The effect of pharmacological inhibition of Serine Proteases on neuronal networks in vitro

Abstract

Neurons are embedded in an extracellular matrix (ECM), which functions both as a scaffold and as a regulator of neuronal function. The ECM is in turn dynamically altered through the action of serine proteases, which break down its constituents. This pathway has been implicated in the regulation of synaptic plasticity and of neuronal intrinsic excitability. In this study, we determined the short-term effects of interfering with proteolytic processes in the ECM, with a newly developed serine protease inhibitor. We monitored the spontaneous electrophysiological activity of in vitro primary rat cortical cultures, using microelectrode arrays. While pharmacological inhibition at a low dosage had no significant effect, at elevated concentrations it altered significantly network synchronization and functional connectivity but left unaltered single-cell electrical properties. These results suggest that serine protease inhibition affects synaptic properties, likely through its actions on the ECM.

Enhancement of Neuroglial Extracellular Matrix Formation and Physiological Activity of Dopaminergic Neural Cocultures by Macromolecular Crowding

“…Other studies have shown that stress during development and in adulthood reduces neural plasticity by modulating structural-ECM deposition [ 18 , 19 ]. In vivo functional studies using knockout mice have shown that the ECM composition also regulates the release of extracellular proteolytic factors such as thrombin, tissue plasminogen activator, neurotrypsin, neuropsin, matrix metalloproteinases, and MMP-9; it has also been shown to modulate brain activity and be involved in neurological disorders [ 20 , 21 ].…”

Modeling the three-dimensional connectivity of in vitro cortical ensembles coupled to Micro-Electrode Arrays

“…The instantaneous firing rate (IFR) of each network burst were aligned by evaluating the delay that produces the maximum absolute value of cross-correlation between a randomly chosen IFR trace and the remaining network bursts and temporally shifting the data accordingly. The average STH was accepted when the correlation coefficient was higher than 0.4 [52]. Then, the rising and decaying phases were individually fitted to evaluate the temporal evolution with the following exponential functions, respectively:…”

Human neuronal networks on micro-electrode arrays are a highly robust tool to study disease-specific genotype-phenotype correlationsin vitro

“…Analysis of the average burst shape. The generation of average burst shapes for each control was performed using adapting scripts and functions implemented in MATLAB (Melé et al 2016, Van De Vijver et al 2019. Spike trains containing all events detected in the 12 electrodes were binned.…”