A quantitative rule to explain multi-spine plasticity

Abstract

Neurons receive thousands of inputs onto their dendritic arbour, where individual synapses undergo activity-dependent changes in strength. The durable forms of synaptic strength change, long-term potentiation (LTP) and long-term depression (LTD), require calcium entry through N-methyl-D-aspartate receptors (NMDARs) that triggers downstream protein signalling cascades in the dendrite. Intriguingly, sLTP and sLTD are not necessarily restricted to the active, targeted synapses (homosynapses), and the changes in synaptic strength can spread and affect the strengths of inactive or non-stimulated synapses (heterosynapses) on the same cell. Precisely how neurons allocate resources for implementing the changes in strength at individual synapses depending on their proximity to activity across space and time remains an open question. In order to gain insights into the elementary processes underlying heterosynaptic plasticity and their interplay with homosynaptic plasticity, we have combined experimental and mathematical modelling approaches. On the one hand, we used glutamate uncaging to precisely and systematically stimulate variable numbers of homosynapses sharing the same dendritic branch whilst monitoring tens of other heterosynapses on the same dendrite. Homosynaptic potentiation of clusters of dendritic spines leads to heterosynaptic changes that are NMDAR-dependent, requiring CaMKII and calcineurin activity. On the other hand, inspired by the calcium levels hypothesis where different amounts of calcium lead to either growth or shrinkage of spines, we have built a model based on a dual-role calcium-dependent protein that induces spine shrinkage or potentiation dynamics. Comparing our experimental results with model predictions, we find that (i) both collaboration and competition among spines for protein resources are key drivers of heterosynaptic plasticity and (ii) the temporal and spatial distance between simultaneously stimulated spines impact the resulting spine dynamics. Moreover, our model can reconcile a number of disparate and sometimes contradictory experimental reports of heterosynaptic sLTP or sLTD at homo and heterosynaptic spines. Our results provide a quantitative description of the heterosynaptic footprint unfolding across minutes and hours post-stimulation across tens of microns of dendritic space. This broadens our knowledge about the operation of non-linear dendritic summation rules and how they impact spiking decisions.

Homosynaptic plasticity induction causes heterosynaptic changes at the unstimulated neighbors in an induction pattern and location-specific manner

“…Additionally, when a group of spines was activated individually but simultaneously at a local dendritic segment, bi-directional postsynaptic heterosynaptic plasticity was observed at unstimulated neighbors (30-Reg combined with voltage clamp at 0 mV) and the polarity of neighboring plasticity was a function of the distance between the center of the stimulated spines and the unstimulated neighbors (Tong et al, 2021 ). This led researchers, through computational modeling, to hypothesize that different activity patterns are likely to engage different endogenous dendritic mechanisms (Chater et al, 2022 ). Here, we confirmed this hypothesis by using two-photon glutamate uncaging and imaging and utilizing different stimulation uncaging patterns for the induction of homosynaptic plasticity.…”

Dendritic spine neck plasticity controls synaptic expression of long-term potentiation

“…Here we simulated only a single-spine stimulation protocol, but if multiple neighbouring spines were stimulated simultaneously, as likely occurs in vivo, then these spillover signals could accumulate to induce substantial heterosynaptic plasticity effects [75][76][77][78]. Data-driven computational modelling of these processes may be useful for dissecting out the contributions of these various interacting spatiotemporal processes in dendrites during learning.…”

“…Experimentally, these two molecules are reported to diffuse into neighbouring spines [14,47,59,62] where they could potentially either directly lead to plastic changes in the strengths of synapses, or modulate their propensity for plasticity. Here we simulated only a single-spine stimulation protocol, but if multiple neighbouring spines were stimulated simultaneously, as likely occurs in vivo , then these spillover signals could accumulate to induce substantial heterosynaptic plasticity effects [75-78]. Data-driven computational modelling of these processes may be useful for dissecting out the contributions of these various interacting spatiotemporal processes in dendrites during learning.…”

SpyDen: Automating molecular and structural analysis across spines and dendrites

“…The algorithm annotations From top to bottom: Raw image, showing the dendritic stretch and associated spiny protrusions, manual annotation of of the dendrite by an expert overlaid in red, segmentation achieved with the SpyDen tool if default values are used in green and optimal segmentation if the algorithm parameters are augmented (blue). B) As in A), but using a set of data from the Chater dataset (Chater et al, 2022). Colour scheme remains the same.…”