Model-Free Idealization: Adaptive Integrated Approach for Idealization of Ion Channel Currents (AI2)

TL;DR

AI2 is a robust, model-free algorithm that automatically idealizes ion channel currents from noisy recordings using Kalman filtering and GMM clustering, effective even with poor SNR and baseline drifts.

Contribution

This paper introduces AI2, a novel, fully automated idealization method combining Kalman filter and GMM clustering for ion channel data without prior model assumptions.

Findings

AI2 outperforms conventional threshold-crossing methods.

AI2 effectively handles low SNR and baseline drifts.

Validated on both simulated and real biological data.

Abstract

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 information on the gating kinetics of ion channels. However, the idealization step is often challenging in cases of currents with poor signal-to-noise ratios (SNR) and baseline drifts, especially when the gating model of the target channel is not identified. We report herein on a highly robust model-free idealization method for achieving this goal. The algorithm, called AI2 (Adaptive Integrated Approach for the Idealization of Ion Channel Currents), is composed of Kalman filter and Gaussian Mixture Model (GMM) 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 which contain either computed or experimentally recorded noise. 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.