Axonal & Dendritic Identity & Structure

Introduction of Neuronal polarity

Polarity is a simple biological definition to describe an asymmetry and play key roles in most basic biological functions. In addition, polarity is also important for asymmetric cell division and further cellular diversity. Neurons are classical examples for a polarized cell, neuronal polarity refers to the different organization of the axonal and dendritic areas. There are mostly one axon and several dendrites for electrical signals’ reception and propagation.

As the branching structures of neurons, the axons and dendrites form the basis of network connections, synaptic communication, and signal integration. In the nervous system, the diversity and complexity of neuron morphology have a profound impact on information processing. Some studies have shown that improper establishment or migration of axonal-dendritic connectivity may result in brain structural defects and severe developmental syndromes. In this case, it is essential to understand the axonal and dendritic identity and structure.

Researches for Axonal and Dendritic

Based on the rapid development of techniques including molecular, microscopic imaging, neuroinformatics tools, and computational simulations, the relationship between neuron structure, activity, and function is fully studied. Spatially and morphologically, dendrites and axons are discrete neurite compartments composed of different protein and lipid components. Neurons can extend multiple short neurites in random directions. When one neurite prolonged extensions an axon, the symmetry is broken and the remaining neurites transform into dendrites. Researches have shown that axonal or dendritic identity is largely dependent on their distinct cytoskeletal configurations.

Changes in neuronal morphology are accompanied by functional changes during development, experience, aging, and disease. Compared with the traditional research methods, the digital reconstructions of neuronal morphologies allow complex quantitative analyses that are unattainable from 2D tracings or raw images or 2D tracings. By combining computational modeling with traditional "wet laboratory" research, parameters that affect neuron function can be quickly identified and contribute to the development of new hypotheses and the design of new experiments. What’s more, digitized morphologies enable computational modeling of biophysically realistic neuronal dynamics.

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