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

Krisztian Koos, Gáspár Oláh, Tamas Balassa, Norbert Mihut, Márton Rózsa, Attila Ozsvár, Ervin Tasnadi, Pál Barzó, Nóra Faragó, László Puskás, Gábor Molnár, József Molnár, Gábor Tamás and Peter Horvath ()
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Krisztian Koos: Eötvös Loránd Research Network
Gáspár Oláh: University of Szeged
Tamas Balassa: Eötvös Loránd Research Network
Norbert Mihut: University of Szeged
Márton Rózsa: University of Szeged
Attila Ozsvár: University of Szeged
Ervin Tasnadi: Eötvös Loránd Research Network
Pál Barzó: University of Szeged
Nóra Faragó: University of Szeged
László Puskás: Institute of Genetics, Biological Research Centre
Gábor Molnár: University of Szeged
József Molnár: Eötvös Loránd Research Network
Gábor Tamás: University of Szeged
Peter Horvath: Eötvös Loránd Research Network

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-21291-4

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