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Related Concept Videos

Patch Clamp01:18

Patch Clamp

6.0K
Many fundamental cell functions such as muscle contraction and nerve transmission rely on the electrical signals produced by the movement of positively and negatively charged ions across the cell membrane. One competent method to record current flowing across the whole cell or single ion channel is the patch-clamp technique.
In this method, a glass micropipette containing electrolyte solution is tightly sealed against a small portion of the cell membrane. As a result, a patch of the cell...
6.0K

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Related Experiment Video

Updated: Nov 18, 2025

Application of Automated Image-guided Patch Clamp for the Study of Neurons in Brain Slices
09:05

Application of Automated Image-guided Patch Clamp for the Study of Neurons in Brain Slices

Published on: July 31, 2017

11.9K

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

Krisztian Koos1, Gáspár Oláh2, Tamas Balassa1

  • 1Synthetic and Systems Biology Unit, Biological Research Centre, Eötvös Loránd Research Network, Szeged, Hungary.

Nature Communications
|February 11, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an automated tool for patch clamp recordings in brain slices, significantly speeding up electrophysiology. The system uses deep learning for cell detection and image analysis for precise pipette control, enabling high-quality neuronal recordings.

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Related Experiment Videos

Last Updated: Nov 18, 2025

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Area of Science:

  • Neuroscience
  • Electrophysiology
  • Biomedical Engineering

Background:

  • Patch clamp recording is crucial for understanding neuronal function but is limited by its manual, time-intensive nature.
  • Existing methods require significant user expertise and are difficult to scale for high-throughput studies.

Purpose of the Study:

  • To develop and validate a fully automated system for patch clamp electrophysiological recordings in label-free tissue slices.
  • To enhance the efficiency and throughput of neuronal recordings for brain research.

Main Methods:

  • Development of a deep learning model for automated cell detection in label-free neuronal images.
  • Implementation of image analysis techniques for precise micropipette movement, cell approach, and whole-cell configuration.
  • Integration of automated logging via a diary module for patch clamp events.

Main Results:

  • Successful high-quality electrophysiological measurements on hundreds of human and rodent neurons.
  • Demonstration of automated cell detection, pipette calibration, and whole-cell configuration.
  • Validation of the tool's ability to perform recordings in label-free tissue slices.

Conclusions:

  • The automated patch clamp system significantly increases the number of daily measurements possible.
  • This tool has the potential to accelerate neuroscience research by overcoming the limitations of manual electrophysiology.
  • The system facilitates subsequent molecular and anatomical analyses on recorded cells.