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Raw data for: Dynamic instability of dendrite tips generates the highly branched morphologies of sensory neurons

Cite this dataset

Shree, Sonal; Sutradhar, Sabyasachi; Howard, Jonathon (2022). Raw data for: Dynamic instability of dendrite tips generates the highly branched morphologies of sensory neurons [Dataset]. Dryad.


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.


Calculation of branch angle: The angle of new branches was measured using the angle tool of ImageJ (zero angle defined as in the direction of the mother). The angle distribution graph was plotted using Prism.

Calculation of branch rate: To determine branching events, time-lapse movies of duration 20-30 minutes were analyzed manually using ImageJ. A new protrusion of length >0.25 ?m was scored as a new branch. The total branching rate (min-1) was calculated by dividing the total number of branching events by the total time. The specific branching rate (?m-1min-1) was calculated as the total branching rate divided by the total branch length. The spatial distribution of all branching events was plotted using MATLAB with the soma at the origin (x=0,\ y=0).

Analysis of the elongation of internal branches: To study the possible role that the elongation of internal branches in arbor growth, we imaged the same dorsal neurons (A3, A4, and A5) every 24 hrs. Larvae were mounted and imaged as described but without the use of anesthetics. Their movement was minimized by imaging at 4 °C for 2-5 mins. They were then returned to the apple-agar plate in the Darwin Chamber. The larvae were imaged using 20X and 40X objectives. For image analysis, the same neurons at 24 & 48 hr, and 48 & 96hr were segmented and aligned using ImageJ to identify conserved non-terminal internal branches in the proximal region. The fractional increases in branches and segment lengths were defined as"

Fractional\ length\ change=\frac{Final\ length-Initial\ length}{Initial\ length}



National Institute of Neurological Disorders and Stroke, Award: R01 NS118884

National Institute of Mental Health, Award: DP1 MH110065