ArticleBMC bioinformatics2011
NeurphologyJ: an automatic neuronal morphology quantification method and its application in pharmacological discovery.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers.
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87 citing papers in PubMed.
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- Automated detection of c-Fos-expressing neurons using inhomogeneous background subtraction in fluorescent images.Neurobiology of learning and memory · 2025Article
- Epigenetic modulation rescues neurodevelopmental deficits in Syngap1Aging cell · 2025Article
- Evaluating deep learning techniques for optimal neurons counting and characterization in complex neuronal cultures.Medical & biological engineering & computing · 2025Article
- Systematic Evaluation of Extracellular Coating Matrix on the Differentiation of Human-Induced Pluripotent Stem Cells to Cortical Neurons.International journal of molecular sciences · 2024Article
- Coupling of Spectrin Repeat Modules for the Assembly of Nanorods and Presentation of Protein Domains.ACS nano · 2024Article
- From Young to Old: Mimicking Neuronal Aging in Directly Converted Neurons from Young Donors.Cells · 2024Article
- Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity.Brain informatics · 2024Review
27 more citing papers are in PubMed but not listed here.
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6 authors.
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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).
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