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Journal articles

Neurite analyzer: An original Fiji plugin for quantification of neuritogenesis in two-dimensional images

Abstract : Background In life sciences, there is a growing need for new informatics tools designed to provide automated solutions in order to analyze big amounts of images obtained from high-throughput imaging systems. Among the most widely used assays in neurotoxicity, endocrinology and brain diseases, the neurite outgrowth assay is popular. New method Cell-to-cell quantification of the main morphological features of neurite outgrowth assays remains very challenging. Here, we provide a new pipeline developed on Fiji software for analysis of series of two-dimensional images. It allows the automated analysis of most of these features. Results We tested the accuracy and usefulness of the software by confirming the effects of estradiol and hypoxia on in vitro neuronal differentiation, previously published by different authors with manual analysis methods. With this new method, we highlighted original interesting data. Comparison with existing method(s) The innovation brought by this plugin lies in the fact that it can process multiple images at the same time, in order to obtain: the number of nuclei, the number of neurites, the length of neurites, the number of neurites junctions, the number of neurites branches, the length of each branch, the position of the branch in the image, the angle of each branch, but also the area of each cell and the number of neurites per cell. Conclusions This plugin is easy to use, highly sensitive, and allows the experimenter to acquire ready-to-use data coming from a vast amount of images. © 2016 Elsevier B.V.
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Contributor : Xavier Chard-Hutchinson Connect in order to contact the contributor
Submitted on : Thursday, September 15, 2016 - 4:46:08 PM
Last modification on : Wednesday, March 30, 2022 - 4:25:43 PM



Alexis J. Haas, Sylvain Prigent, Stéphanie Dutertre, Yves Le Dréan, Yann Le Page. Neurite analyzer: An original Fiji plugin for quantification of neuritogenesis in two-dimensional images. Journal of Neuroscience Methods, Elsevier, 2016, 271, pp.86--91. ⟨10.1016/j.jneumeth.2016.07.011⟩. ⟨hal-01367152⟩



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