Back

Asynchronous glutamate exocytosis is enhanced in low release probability synapses and is widely dispersed across the active zone

Mendonca, P.; Tagliatti, E.; Langley, H.; Kotzadimitriou, D.; Zamora-Chimal, C.; Timofeeva, Y.; Volynski, K.

2021-05-04 neuroscience
10.1101/2021.05.04.441792 bioRxiv
Show abstract

The balance between fast synchronous and delayed asynchronous release of neurotransmitters has a major role in defining computational properties of neuronal synapses and regulation of neuronal network activity. However, how it is tuned at the single synapse level remains poorly understood. Here, using the fluorescent glutamate sensor SF-iGluSnFR, we image quantal vesicular release in tens to hundreds of individual synaptic outputs (presynaptic boutons) from single pyramidal cells in culture with 4 millisecond temporal resolution, and localise vesicular release sites with ~ 75 nm spatial resolution. We find that the ratio between synchronous and asynchronous synaptic vesicle exocytosis varies extensively among presynaptic boutons supplied by the same axon, and that asynchronous release fraction is elevated in parallel with short-term facilitation at synapses with low release probability. We further demonstrate that asynchronous exocytosis sites are more widely distributed within the presynaptic release area than synchronous sites. These findings are consistent with a model in which functional presynaptic properties are regulated via a synapsespecific adjustment of the coupling distance between presynaptic Ca2+ channels and releaseready synaptic vesicles. Together our results reveal a universal relationship between the two major functional properties of synapses - the timing and the overall probability of neurotransmitter release.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.