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In Nature communications ; h5-index 260.0

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.

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