Solid-supported membrane-based electrophysiology (SSME) has evolved into an accessible and convenient technology for measuring electrogenic membrane transport, offering advantages in reproducibility, time resolution, and quantitative analysis. This technique relies on capacitive coupling between membrane fragments or reconstituted proteoliposomes and the solid-supported membrane (SSM).

Due to their capacitive, transient nature, the electrical signals recorded with SSME are non-linearly related to protein function, sometimes making it challenging to associate the recorded signal with molecular function, especially for researchers who are not experts in electrophysiology.

A recent study by Adel Hussein, Xihui Zhang, and David L. Stokes addresses this issue and provides a numerical model to better understand the nuances of SSME measurements and optimize experimental conditions.

The model is implemented in Python to simulate data from two assays: SSME and the DisC3 fluorescence assay (measuring membrane potential with voltage-sensitive dyes). The parameters of the model were carefully tuned to mimic experimental data and explore the effects of various factors, such as nonselective leak conductance and turnover rates.

The model demonstrates that leak conductance of cell membrane fragments or proteoliposomal membranes can have a significant effect on the results. Specifically, the apparent exponential rate constant in DisC3 fluorescence data was strongly influenced by leak conductance.

On the other hand, simulations indicate that the initial peak current in SSME data, which corresponds to the initial rate of ion transport, is relatively insensitive to leak conductance, suggesting that SSME provides a more accurate measurement of the initial rate from peak current.

The model also explored the impact of ionophores like nigericin and A23187, showing that while they facilitate ion equilibration, their effects can vary, necessitating empirical determination of suitable concentrations.

The study also provides data on the effect of mutations on ion transport properties, such as how certain mutations in the KdpFABC transporter alter its ion selectivity and affinity, validated through SSME studies.

Overall, this study presents a valuable computational tool for researchers studying transporters with SSME. By providing a deeper understanding of the factors affecting SSME assay measurements, the model helps in optimizing experimental conditions and interpreting the data.

—

Find the full article here: https://www.sciencedirect.com/science/article/pii/S2667074724000284

Learn more about SSM-Based Electrophysiology and SURFE²R devices here: https://www.nanion.de/products/surfe2r-n1/