![]() 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).ĭue to the limited budget of individual laboratories and various cell models and experimental designs amongst them, the open source codes of freeware tools are immensely useful for researchers. While commercially available software capable of automatic quantification of neurite outgrowth have been used in recent high-content screening studies, such tools are only available to large research facilities and are usually not openly available for user customization. Although some of the small-scale screenings were conducted by manual quantification of neuronal morphology, these manual methods are extremely time-consuming and becoming impractical for large datasets. These features include soma number, soma size, neurite length, and neurite branching complexity. 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. The main restricting factor is the lack of adequate tools for rapidly analyzing and quantifying the massive amount of neuronal images.Ī neuron typically consists of two morphological structures, the round neuronal cell body (called soma) and the elongated neuronal protrusions (called neurites). However, high-content screenings on biological or pharmacological molecules that can induce neuronal differentiation, promote neuronal regeneration, or delay neurodegeneration are very limited. ![]() Recent advancements in automated fluorescence microscopy have made high-content screening an essential technique for discovering novel molecular pathways in diseases or potential new therapeutic treatments. The source codes of NeurphologyJ (interactive and high-throughput versions) and the images used for testing are freely available (see Availability). The ImageJ plugin with supports of batch processing is easily customized for dealing with high-content screening applications. This study proposes an automatic and fast neuronal quantification method NeurphologyJ. This reveals that NeurphologyJ is effective enough to be utilized in applications of pharmacological discoveries. We were also able to calculate the IC50 of nocodazole using NeurphologyJ. Furthermore, the quantification result of nocodazole perturbation is consistent with its known inhibitory effect on neurite outgrowth. NeurphologyJ can accurately measure neurite length, soma number, neurite attachment points, and neurite ending points from a single image. Our results reveal that NeurphologyJ is comparable to NeuronJ, that the coefficient correlation between the estimated neurite lengths is as high as 0.992. We evaluated NeurphologyJ by comparing it with both the computer-aided manual tracing method NeuronJ and an existing ImageJ-based plugin method NeuriteTracer. Consequently, some morphology operations of image processing can be efficiently applied. The high performance of NeurphologyJ arises mainly from an elegant image enhancement method. ![]() NeurphologyJ is implemented as a plugin to ImageJ, an open-source Java-based image processing and analysis platform. 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). However, there exist very few freeware tools and methods which provide automatic neuronal morphology quantification for pharmacological discovery. Automatic quantification of neuronal morphology from images of fluorescence microscopy plays an increasingly important role in high-content screenings. ![]()
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