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

Stochastic growth and selective stabilization generate stereotyped dendritic arbors

Stereotyped dendritic arbors are shaped by dynamic and stochastic growth during neuronal development. It remains unclear how guidance receptors and ligands coordinate branch dynamic growth, retraction, and stabilization to specify dendritic arbors. We previously showed that extracellular ligand SAX-7/LICAM dictates the shape of the PVD sensory neuron via binding to the dendritic guidance receptor DMA-1, a single transmembrane adhesion molecule. Here, we perform structure-function analyses of DMA-1 and unexpectedly find that robust, stochastic dendritic growth does not require ligand-binding. Instead, ligand-binding inhibits growth, prevents retraction, and specifies arbor shape. Furthermore, we demonstrate that dendritic growth requires a pool of ligand-free DMA-1, which is maintained by receptor endocytosis and reinsertion to the plasma membrane via recycling endosomes. Mutants defective of DMA-1 endocytosis show severely truncated dendritic arbors. We present a model in which ligand-free guidance receptor mediates intrinsic, stochastic dendritic growth, while extracellular ligands instruct dendrite shape by inhibiting growth.

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

Microtubules are nucleated by γ-tubulin ring complexes (γ-TuRCs) and are essential for neuronal development. Nevertheless, γ-TuRC depletion has been reported to perturb only higher-order branching in elaborated Drosophila larval class IV dendritic arborization (da) neurons. This relatively mild phenotype has been attributed to defects in microtubule nucleation from Golgi outposts, yet most Golgi outposts lack associated γ-TuRCs. By analyzing dendritic arbor regrowth in pupae, we show that γ-TuRCs are also required for the growth and branching of primary and secondary dendrites, as well as for higher-order branching. Moreover, we identify the augmin complex (hereafter augmin), which recruits γ-TuRCs to the sides of pre-existing microtubules, as being required predominantly for higher-order branching. Augmin strongly promotes the anterograde growth of microtubules in terminal dendrites and thus terminal dendrite stability. Consistent with a specific role in higher-order branching, we find that augmin is expressed less strongly and is largely dispensable in larval class I da neurons, which exhibit few higher-order dendrites. Thus, γ-TuRCs are essential for various aspects of complex dendritic arbor development, and they appear to function in higher-order branching via the augmin pathway, which promotes the elaboration of dendritic arbors to help define neuronal morphology.

Topology recapitulates morphogenesis of neuronal dendrites

Branching allows neurons to make synaptic contacts with large numbers of other neurons, facilitating the high connectivity of nervous systems. Neuronal arbors have geometric properties such as branch lengths and diameters that are optimal in that they maximize signaling speeds while minimizing construction costs. In this work, we asked whether neuronal arbors have topological properties that may also optimize their growth or function. We discovered that for a wide range of invertebrate and vertebrate neurons the distributions of their subtree sizes follow power laws, implying that they are scale invariant. The power-law exponent distinguishes different neuronal cell types. Postsynaptic spines and branchlets perturb scale invariance. Through simulations, we show that the subtree-size distribution depends on the symmetry of the branching rules governing arbor growth and that optimal morphologies are scale invariant. Thus, the subtree-size distribution is a topological property that recapitulates the functional morphology of dendrites.

Sculpting the dendritic landscape: Actin, microtubules, and the art of arborization

Dendrites are intricately designed neuronal compartments that play a vital role in the gathering and processing of sensory or synaptic inputs. Their diverse and elaborate structures are distinct features of neuronal organization and function. Central to the generation of these dendritic arbors is the neuronal cytoskeleton. In this review, we delve into the current progress toward our understanding of how dendrite arbors are generated and maintained, focusing on the role of the actin and microtubule cytoskeleton.

Efficient simulation of neural development using shared memory parallelization

The Neural Development Simulator, NeuroDevSim, is a Python module that simulates the most important aspects of brain development: morphological growth, migration, and pruning. It uses an agent-based modeling approach inherited from the NeuroMaC software. Each cycle has agents called fronts execute model-specific code. In the case of a growing dendritic or axonal front, this will be a choice between extension, branching, or growth termination. Somatic fronts can migrate to new positions and any front can be retracted to prune parts of neurons. Collision detection prevents new or migrating fronts from overlapping with existing ones. NeuroDevSim is a multi-core program that uses an innovative shared memory approach to achieve parallel processing without messaging. We demonstrate linear strong parallel scaling up to 96 cores for large models and have run these successfully on 128 cores. Most of the shared memory parallelism is achieved without memory locking. Instead, cores have only write privileges to private sections of arrays, while being able to read the entire shared array. Memory conflicts are avoided by a coding rule that allows only active fronts to use methods that need writing access. The exception is collision detection, which is needed to avoid the growth of physically overlapping structures. For collision detection, a memory-locking mechanism was necessary to control access to grid points that register the location of nearby fronts. A custom approach using a serialized lock broker was able to manage both read and write locking. NeuroDevSim allows easy modeling of most aspects of neural development for models simulating a few complex or thousands of simple neurons or a mixture of both.

Code available at

https://github.com/CNS-OIST/NeuroDevSim.

Dynamic microtubules slow down during their shrinkage phase

Microtubules are dynamic polymers that undergo stochastic transitions between growing and shrinking phases. The structural and chemical properties of these phases remain poorly understood. The transition from growth to shrinkage, termed catastrophe, is not a first-order reaction but rather a multistep process whose frequency increases with the growth time: the microtubule ages as the older microtubule tip becomes more unstable. Aging shows that the growing phase is not a single state but comprises several substates of increasing instability. To investigate whether the shrinking phase is also multistate, we characterized the kinetics of microtubule shrinkage following catastrophe using an in vitro reconstitution assay with purified tubulins. We found that the shrinkage speed is highly variable across microtubules and that the shrinkage speed of individual microtubules slows down over time by as much as several fold. The shrinkage slowdown was observed in both fluorescently labeled and unlabeled microtubules as well as in microtubules polymerized from tubulin purified from different species, suggesting that the shrinkage slowdown is a general property of microtubules. These results indicate that microtubule shrinkage, like catastrophe, is time dependent and that the shrinking microtubule tip passes through a succession of states of increasing stability. We hypothesize that the shrinkage slowdown is due to destabilizing events that took place during growth, which led to multistep catastrophe. This suggests that the aging associated with growth is also manifested during shrinkage, with the older, more unstable growing tip being associated with a faster depolymerizing shrinking tip.