Open AccessAutoNeuriteJ: An ImageJ plugin for measurement and classification of neuritic extensions
Author(s) -
Benoît Boulan,
Anne Béghin,
Charlotte Ravanello,
Jean-Christophe Deloulme,
Sylvie Gory-Fauré,
Annie Andrieux,
Jacques Brocard,
Éric Denarier
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0234529
Subject(s) - neurite , neuron , neuroscience , phenotype , growth cone , microtubule , plug in , biology , computational biology , computer science , microbiology and biotechnology , axon , in vitro , genetics , gene , programming language
Morphometry characterization is an important procedure in describing neuronal cultures and identifying phenotypic differences. This task usually requires labor-intensive measurements and the classification of numerous neurites from large numbers of neurons in culture. To automate these measurements, we wrote AutoNeuriteJ, an imageJ/Fiji plugin that measures and classifies neurites from a very large number of neurons. We showed that AutoNeuriteJ is able to detect variations of neuritic growth induced by several compounds known to affect the neuronal growth. In these experiments measurement of more than 5000 mouse neurons per conditions was obtained within a few hours. Moreover, by analyzing mouse neurons deficient for the microtubule associated protein 6 (MAP6) and wild type neurons we illustrate that AutoNeuriteJ is capable to detect subtle phenotypic difference in axonal length. Overall the use of AutoNeuriteJ will provide rapid, unbiased and accurate measurement of neuron morphologies.