Investigating the Reliability of the Evoked Response in Human IPSCs-derived Neuronal Networks Coupled to Micro-electrode Arrays

Overview

Journal APL Bioeng

Date 2023 Dec 22

PMID 38130601

Authors

Giorgia Zanini

Giulia Parodi

Michela Chiappalone

Sergio Martinoia

Affiliations

Soon will be listed here.

Abstract

models of neuronal networks have emerged as a potent instrument for gaining deeper insights into the intricate mechanisms governing the human brain. Notably, the integration of human-induced pluripotent stem cells (hiPSCs) with micro-electrode arrays offers a means to replicate and dissect both the structural and functional elements of the human brain within a controlled environment. Given that neuronal communication relies on the emission of electrical (and chemical) stimuli, the employment of electrical stimulation stands as a mean to comprehensively interrogate neuronal assemblies, to better understand their inherent electrophysiological dynamics. However, the establishment of standardized stimulation protocols for cultures derived from hiPSCs is still lacking, thereby hindering the precise delineation of efficacious parameters to elicit responses. To fill this gap, the primary objective of this study resides in delineating effective parameters for the electrical stimulation of hiPSCs-derived neuronal networks, encompassing the determination of voltage amplitude and stimulation frequency able to evoke reliable and stable responses. This study represents a stepping-stone in the exploration of efficacious stimulation parameters, thus broadening the electrophysiological activity profiling of neural networks sourced from human-induced pluripotent stem cells.

Citing Articles

Inferring structure of cortical neuronal networks from activity data: A statistical physics approach.

Po H, Houben A, Haeb A, Jenkins D, Hill E, Parri H PNAS Nexus. 2025; 4(1):pgae565.

PMID: 39790102 PMC: 11713615. DOI: 10.1093/pnasnexus/pgae565.

Electrical and chemical modulation of homogeneous and heterogeneous human-iPSCs-derived neuronal networks on high density arrays.

Parodi G, Zanini G, Chiappalone M, Martinoia S Front Mol Neurosci. 2024; 17:1304507.

PMID: 38380114 PMC: 10877635. DOI: 10.3389/fnmol.2024.1304507.

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