Achieving functional neuronal dendrite structure through sequential stochastic growth and retraction
Abstract
Class I ventral posterior dendritic arborisation (c1vpda) proprioceptive sensory neurons respond to contractions in the Drosophila larval body wall during crawling. Their dendritic branches run along the direction of contraction, possibly a functional requirement to maximise membrane curvature during crawling contractions. Although the molecular machinery of dendritic patterning in c1vpda has been extensively studied, the process leading to the precise elaboration of their comb-like shapes remains elusive. Here, to link dendrite shape with its proprioceptive role, we performed long-term, non-invasive, in vivo time-lapse imaging of c1vpda embryonic and larval morphogenesis to reveal a sequence of differentiation stages. We combined computer models and dendritic branch dynamics tracking to propose that distinct sequential phases of stochastic growth and retraction achieve efficient dendritic trees both in terms of wire and function. Our study shows how dendrite growth balances structure–function requirements, shedding new light on general principles of self-organisation in functionally specialised dendrites.
Data availability
All data and all code is available on Zenodo https://doi.org/10.5281/zenodo.4290200
Article and author information
Author details
Funding
Bundesministerium für Bildung und Forschung (01GQ1406)
- Hermann Cuntz
Deutsche Forschungsgemeinschaft (SPP 1464)
- Gaia Tavosanis
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Timothy O'Leary, University of Cambridge, United Kingdom
Version history
- Received: July 9, 2020
- Accepted: November 15, 2020
- Accepted Manuscript published: November 26, 2020 (version 1)
- Accepted Manuscript updated: December 4, 2020 (version 2)
- Version of Record published: January 26, 2021 (version 3)
Copyright
© 2020, Ferreira Castro et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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