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Automatic deep learning-driven label-free image-guided patch clamp system

Patch clamp recording of neurons is a labor-intensive and time-consuming procedure. Here, we demonstrate a tool that fully automatically performs electrophysiological recordings in label-free tissue slices. The automation covers the detection of cells in label-free images, calibration of the micropi...

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Autores principales: Koos, Krisztian, Oláh, Gáspár, Balassa, Tamas, Mihut, Norbert, Rózsa, Márton, Ozsvár, Attila, Tasnadi, Ervin, Barzó, Pál, Faragó, Nóra, Puskás, László, Molnár, Gábor, Molnár, József, Tamás, Gábor, Horvath, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875980/
https://www.ncbi.nlm.nih.gov/pubmed/33568670
http://dx.doi.org/10.1038/s41467-021-21291-4
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author Koos, Krisztian
Oláh, Gáspár
Balassa, Tamas
Mihut, Norbert
Rózsa, Márton
Ozsvár, Attila
Tasnadi, Ervin
Barzó, Pál
Faragó, Nóra
Puskás, László
Molnár, Gábor
Molnár, József
Tamás, Gábor
Horvath, Peter
author_facet Koos, Krisztian
Oláh, Gáspár
Balassa, Tamas
Mihut, Norbert
Rózsa, Márton
Ozsvár, Attila
Tasnadi, Ervin
Barzó, Pál
Faragó, Nóra
Puskás, László
Molnár, Gábor
Molnár, József
Tamás, Gábor
Horvath, Peter
author_sort Koos, Krisztian
collection PubMed
description Patch clamp recording of neurons is a labor-intensive and time-consuming procedure. Here, we demonstrate a tool that fully automatically performs electrophysiological recordings in label-free tissue slices. The automation covers the detection of cells in label-free images, calibration of the micropipette movement, approach to the cell with the pipette, formation of the whole-cell configuration, and recording. The cell detection is based on deep learning. The model is trained on a new image database of neurons in unlabeled brain tissue slices. The pipette tip detection and approaching phase use image analysis techniques for precise movements. High-quality measurements are performed on hundreds of human and rodent neurons. We also demonstrate that further molecular and anatomical analysis can be performed on the recorded cells. The software has a diary module that automatically logs patch clamp events. Our tool can multiply the number of daily measurements to help brain research.
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spelling pubmed-78759802021-02-24 Automatic deep learning-driven label-free image-guided patch clamp system Koos, Krisztian Oláh, Gáspár Balassa, Tamas Mihut, Norbert Rózsa, Márton Ozsvár, Attila Tasnadi, Ervin Barzó, Pál Faragó, Nóra Puskás, László Molnár, Gábor Molnár, József Tamás, Gábor Horvath, Peter Nat Commun Article Patch clamp recording of neurons is a labor-intensive and time-consuming procedure. Here, we demonstrate a tool that fully automatically performs electrophysiological recordings in label-free tissue slices. The automation covers the detection of cells in label-free images, calibration of the micropipette movement, approach to the cell with the pipette, formation of the whole-cell configuration, and recording. The cell detection is based on deep learning. The model is trained on a new image database of neurons in unlabeled brain tissue slices. The pipette tip detection and approaching phase use image analysis techniques for precise movements. High-quality measurements are performed on hundreds of human and rodent neurons. We also demonstrate that further molecular and anatomical analysis can be performed on the recorded cells. The software has a diary module that automatically logs patch clamp events. Our tool can multiply the number of daily measurements to help brain research. Nature Publishing Group UK 2021-02-10 /pmc/articles/PMC7875980/ /pubmed/33568670 http://dx.doi.org/10.1038/s41467-021-21291-4 Text en © The Author(s) 2021 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
Koos, Krisztian
Oláh, Gáspár
Balassa, Tamas
Mihut, Norbert
Rózsa, Márton
Ozsvár, Attila
Tasnadi, Ervin
Barzó, Pál
Faragó, Nóra
Puskás, László
Molnár, Gábor
Molnár, József
Tamás, Gábor
Horvath, Peter
Automatic deep learning-driven label-free image-guided patch clamp system
title Automatic deep learning-driven label-free image-guided patch clamp system
title_full Automatic deep learning-driven label-free image-guided patch clamp system
title_fullStr Automatic deep learning-driven label-free image-guided patch clamp system
title_full_unstemmed Automatic deep learning-driven label-free image-guided patch clamp system
title_short Automatic deep learning-driven label-free image-guided patch clamp system
title_sort automatic deep learning-driven label-free image-guided patch clamp system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875980/
https://www.ncbi.nlm.nih.gov/pubmed/33568670
http://dx.doi.org/10.1038/s41467-021-21291-4
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