Model-free idealization: Adaptive integrated approach for idealization of ion-channel currents

Single-channel electrophysiological recordings provide insights into transmembrane ion permeation and channel gating mechanisms. The first step in the analysis of the recorded currents involves an idealization process, in which noisy raw data are classified into two discrete levels corresponding to the open and closed states of channels. This provides valuable in-formation on the gating kinetics of ion channels. However, the idealization step is often challenging in cases of currents with poor signal-to-noise ratios and baseline drifts, especially when the gating model of the target channel is not identified. We report here-in on a highly robust model-free idealization method for achieving this goal. The algorithm, called adaptive integrated approach for idealization of ion-channel currents (AI2), is composed of Kalman filter and Gaussian mixture model clustering and functions without user input. AI2 automatically determines the noise reduction setting based on the degree of separation between the open and closed levels. We validated the method on pseudo-channel-current datasets that contain either computed or experimentally recorded noise. We also investigated the relationship between the noise reduction parameter of the Kalman filter and the cutoff frequency of the low-pass filter. The AI2 algorithm was then tested on actual experimental data for biological channels including gramicidin A, a voltage-gated sodium channel, and other unidentified channels. We compared the idealization results with those obtained by the conventional methods, including the 50%-threshold-crossing method.

Automated summary

This article introduces an idealization method for single-channel electrophysiological recordings that provides valuable information about the gating kinetics of ion channels. The method, called AI2, uses an adaptive integrated approach to process the noisy data and automatically determines the optimal noise reduction settings. The method has been validated and tested on both simulated and experimental data.