PT  - JOURNAL ARTICLE
AU  - Koos, Krisztian
AU  - Oláh, Gáspár
AU  - Balassa, Tamas
AU  - Mihut, Norbert
AU  - Rózsa, Márton
AU  - Ozsvár, Attila
AU  - Tasnadi, Ervin
AU  - Barzó, Pál
AU  - Faragó, Nóra
AU  - Puskás, László
AU  - Molnár, Gábor
AU  - Molnár, József
AU  - Tamás, Gábor
AU  - Horvath, Peter
TI  - Automatic deep learning driven label-free image guided patch clamp system for human and rodent in vitro slice physiology
AID  - 10.1101/2020.05.05.078162
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.05.05.078162
4099  - http://biorxiv.org/content/early/2020/05/05/2020.05.05.078162.short
4100  - http://biorxiv.org/content/early/2020/05/05/2020.05.05.078162.full
AB  - Patch clamp recording of neurons is a labor-intensive and time-consuming procedure. We have developed 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 was 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 were 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.ONE SENTENCE SUMMARY Novel deep learning and image analysis algorithms for automated patch clamp systems to reliably measure neurons in human and rodent brain slices.Competing Interest StatementThe authors have declared no competing interest.