Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level

Yuan, X.; Schröter, M.; Obien, M. E. J.; Fiscella, M.; Gong, W.; Kikuchi, T.; Odawara, A.; Noji, S.; Suzuki, I.; Takahashi, J.; Hierlemann, A.; Frey, U.

doi:10.1101/2020.05.01.071787

bioRxiv · 10.1101/2020.05.01.071787

Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level

Yuan, X.·Schröter, M.·Obien, M. E. J.·Fiscella, M.·Gong, W.·Kikuchi, T.·Odawara, A.·Noji, S.·Suzuki, I.·Takahashi, J.·Hierlemann, A.·Frey, U.

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Abstract

Chronic imaging of neuronal networks in vitro has provided fundamental insights into mechanisms underlying neuronal function. Existing labeling and optical imaging methods, however, cannot be used for continuous and long-term recordings of the dynamics and evolution of neuronal networks, as fluorescence indicators can cause phototoxicity. Here, we introduce a versatile platform for label-free, comprehensive and detailed electrophysiological live-cell imaging of various neurogenic cells and tissues over extended times. We report on a novel dual-mode high-density microelectrode array, which can simultaneously record in i) full-frame mode with 19,584 recording sites and ii) high-signal-to-noise mode with 246 channels. We set out to demonstrate the capabilities of this platform with recordings from primary and iPSC-derived neuronal cultures and tissue preparations over several weeks, providing detailed morpho-electrical phenotypic parameters at subcellular, cellular and network level. Moreover, we developed reliable analysis tools with drastically increased throughput for extracting axonal morphology and conduction parameters.

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Yuan, X., Schröter, M., Obien, M. E. J., Fiscella, M., Gong, W., Kikuchi, T., Odawara, A., Noji, S., Suzuki, I., Takahashi, J., Hierlemann, A., Frey, U.. 2020-05-03. Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level. https://doi.org/10.1101/2020.05.01.071787

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