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Computational modelling of trans-synaptic nanocolumns, a modulator of synaptic transmission

Li, X.; Hemond, G.; Godin, A. G.; Doyon, N.

2021-10-08 neuroscience
10.1101/2021.10.07.463460 bioRxiv
Show abstract

Nanocolumns are trans-synaptic structures which align presynaptic vesicles release sites and postsynaptic receptors. However, how these nano structures shape synaptic signaling remains little understood. Given the difficulty to probe submicroscopic structures experimentally, computer modelling is a usefull approach to investigate the possible functional impacts of nanocolumns. In our in silico model, as has been experimentally observed, a nanocolumn is characterized by a tight distribution of postsynaptic receptors aligned with the presynaptic vesicle release site and by the presence of trans-synaptic molecules which can modulate neurotransmitter diffusion. We found that nanocolumns can play an important role in reinforcing synaptic current mostly when the presynaptic vesicle contains a small number of neurotransmitters. We also show that synapses with and without nanocolumns could have differentiated responses to spontaneous or evoked events. Our work provides a new methodology to investigate in silico the role of the submicroscopic organization of the synapse. Author summaryNeurotransmitter release, diffusion, and binding to postsynaptic receptors are key steps in synaptic transmission. However, the submicroscopic arrangement of receptors and presynaptic sites of neurotransmitter release remains little investigated. Experimental observations revealed the presence of trans-synaptic nanocolumns which span both the pre and post synaptic sites and fine tune the position of the post synaptic receptors. The functional impact of these nanocolumns (i.e. their influence on synaptic current) is both little understood and difficult to investigate experimentally. Here we construct a novel in silico model to investigate the functional impact of nanocolumns and show that they could play a functional role in reinforcing weak synapses.

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