Evidence map›Paper›PMID 21651810›Full record

ArticleBMC bioinformatics2011

NeurphologyJ: an automatic neuronal morphology quantification method and its application in pharmacological discovery.

Shinn-Ying Ho, Chih-Yuan Chao, Hui-Ling Huang, Tzai-Wen Chiu, Phasit Charoenkwan, Eric Hwang

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Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers.

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87citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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87 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Shinn-Ying HoInstitute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan.
Chih-Yuan Chao
Hui-Ling Huang
Tzai-Wen Chiu
Phasit Charoenkwan
Eric Hwang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAutomatic quantification of neuronal morphology from images of fluorescence microscopy plays an increasingly important role in high-content screenings. However, there exist very few freeware tools and methods which provide automatic neuronal morphology quantification for pharmacological discovery.

resultsThis study proposes an effective quantification method, called NeurphologyJ, capable of automatically quantifying neuronal morphologies such as soma number and size, neurite length, and neurite branching complexity (which is highly related to the numbers of attachment points and ending points). NeurphologyJ is implemented as a plugin to ImageJ, an open-source Java-based image processing and analysis platform. The high performance of NeurphologyJ arises mainly from an elegant image enhancement method. Consequently, some morphology operations of image processing can be efficiently applied. We evaluated NeurphologyJ by comparing it with both the computer-aided manual tracing method NeuronJ and an existing ImageJ-based plugin method NeuriteTracer. Our results reveal that NeurphologyJ is comparable to NeuronJ, that the coefficient correlation between the estimated neurite lengths is as high as 0.992. NeurphologyJ can accurately measure neurite length, soma number, neurite attachment points, and neurite ending points from a single image. Furthermore, the quantification result of nocodazole perturbation is consistent with its known inhibitory effect on neurite outgrowth. We were also able to calculate the IC50 of nocodazole using NeurphologyJ. This reveals that NeurphologyJ is effective enough to be utilized in applications of pharmacological discoveries.

conclusionsThis study proposes an automatic and fast neuronal quantification method NeurphologyJ. The ImageJ plugin with supports of batch processing is easily customized for dealing with high-content screening applications. The source codes of NeurphologyJ (interactive and high-throughput versions) and the images used for testing are freely available (see Availability).

Indexed as

SoftwareAnimalsImage Processing, Computer-AssistedMiceMicroscopy, FluorescenceNeuritesNeuronsNocodazoleTubulin ModulatorsNocodazoleTubulin Modulators

Identifiers

PMID21651810
PMCPMC3121649

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.