Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Research article

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

Shinn-Ying Ho12, Chih-Yuan Chao3, Hui-Ling Huang12, Tzai-Wen Chiu23, Phasit Charoenkwan1 and Eric Hwang123*

Author Affiliations

1 Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan

2 Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan

3 Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, Taiwan

For all author emails, please log on.

BMC Bioinformatics 2011, 12:230  doi:10.1186/1471-2105-12-230

Published: 8 June 2011

Abstract

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).