Chao, Chih-Yuan

Charoenkwan, Phasit

Chiu, Tzai-Wen

Ho, Shinn-Ying

Huang, Hui-Ling

Hwang, Eric

English

PubMed

Shinn-Ying Ho

Chih-Yuan Chao

Hui-Ling Huang

Tzai-Wen Chiu

Phasit Charoenkwan

Eric Hwang

Springer

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

Springer - Publisher Connector

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

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