Skip to main content
Dryad logo

Synaptic connectivity and neuronal network activity changes after extracellular matrix depletion

Citation

Dzyubenko, Egor; Hermann, Dirk; Fleischer, Michael (2021), Synaptic connectivity and neuronal network activity changes after extracellular matrix depletion, Dryad, Dataset, https://doi.org/10.5061/dryad.vx0k6djpp

Abstract

Maintaining the balance between excitation and inhibition is essential for the appropriate control of neuronal network activity. Sustained excitation-inhibition (E-I) balance relies on the orchestrated adjustment of synaptic strength, neuronal activity and network circuitry. While growing evidence indicates that extracellular matrix (ECM) of the brain is a crucial regulator of neuronal excitability and synaptic plasticity, it remains unclear whether and how ECM contributes to neuronal circuit stability. Here we demonstrate that the integrity of ECM supports the maintenance of E-I balance by retaining inhibitory connectivity. Depletion of ECM in mature neuronal networks preferentially decreases the density of inhibitory synapses and the size of individual inhibitory postsynaptic scaffolds. After ECM depletion, inhibitory synapse strength homeostatically increases via the reduction of presynaptic GABAB receptors. However, the inhibitory connectivity reduces to an extent that inhibitory synapse scaling is no longer efficient in controlling neuronal network activity. Our results indicate that the brain ECM preserves the balanced network state by stabilizing inhibitory synapses.

Methods

We performed multi-electrode array (MEA) recordings, synapse density quantifications, patch-clamp electrophysiology, STED microscopy and in silico neuronal network activity simulations. The data was processed with in-house scripts for ImageJ and MatLab, and with standard programs bundled with the equipment

Usage Notes

All notes and nesessary explanations are provided within the uploaded file folders

Funding

Deutsche Forschungsgemeinschaft, Award: 259317790

Deutsche Forschungsgemeinschaft, Award: 389030878

Deutsche Forschungsgemeinschaft, Award: 405358801