Cargando…

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

BACKGROUND: Automatic 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...

Descripción completa

Detalles Bibliográficos
Autores principales: Ho, Shinn-Ying, Chao, Chih-Yuan, Huang, Hui-Ling, Chiu, Tzai-Wen, Charoenkwan, Phasit, Hwang, Eric
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3121649/
https://www.ncbi.nlm.nih.gov/pubmed/21651810
http://dx.doi.org/10.1186/1471-2105-12-230
_version_ 1782206843694612480
author Ho, Shinn-Ying
Chao, Chih-Yuan
Huang, Hui-Ling
Chiu, Tzai-Wen
Charoenkwan, Phasit
Hwang, Eric
author_facet Ho, Shinn-Ying
Chao, Chih-Yuan
Huang, Hui-Ling
Chiu, Tzai-Wen
Charoenkwan, Phasit
Hwang, Eric
author_sort Ho, Shinn-Ying
collection PubMed
description BACKGROUND: Automatic 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. RESULTS: This 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. CONCLUSIONS: This 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).
format Online
Article
Text
id pubmed-3121649
institution National Center for Biotechnology Information
language English
publishDate 2011
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-31216492011-06-24 NeurphologyJ: An automatic neuronal morphology quantification method and its application in pharmacological discovery Ho, Shinn-Ying Chao, Chih-Yuan Huang, Hui-Ling Chiu, Tzai-Wen Charoenkwan, Phasit Hwang, Eric BMC Bioinformatics Research Article BACKGROUND: Automatic 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. RESULTS: This 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. CONCLUSIONS: This 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). BioMed Central 2011-06-08 /pmc/articles/PMC3121649/ /pubmed/21651810 http://dx.doi.org/10.1186/1471-2105-12-230 Text en Copyright ©2011 Ho et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Ho, Shinn-Ying
Chao, Chih-Yuan
Huang, Hui-Ling
Chiu, Tzai-Wen
Charoenkwan, Phasit
Hwang, Eric
NeurphologyJ: An automatic neuronal morphology quantification method and its application in pharmacological discovery
title NeurphologyJ: An automatic neuronal morphology quantification method and its application in pharmacological discovery
title_full NeurphologyJ: An automatic neuronal morphology quantification method and its application in pharmacological discovery
title_fullStr NeurphologyJ: An automatic neuronal morphology quantification method and its application in pharmacological discovery
title_full_unstemmed NeurphologyJ: An automatic neuronal morphology quantification method and its application in pharmacological discovery
title_short NeurphologyJ: An automatic neuronal morphology quantification method and its application in pharmacological discovery
title_sort neurphologyj: an automatic neuronal morphology quantification method and its application in pharmacological discovery
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3121649/
https://www.ncbi.nlm.nih.gov/pubmed/21651810
http://dx.doi.org/10.1186/1471-2105-12-230
work_keys_str_mv AT hoshinnying neurphologyjanautomaticneuronalmorphologyquantificationmethodanditsapplicationinpharmacologicaldiscovery
AT chaochihyuan neurphologyjanautomaticneuronalmorphologyquantificationmethodanditsapplicationinpharmacologicaldiscovery
AT huanghuiling neurphologyjanautomaticneuronalmorphologyquantificationmethodanditsapplicationinpharmacologicaldiscovery
AT chiutzaiwen neurphologyjanautomaticneuronalmorphologyquantificationmethodanditsapplicationinpharmacologicaldiscovery
AT charoenkwanphasit neurphologyjanautomaticneuronalmorphologyquantificationmethodanditsapplicationinpharmacologicaldiscovery
AT hwangeric neurphologyjanautomaticneuronalmorphologyquantificationmethodanditsapplicationinpharmacologicaldiscovery