Cooperative stochastic binding and unbinding explain synaptic size dynamics and statistics

Abstract

Synapses are dynamic molecular assemblies whose sizes fluctuate significantly over time-scales of hours and days. In the current study, we examined the possibility that the spontaneous microscopic dynamics exhibited by synaptic molecules can explain the macroscopic size fluctuations of individual synapses and the statistical properties of synaptic populations. We present a mesoscopic model, which ties the two levels. Its basic premise is that synaptic size fluctuations reflect cooperative assimilation and removal of molecules at a patch of postsynaptic membrane. The introduction of cooperativity to both assimilation and removal in a stochastic biophysical model of these processes, gives rise to features qualitatively similar to those measured experimentally: nanoclusters of synaptic scaffolds, fluctuations in synaptic sizes, skewed, stable size distributions and their scaling in response to perturbations. Our model thus points to the potentially fundamental role of cooperativity in dictating synaptic remodeling dynamics and offers a conceptual understanding of these dynamics in terms of central microscopic features and processes.

Lifetime of a structure evolving by cluster aggregation and particle loss, and application to postsynaptic scaffold domains

“…This is also seen in other proposed models of synaptic fluctuations e.g. [30]. This emphasizes the much greater difficulty of preserving in a persistent way the size of a domain or the "strength" of synapses than maintaining their mere existence.…”

“…The model of ref. [3] can be experimentally distinguished from other ones [28][29][30] on different grounds. First, at the most basic level, it supposes that the existence of a scaffold domain depends on a lateral flux of scaffolding proteins onto the domain.…”

Stable but not rigid: Long-termin vivoSTED nanoscopy uncovers extensive remodeling of stable spines and indicates multiple drivers of structural plasticity

“…Studies of this spontaneous synaptic volatility have so far exclusively used spine head total fluorescence as a proxy of head volume, which itself is a proxy of synaptic strength [23][24][25][26] . The near log-normal distributions and multiplicative dynamics observed experimentally are well explained by mathematical models for the random cooperative assembly and turnover of postsynaptic macromolecular complexes 27,28 . Because the actin cytoskeleton that maintains and modifies spine morphology is composed of several pools of f-actin, all of which undergo continuous assembly and disassembly, not only the spine head but the entire spine morphology should be expected to exhibit spontaneous random intrinsic fluctuations.…”

Identifying clusters on a discrete periodic lattice via machine learning

“…This paper will apply hierarchical clustering to discrete lattice systems in biophysics. These lattices arise when we model the spatiotemporal organization of cell membrane proteins, the study of which would shed light on synaptic transmission [5,6], viral infections [7], and inter-cellular communication [8] among other areas. In these lattice models, the membrane is discretized into patches according to specific biophysical considerations [9,10], with each patch described by, for instance, a k−dimensional vector recording the amounts of the k chemical species present.…”

“…To our knowledge, this technique has first been utilized in membrane biophysics by Shomar et al [6], who in turn were inspired by a nanoscopy experiment [12]. The MATLAB clustering code in [6] was published as part of its Supplementary Materials. Unfortunately, that code does not model diffusion, and therefore does not correspond to any particular boundary conditions.…”