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

• Published in: Nature communications Vol. 12; no. 1; pp. 936 - 11
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.02.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/116; 631/114/1305; 631/114/1314; 631/114/1564; 631/1647/2204/1453/1970; 9/74; Adult; Aged; Animals; Automation; Brain; Brain - cytology; Brain slice preparation; Deep Learning; Electrophysiology; Female; Humanities and Social Sciences; Humans; Image analysis; Image processing; Image Processing, Computer-Assisted; Labor; Male; Middle Aged; multidisciplinary; Neurons; Neurons - chemistry; Neurons - cytology; Patch-Clamp Techniques; Rats; Rats, Wistar; Recording; Rodents; Science; Science (multidisciplinary); Software
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-21291-4

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. Patch clamp recording of neurons is slow and labor-intensive. Here the authors present a method for automated deep learning driven label-free image guided patch clamp physiology to perform measurements on hundreds of human and rodent neurons.