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A predictive model for seal condition in an automated patch clamp system

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Abstract

The patch clamp technique is regarded as the gold standard in electrophysiological measurements. It provides quantitative recordings for investigations and analyses of physiological activities by cellular ion channels. The gigaseal formation process is an essential factor for guaranteeing patch clamp recording conditions. This process contributes to monitoring biological ion channel currents by reducing the leakage current between pipette tip and cell membrane. While automated patch clamp systems are thriving, the implementation of criteria derived from empirical values inevitably randomizes the success of giga-ohm seals. In this paper, we addressed the seal condition between the bath current and the seal current in the gigaseal formation process. The sealing limit of the cell membrane to the micro-opening was indicated as the critical point of seal current. A predictive model based on the critical point was proposed to optimize the threshold of the seal current for gigaseal formation. It offers a systematic approach for micro-opening type implementations of high-throughput design. An automated patch clamp system with a predictive model (PM-APCS) was designed and developed to obtain whole cell voltage clamp recordings. In the development, HEK 293 cells and C2C12 cells were employed for the validation of the method. The success rate of gigaseal formation was 95.9%, which could greatly advance the existing manual or automatic methods. Overall, our findings provide important insights for understanding the seal current mechanism. The predictive model has the potential to accelerate the application of various automated systems for electrophysiology.

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Funding

This research was partially supported by the TBRS grant from the Research Grant Council of the Hong Kong Special Administrative Region Government (T42-717/20-R).

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Authors and Affiliations

  1. Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, China

    Shengjie Yang & King Wai Chiu Lai

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  1. Shengjie Yang
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  2. King Wai Chiu Lai
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Contributions

S. Yang designed and performed the experiments, derived the model, and analyzed the data. K. W. C. Lai was involved in conceptual ideas and supervised the work. All authors discussed the results and contributed to the final manuscript.

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Correspondence to King Wai Chiu Lai.

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Yang, S., Lai, K.W.C. A predictive model for seal condition in an automated patch clamp system. J Micro-Bio Robot 18, 75–87 (2022). https://doi.org/10.1007/s12213-023-00160-x

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