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- Title
Automatic deep learning-driven label-free image-guided patch clamp system.
- 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
- 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. 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.
- Subjects
IMAGE databases; CELL imaging; IMAGE analysis; DEEP learning; NEURONS; PHYSIOLOGY
- Publication
Nature Communications, 2021, Vol 12, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-021-21291-4