Computing neurite outgrowth and arborization in superior cervical ganglion neurons

Dendrites are the primary site of synaptic activity in neurons and changes in synapses are often the first pathological stage in neurodegenerative diseases. Molecular studies of these changes rely on morphological analysis of the imaging of somas and dendritic arbors of cultured or primary neurons. As research on preventing or reversing synaptic degeneration develops, demands increase for user-friendly 2D neurite analyzers without undermining accuracy and reproducibility. The most common method of 2D neurite analysis is manual by using ImageJ. This method relies completely on the user's ability to distinguish the shape and size of dendrites and trace morphology with a series of straight connected lines. Semi-automatic methods have also been developed, such as the NeuronJ plugin for ImageJ. These methods still rely on the user to identify the start and end of the dendrites, but automatically determine the shape, reducing the likelihood of user bias and speeding the process. Some automatic methods have been developed through image processing software, like ImagePro. These programs tend to be expensive, but have been shown to be fast and effective, limiting user interaction. In this study, we compare three methods of neurite analysis-ImageJ, NeuronJ, and ImagePro-in measuring the soma size, number of dendrites, and length of dendrites per cell of embryonic sympathetic rat neurons with BMP-7-induced dendritic growth. Our results indicate that ImageJ and NeuronJ measurements were of similar effectiveness and consistent throughout various images and multiple trials. NeuronJ required less user interaction in measuring the length of dendrites than the manual method and therefore, was faster and less labor intensive. Conversely, ImagePro tended to be inconsistent across images, overestimating both soma size and the number of dendrites per cell while underestimating the length of dendrites. Overall, NeuronJ, in conjunction with ImageJ, is the most reliable and efficient method of 2D neurite analysis tested in the present study.