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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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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 |
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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 |
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