully available without restriction. Received: DecemAccepted: JPublished: July 19, 2021Ĭopyright: © 2021 Yuval et al. Our findings suggest molecular mechanisms for dendritic shape regulation and may help direct new avenues of research.Ĭitation: Yuval O, Iosilevskii Y, Meledin A, Podbilewicz B, Shemesh T (2021) Neuron tracing and quantitative analyses of dendritic architecture reveal symmetrical three-way-junctions and phenotypes of git-1 in C. We further quantified subtle morphological effects due to mutation in the git-1 gene, a known regulator of dendritic spines. We have found that the junctions connecting branched dendrites have a three-way-symmetry, although the dendrites are arranged in a crosshatch pattern, and that the distribution of junctions varies across distinct sub-classes of the PVD’s dendritic tree. By analyzing this dataset, we discovered several novel structural features. A key feature of our approach is to represent dendritic trees by a set of fundamental shapes, such as junctions and linear elements. Here, we present an algorithmic approach for detection and classification of the tree-like dendrites of the PVD neuron in C. A major aspect of the study of neurons, dating back over a century, involves the characterization of neuronal shapes and of their dendritic processes. ![]() Nerve cells (neurons) collect input signals via branched cellular projections called dendrites. Our findings shed new light on PVD architecture, demonstrating the effectiveness of our objective analyses of dendritic morphology and suggest molecular control mechanisms. We quantify the effect of mutation in git-1, a regulator of dendritic spine formation, on PVD morphology and discover a localized reduction in junctions. Surprisingly, these junctions are three-way-symmetrical on average, while dendritic processes are arranged orthogonally. We obtain excellent automatic tracing of PVD trees and uncover that dendritic junctions are unevenly distributed. The extracted neuronal architecture is represented by a database of structural elements for abstracted analysis. Here, we present a method for neuronal feature extraction, based on deep-learning and fitting algorithms. ![]() elegans serves as a model to study dendritic patterning however, quantitative, objective and automated analyses of PVD morphology are missing. Alterations to dendritic morphology are associated with developmental, behavioral and neurodegenerative changes. Complex dendritic trees are a distinctive feature of neurons. ![]()
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