Unsupervised Idealization of Ion Channel Recordings by Minimum Description Length: Application to Human PIEZO1-Channels

Frontiers in Neuroinformatics
Radhakrishnan GnanasambandamJakob K Dreyer

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 analys...Continue Reading

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Citations

Jul 12, 2018·IEEE Transactions on Nanobioscience·Florian PeinAxel Munk
Jan 12, 2020·Communications Biology·Numan CelikRichard Barrett-Jolley
Apr 11, 2021·European Biophysics Journal : EBJ·Florian PeinAxel Munk
Oct 5, 2021·ACS Sensors·Chenyu WenShi-Li Zhang

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Software Mentioned

SMURF
MATLAB
smuceR
package
SCAN
SKM
MDL
QuB
stepR
R

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