Abstract
Researchers can investigate the mechanistic and molecular basis of many physiological phenomena in cells by analyzing the fundamental properties of single ion channels. These analyses entail recording single channel currents and measuring current amplitudes and transition rates between conductance states. Since most electrophysiological recordings contain noise, the data analysis can proceed by idealizing the recordings to isolate the true currents from the noise. This de-noising can be accomplished with threshold crossing algorithms and Hidden Markov Models, but such procedures generally depend on inputs and supervision by the user, thus requiring some prior knowledge of underlying processes. Channels with unknown gating and/or functional sub-states and the presence in the recording of currents from uncorrelated background channels present substantial challenges to such analyses. Here we describe and characterize an idealization algorithm based on Rissanen's Minimum Description Length (MDL) Principle. This method uses minimal assumptions and idealizes ion channel recordings without requiring a detailed user input or a priori assumptions about channel conductance and kinetics. Furthermore, we demonstrate that correlation analysis of conductance steps can resolve properties of single ion channels in recordings contaminated by signals from multiple channels. We first validated our methods on simulated data defined with a range of different signal-to-noise levels, and then showed that our algorithm can recover channel currents and their substates from recordings with multiple channels, even under conditions of high noise. We then tested the MDL algorithm on real experimental data from human PIEZO1 channels and found that our method revealed the presence of substates with alternate conductances.
Highlights
The analysis of discrete events in single ion channel data has been a powerful tool in electrophysiology research since the pioneering work of Erwin Neher and Bert Sakmann, recognized with the 1991 Nobel Prize in Medicine
The channel was modeled as a two-state Markov process of unit step-size, with equal probability of being either open or closed, and with a state transition probability of 1/100, and the added Gaussian noise was set at signal-to-noise ratio (SNR) = 3.3 or SNR = 1 (Figure 1A top and lower panel respectively)
98% of the steps were detected at SNR = 3.3 and only 50% at SNR = 1
Summary
The analysis of discrete events in single ion channel data has been a powerful tool in electrophysiology research since the pioneering work of Erwin Neher and Bert Sakmann, recognized with the 1991 Nobel Prize in Medicine. These analyses usually entail data modeling methods that rely on user-defined inputs, filters, event detection thresholds or subjective. Idealization by Minimum Description Length criteria for event detection These conditions present difficulties, when analyzing time series composed of currents from multiple ion channels, or in cases when a channel can make a transition between one or more sub-conductance states. HMM analysis is most suitable when different conductance states of channels can be estimated a priori; so long as these estimates are valid, the HMM algorithm provides maximal information about the conductance transitions and their kinetics
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