Achieving functional neuronal dendrite structure through sequential stochastic growth and retraction

Abstract

<jats:p>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 targeted growth and stochastic 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.</jats:p>

The branching code: a model of actin-driven dendrite arborisation

“…The two-step model used in this work suggests that while main dendritic trees have common growth rules that are balancing between efficiency and precision, the dendritic specialisations of any neuronal cell type need to be studied carefully, since the details do not necessarily have the same constraints. This view is compatible with findings in a companion paper where functional constraints shape the dendrites of c1da neurons in a specialised branch retraction phase additionally to the general growth phase that guarantees optimal wiring (Castro et al, 2020).…”

“…Moreover, the iterations of growth described by the model translate directly to the rough description of dendrites in other cell types including three dimensional dendrites in mammalian cortex such as dentate gyrus granule cells and cortical pyramidal cells in various layers . With a more detailed modelling approach this general growth model derived from c4da neurons has also been applied to understand the dendritic computations performed by c1da neurons in the fly larva (Castro et al, 2020). We thus took advantage of the possibility of linking dynamics of dendrite growth with a more formal and mathematical understanding of dendrite morphology afforded by the c4da model to dissect the dynamic growth process of c3da neuron dendrites.…”

“…As a second step towards a quantitative description of c3da neuron dendrites and of the morphological effect of mutating individual AMPs, we identified a specific set of distinctive morphometric features for these neurons. We collected 28 general dendritic branching features (see STAR Methods, Table 3) (Castro et al, 2020) and we used them to quantitatively describe c3da dendrites.…”

“…We additionally generated novel tools for extracting quantitative parameters of the dynamic behaviour of dendrite branches from time-lapse movies based on a novel branch registration software (Baltruschat et al, 2020). This time-lapse tool operates similarly as in (Sheng et al, 2018) and was developed in parallel to (Castro et al, 2020), with the advantage of having an automated quantification after registration which detects branch types and their dynamics. Moreover, the tool operates in the same framework as the tracing and morphological analysis.…”

“…Models based on local or on global rules have been applied to reproduce the overall organisation of dendritic trees, including da neurons (Nanda et al, 2018b;Baltruschat et al, 2020;Castro et al, 2020). We based our c3da model on the fundamental organising principle that dendrites are built through minimising cable length and signal conduction times (Cuntz et al, 2007;Wen and Chklovskii, 2008;Cuntz et al, 2010;Baltruschat et al, 2020).…”