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).
Highlights
Automatic quantification of neuronal morphology from images of fluorescence microscopy plays an increasingly important role in high-content screenings
While commercially available software capable of automatic quantification of neurite outgrowth have been used in recent high-content screening studies [6,7,8], such tools are only available to large research facilities and are usually not openly available for user customization
We have proposed an automatic neuronal morphology quantification method and its ImageJ plugin named NeurphologyJ with supports for single image or batch processing
Summary
Automatic quantification of neuronal morphology from images of fluorescence microscopy plays an increasingly important role in high-content screenings. To determine the efficacy of a particular pharmacological perturbation on neuronal regeneration using high-content screening techniques, automatic quantification of several morphological features is necessary. These features include soma number, soma size, neurite length, and neurite branching complexity. While commercially available software capable of automatic quantification of neurite outgrowth have been used in recent high-content screening studies [6,7,8], such tools are only available to large research facilities and are usually not openly available for user customization. These commercial software packages available for 2D or 3D neurite quantification include Amira (Visage Imaging), HCA-Vision (CSIRO Biotech Imaging), Imaris (Bitplane), and Neurolucida (MBF Bioscience)
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