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Dataset and software to analyze and simulate neuronal morphogenesis in Drosophila class IV da neurons

Citation

Sutradhar, Sabyasachi; Shree, Sonal; Howard, Jonathon (2022), Dataset and software to analyze and simulate neuronal morphogenesis in Drosophila class IV da neurons, Dryad, Dataset, https://doi.org/10.5061/dryad.fbg79cnx9

Abstract

The highly ramified arbors of neuronal dendrites provide the substrate for the high connectivity and computational power of the brain. Altered dendritic morphology is associated with neuronal diseases. Many molecules have been shown to play crucial roles in shaping and maintaining dendrite morphology. Yet, the underlying principles by which molecular interactions generate branched morphologies are not understood. To elucidate these principles, we visualized the growth of dendrites throughout larval development of Drosophila sensory neurons and discovered that the tips of dendrites undergo dynamic instability, transitioning rapidly and stochastically between growing, shrinking, and paused states. By incorporating these measured dynamics into a novel, agent-based computational model, we showed that the complex and highly variable dendritic morphologies of these cells are a consequence of the stochastic dynamics of their dendrite tips. These principles may generalize to branching of other neuronal cell-types, as well as to branching at the subcellular and tissue levels.

Methods

Three packages have been developed to analyse and simulate the morphogenesis of Drosophila class IV da neurons. Here are the details:

1. TipTrack: A MATLAB based tracking software that can precisely measure the length of slender filamentous structures (e.g. microtubules, dendrites etc.). This software has been used to measure dendritic length as a function of time with subpixel accuracy. The tracked dendrites traces are then used to calculate the growth/shrink velocity and transition rates between different states.

2. NeuroMorpho: This package can measure the coarse properties (e.g. branch length, branch number etc.) and fine scale properties ( fractal dimension, mesh size etc.) of class Iv and other types of neurons.

3. NeuronArbor Simulator: This package implements the agent based model described in Shree et. al.,2022, in C.

Funding

National Institutes of Health