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Discriminating single-bacterial shape using low-aspect-ratio pores.

Makusu Tsutsui1, Takeshi Yoshida2, Kazumichi Yokota2

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This study introduces a novel method using nanopore technology and machine learning to detect bacterial shape, not just size. This approach achieves up to 90% accuracy in distinguishing single bacterial cells by their unique electrical signatures.

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Area of Science:

  • Nanotechnology
  • Biophysics
  • Machine Learning

Background:

  • Resistive pulse analysis traditionally identifies particles by size based on ionic current blockage.
  • Distinguishing particles with similar size and morphology using conventional methods is challenging.

Purpose of the Study:

  • To demonstrate the shape-sensing capability of solid-state nanopore sensors.
  • To leverage machine learning for analyzing ionic current waveforms to detect nanoscopic differences in microbial shape.

Main Methods:

  • Utilizing nanopore technology to generate ionic current waveforms.
  • Applying machine learning algorithms to pattern-analyze sub-nanoampere corrugations in the waveforms.
  • Examining ionic current spikes for bacteria with closely resembled morphology and size.

Main Results:

  • Identified characteristic electrical signatures corresponding to nanoscopic differences in microbial shape.
  • Achieved up to 90% accuracy in discriminating single bacterial cells based on shape.
  • Demonstrated that ionic current patterns can reflect subtle morphological variations.

Conclusions:

  • Machine learning combined with nanopore technology enables shape sensing of single particles.
  • This data-analytics-driven microporescopy offers new applications for resistive pulse analysis.
  • Potential for screening viruses and bacteria based on unique morphologies at a single-particle level.