An in vitro model of neuronal ensembles

Advances in 3D neuronal cultures, such as brain spheroids and organoids, are allowing unprecedented in vitro access to some of the molecular, cellular and developmental mechanisms underlying brain diseases. However, their efficacy in recapitulating brain network properties that encode brain function...

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Veröffentlicht in:Nature communications 2022-06, Vol.13 (1), p.3340-3340, Article 3340
Hauptverfasser: Rabadan, M. Angeles, De La Cruz, Estanislao Daniel, Rao, Sneha B., Chen, Yannan, Gong, Cheng, Crabtree, Gregg, Xu, Bin, Markx, Sander, Gogos, Joseph A., Yuste, Rafael, Tomer, Raju
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Sprache:eng
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Zusammenfassung:Advances in 3D neuronal cultures, such as brain spheroids and organoids, are allowing unprecedented in vitro access to some of the molecular, cellular and developmental mechanisms underlying brain diseases. However, their efficacy in recapitulating brain network properties that encode brain function remains limited, thereby precluding development of effective in vitro models of complex brain disorders like schizophrenia. Here, we develop and characterize a Modular Neuronal Network (MoNNet) approach that recapitulates specific features of neuronal ensemble dynamics, segregated local-global network activities and a hierarchical modular organization. We utilized MoNNets for quantitative in vitro modelling of schizophrenia-related network dysfunctions caused by highly penetrant mutations in SETD1A and 22q11.2 risk loci. Furthermore, we demonstrate its utility for drug discovery by performing pharmacological rescue of alterations in neuronal ensembles stability and global network synchrony. MoNNets allow in vitro modelling of brain diseases for investigating the underlying neuronal network mechanisms and systematic drug discovery. Advances in 3D neuronal cultures have allowed unprecedented access to the mechanisms underlying brain diseases. This work describes the novel Modular Neuronal Network (MoNNet) system, which enables more complex studies of cortical neuronal ensemble dynamics.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-022-31073-1