Automatic deep learning driven label-free image guided patch clamp system for human and rodent in vitro slice physiology
| Autor: | Tamas Balassa, Krisztian Koos, Norbert Mihut, Gábor Tamás, Gáspár Oláh, Gábor Molnár, Peter Horvath, Jozsef Molnar, Attila Ozsvár, László G. Puskás, Pál Barzó, Nóra Faragó, Ervin Tasnadi, Márton Rózsa |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0303 health sciences
Computer science business.industry Deep learning Pipette In vitro slice 03 medical and health sciences Electrophysiology 0302 clinical medicine Automated patch clamp Computer vision Artificial intelligence Patch clamp business 030217 neurology & neurosurgery 030304 developmental biology Label free |
| DOI: | 10.1101/2020.05.05.078162 |
| Popis: | 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 SUMMARYNovel deep learning and image analysis algorithms for automated patch clamp systems to reliably measure neurons in human and rodent brain slices. |
| Databáze: | OpenAIRE |
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