A Predictive Model of Seal Condition in Automated Patch Clamp System

Abstract

Patch clamp, the fundamental technique in electrophysiology, provides evidence for analyzing physiological activities of ion channels. The gigaseal formation process is an essential factor for guaranteeing recording condition. This process contributes to monitor biological ion channel currents by reducing the leakage current between pipette tip and cell membrane. While automated patch clamp systems are booming, implementation of criteria derived from empirical values inevitably randomizes the success of giga-ohm seal. In this paper, we have addressed the seal condition between the bath current and the seal current in the gigaseal formation process. The sealing limit of cell membrane to pipette tip was indicated as the critical point of seal current. A predictive model based on the critical point has been proposed to optimize the threshold of the seal current for gigaseal formation. An automated patch clamp system with the predictive model (PM-APCS) has been designed and developed to harvest whole cell voltage clamp recordings. In the development, HEK 293 cells were employed for the validation of the method. The success rate of gigaseal formation was 95.9%, which could greatly advance the exiting manual or automatic methods. Overall, our findings provide important insights for the understanding of the mechanism of seal current. The predictive model has the potential to accelerate the application of various automated systems for electrophysiology.