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

Nat Commun. 2021 Feb 10;12(1):936. doi: 10.1038/s41467-021-21291-4.

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.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Animals
  • Automation
  • Brain / cytology
  • Deep Learning*
  • Electrophysiology
  • Female
  • Humans
  • Image Processing, Computer-Assisted
  • Male
  • Middle Aged
  • Neurons / chemistry
  • Neurons / cytology*
  • Patch-Clamp Techniques
  • Rats
  • Rats, Wistar
  • Software
  • Video Recording