A 3D neuronal network read-out interface with high recording performance using a neuronal cluster patterning on a microelectrode array

In recent years, in vitro three-dimensional (3D) neuronal network models utilizing extracellular matrices have been advancing. To understand the network activity from these models, attempts have been made to measure activity in multiple regions simultaneously using a microelectrode array (MEA). Alth...

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Veröffentlicht in:Biosensors & bioelectronics 2024-10, Vol.261, p.116507, Article 116507
Hauptverfasser: Yoon, Dongjo, Nam, Yoonkey
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Sprache:eng
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Zusammenfassung:In recent years, in vitro three-dimensional (3D) neuronal network models utilizing extracellular matrices have been advancing. To understand the network activity from these models, attempts have been made to measure activity in multiple regions simultaneously using a microelectrode array (MEA). Although there hve been many attempts to measure the activity of 3D networks using 2-dimensional (2D) MEAs, the physical coupling between the 3D network and the microelectrodes was not stable and needed to be improved. In this study, we proposed a neuronal cluster interface that improves the active channel ratio of commercial 2D MEAs, enabling reliable measurement of 3D network activity. To achieve this, neuronal clusters, which consist of a small number of neurons, were patterned on microelectrodes and used as mediators to transmit the signal between the 3D network and the microelectrodes. We confirmed that the patterned neuronal clusters enhanced the active channel ratio and SNR(signal-to-noise-ratio) about 3D network recording and stimulation for a month. Our interface was able to functionally connect with 3D networks and measure the 3D network activity without significant alternation of activity characteristics. Finally, we demonstrated that our interface can be used to analyze the differences in the dynamics of 3D and 2D networks and to construct the 3D clustered network. This method is expected to be useful for studying the functional activity of various 3D neuronal network models, offering broad applications for the use of these models.
ISSN:0956-5663
1873-4235
1873-4235
DOI:10.1016/j.bios.2024.116507