Dynamic instability of dendrite tips generates the highly branched morphologies of sensory neurons

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. However, 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 found that the tips of dendrites undergo dynamic instability, transitioning rapidly and stochastically between growing, shrinking, and paused states. By incorporating these measured dynamics into an 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.

γ-TuRCs and the augmin complex are required for the development of highly branched dendritic arbors in Drosophila

“…To examine the effect of depleting γ-TuRCs on larval class IV neurons, we expressed grip91 GCP3 -RNAi with UAS-Dicer2 and examined class IV ddaC arbors at 72 h and 96 h after egg laying (AEL). The growth of class IV da neurons has been well documented ( Gao et al, 1999 ; Parrish et al, 2009 ; Shree et al, 2022 ). Dendrite growth begins in late embryos at around 16 h AEL.…”

“…We next examined the dynamics of terminal dendrites in control-RNAi and Dgt4-RNAi neurons. It is known that terminal dendrites are highly dynamic ( Shree et al, 2022 ), forming in an actin-dependent manner ( Nithianandam and Chien, 2018 ; Stürner et al, 2019 ) and then either disappearing, remaining stable or extending their growth. Moreover, their stability correlates with the presence of anterograde EB1–GFP comets ( Ori-McKenney et al, 2012 ; Sears and Broihier, 2016 ).…”

Neuromorphic Optical Data Storage Enabled by Nanophotonics: A Perspective

“…The encoding, storage, and retrieval of memories in the brain not only involves memory generation but also inhibition and latency, that is the formation of inhibitory engrams, silent engrams, and forgetting. − Understating the correlation between cell engrams and dynamic memories and reproducing these functions using nanophotonics techniques and materials pose great challenges. New neuroscience approaches, such as new techniques, tools, models, and algorithms − that allow for the study of neurons in interaction, , are critical for understanding the working mechanism of neuronal activity and functional connection leading to the brain’s memory. While typical nanophotonics-enabled ODS aims for higher storage capacity and throughput, the brain is capable of performing memory along with numerous other complex tasks.…”

Structure, motion, and multiscale search of traveling networks

“…Processes of interest can often be understood using only the connectivity information between nodes [13]; however, additional constraints arise in spatial networks where each node is also embedded inside a real or abstract space [14]. Networks can be static or dynamic [15], and movement in the context of networks is usually discussed in terms of processes occurring on the network, for example, an agent walking over a network [16, 17] or information or disease spreading through a network [18, 19], or in terms of a network developing from a rooted position like neuronal dendrites [12] or branched organs [20]. Despite significant work on temporal networks [15], surprisingly, there appears to be little systematic work on how networks themselves travel through space away from an initial location without a permanent anchor point.…”