Autonomous patch-clamp robot for functional characterization of neurons in vivo: development and application to mouse...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Electrophysiology

Background:

• In vivo patch clamping is crucial for cell-type characterization but is limited by low throughput and high skill requirements.
• Existing methods are manual, hindering scalability for large-scale neuroscience studies.

Purpose of the Study:

• To develop an autonomous robotic system for high-throughput in vivo patch-clamp recordings.
• To enable automated, serial intracellular recordings for functional cell-type classification.

Main Methods:

• An automated robot was designed to perform sequential patch-clamp recordings using a carousel of 40 pipettes.
• The system integrates automated pipette filling, brain insertion, cell localization, patching, and disposal.
• Automated visual stimulation and electrophysiology software facilitated functional cell-type classification.

Main Results:

• The robot successfully performed multiple, consecutive in vivo patch-clamp recordings in anesthetized mice.
• Functional cell-type classification was achieved for 37 cells in the mouse visual cortex (V1) layer 5.
• The system demonstrated a 9% yield with an average of 5.3 minutes per attempt over hundreds of trials.

Conclusions:

• The developed autonomous robot significantly advances in vivo patch-clamp technique by automating a highly manual process.
• This automated approach offers a standardized, quantitative method to overcome limitations in throughput and scalability.
• The technology is poised to enable large-scale studies and integration with complementary techniques for comprehensive cell characterization.