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

Chronic imaging of neuronal networks in vitro has provided fundamental insights into mechanisms underlying neuronal function. Current labeling and optical imaging methods, however, cannot be used for continuous and long-term recordings of the dynamics and evolution of neuronal networks, as fluoresce...

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Autores principales: Yuan, Xinyue, Schröter, Manuel, Obien, Marie Engelene J., Fiscella, Michele, Gong, Wei, Kikuchi, Tetsuhiro, Odawara, Aoi, Noji, Shuhei, Suzuki, Ikuro, Takahashi, Jun, Hierlemann, Andreas, Frey, Urs
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7519655/
https://www.ncbi.nlm.nih.gov/pubmed/32978383
http://dx.doi.org/10.1038/s41467-020-18620-4
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author Yuan, Xinyue
Schröter, Manuel
Obien, Marie Engelene J.
Fiscella, Michele
Gong, Wei
Kikuchi, Tetsuhiro
Odawara, Aoi
Noji, Shuhei
Suzuki, Ikuro
Takahashi, Jun
Hierlemann, Andreas
Frey, Urs
author_facet Yuan, Xinyue
Schröter, Manuel
Obien, Marie Engelene J.
Fiscella, Michele
Gong, Wei
Kikuchi, Tetsuhiro
Odawara, Aoi
Noji, Shuhei
Suzuki, Ikuro
Takahashi, Jun
Hierlemann, Andreas
Frey, Urs
author_sort Yuan, Xinyue
collection PubMed
description Chronic imaging of neuronal networks in vitro has provided fundamental insights into mechanisms underlying neuronal function. Current labeling and optical imaging methods, however, cannot be used for continuous and long-term recordings of the dynamics and evolution of neuronal networks, as fluorescent 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 time scales. We report on a 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 develop reliable analysis tools, which drastically increase the throughput to infer axonal morphology and conduction speed.
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spelling pubmed-75196552020-10-14 Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level Yuan, Xinyue Schröter, Manuel Obien, Marie Engelene J. Fiscella, Michele Gong, Wei Kikuchi, Tetsuhiro Odawara, Aoi Noji, Shuhei Suzuki, Ikuro Takahashi, Jun Hierlemann, Andreas Frey, Urs Nat Commun Article Chronic imaging of neuronal networks in vitro has provided fundamental insights into mechanisms underlying neuronal function. Current labeling and optical imaging methods, however, cannot be used for continuous and long-term recordings of the dynamics and evolution of neuronal networks, as fluorescent 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 time scales. We report on a 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 develop reliable analysis tools, which drastically increase the throughput to infer axonal morphology and conduction speed. Nature Publishing Group UK 2020-09-25 /pmc/articles/PMC7519655/ /pubmed/32978383 http://dx.doi.org/10.1038/s41467-020-18620-4 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Yuan, Xinyue
Schröter, Manuel
Obien, Marie Engelene J.
Fiscella, Michele
Gong, Wei
Kikuchi, Tetsuhiro
Odawara, Aoi
Noji, Shuhei
Suzuki, Ikuro
Takahashi, Jun
Hierlemann, Andreas
Frey, Urs
Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level
title Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level
title_full Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level
title_fullStr Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level
title_full_unstemmed Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level
title_short Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level
title_sort versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7519655/
https://www.ncbi.nlm.nih.gov/pubmed/32978383
http://dx.doi.org/10.1038/s41467-020-18620-4
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