Deep Learning-Enhanced Nanopore Sensing of Single-Nanoparticle Translocation Dynamics

Noise is ubiquitous in real space that hinders detection of minute yet important signals in electrical sensors. Here, the authors report on a deep learning approach for denoising ionic current in resistive pulse sensing. Electrophoretically-driven translocation motions of single-nanoparticles in a nano-corrugated nanopore are detected. The noise is reduced by a convolutional auto-encoding neural network, designed to iteratively compare and minimize differences between a pair of waveforms via a gradient descent optimization. This denoising in a high-dimensional feature space is demonstrated to allow detection of the corrugation-derived wavy signals that cannot be identified in the raw curves nor after digital processing in frequency domains under the given noise floor, thereby enabled in-situ tracking to electrokinetic analysis of fast-moving single- and double-nanoparticles. The ability of the unlabeled learning to remove noise without compromising temporal resolution may be useful in solid-state nanopore sensing of protein structure and polynucleotide sequence.

Automated summary

The study introduces a deep learning approach for denoising ionic current in resistive pulse sensing, enabling the detection of electrophoretically-driven translocation motions of single nanoparticles in a nano-corrugated nanopore. The noise reduction by convolutional auto-encoding neural network allows for the identification of corrugation-derived wavy signals in a high-dimensional feature space, facilitating the in-situ tracking of fast-moving single- and double-nanoparticles. This unlabeled learning method effectively removes noise without compromising temporal resolution, making it potentially useful in solid-state nanopore sensing applications for protein structure and polynucleotide sequence analysis.