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

Makusu Tsutsui, Takeshi Yoshida, Kazumichi Yokota, Hirotoshi Yasaki, Takao Yasui, Akihide Arima, Wataru Tonomura, Kazuki Nagashima, Takeshi Yanagida, Noritada Kaji, Masateru Taniguchi, Takashi Washio, Yoshinobu Baba, Tomoji Kawai

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Conventional concepts of resistive pulse analysis is to discriminate particles in liquid by the difference in their size through comparing the amount of ionic current blockage. In sharp contrast, we herein report a proof-of-concept demonstration of the shape sensing capability of solid-state pore sensors by leveraging the synergy between nanopore technology and machine learning. We found ionic current spikes of similar patterns for two bacteria reflecting the closely resembled morphology and size in an ultra-low thickness-to-diameter aspect-ratio pore. We examined the feasibility of a machine learning strategy to pattern-analyse the sub-nanoampere corrugations in each ionic current waveform and identify characteristic electrical signatures signifying nanoscopic differences in the microbial shape, thereby demonstrating discrimination of single-bacterial cells with accuracy up to 90%. This data-analytics-driven microporescopy capability opens new applications of resistive pulse analyses for screening viruses and bacteria by their unique morphologies at a single-particle level.

Original languageEnglish
Article number17371
JournalScientific reports
Volume7
Issue number1
DOIs
Publication statusPublished - Dec 1 2017

Fingerprint

Learning systems
Aspect ratio
Bacteria
Nanopores
Viruses
Screening
Demonstrations
Sensors
Liquids

All Science Journal Classification (ASJC) codes

  • General

Cite this

Tsutsui, M., Yoshida, T., Yokota, K., Yasaki, H., Yasui, T., Arima, A., ... Kawai, T. (2017). Discriminating single-bacterial shape using low-aspect-ratio pores. Scientific reports, 7(1), [17371]. https://doi.org/10.1038/s41598-017-17443-6

Discriminating single-bacterial shape using low-aspect-ratio pores. / Tsutsui, Makusu; Yoshida, Takeshi; Yokota, Kazumichi; Yasaki, Hirotoshi; Yasui, Takao; Arima, Akihide; Tonomura, Wataru; Nagashima, Kazuki; Yanagida, Takeshi; Kaji, Noritada; Taniguchi, Masateru; Washio, Takashi; Baba, Yoshinobu; Kawai, Tomoji.

In: Scientific reports, Vol. 7, No. 1, 17371, 01.12.2017.

Research output: Contribution to journalArticle

Tsutsui, M, Yoshida, T, Yokota, K, Yasaki, H, Yasui, T, Arima, A, Tonomura, W, Nagashima, K, Yanagida, T, Kaji, N, Taniguchi, M, Washio, T, Baba, Y & Kawai, T 2017, 'Discriminating single-bacterial shape using low-aspect-ratio pores', Scientific reports, vol. 7, no. 1, 17371. https://doi.org/10.1038/s41598-017-17443-6
Tsutsui M, Yoshida T, Yokota K, Yasaki H, Yasui T, Arima A et al. Discriminating single-bacterial shape using low-aspect-ratio pores. Scientific reports. 2017 Dec 1;7(1). 17371. https://doi.org/10.1038/s41598-017-17443-6
Tsutsui, Makusu ; Yoshida, Takeshi ; Yokota, Kazumichi ; Yasaki, Hirotoshi ; Yasui, Takao ; Arima, Akihide ; Tonomura, Wataru ; Nagashima, Kazuki ; Yanagida, Takeshi ; Kaji, Noritada ; Taniguchi, Masateru ; Washio, Takashi ; Baba, Yoshinobu ; Kawai, Tomoji. / Discriminating single-bacterial shape using low-aspect-ratio pores. In: Scientific reports. 2017 ; Vol. 7, No. 1.
@article{7bd117c50f4d46a98c5c52fdf8f2f07c,
title = "Discriminating single-bacterial shape using low-aspect-ratio pores",
abstract = "Conventional concepts of resistive pulse analysis is to discriminate particles in liquid by the difference in their size through comparing the amount of ionic current blockage. In sharp contrast, we herein report a proof-of-concept demonstration of the shape sensing capability of solid-state pore sensors by leveraging the synergy between nanopore technology and machine learning. We found ionic current spikes of similar patterns for two bacteria reflecting the closely resembled morphology and size in an ultra-low thickness-to-diameter aspect-ratio pore. We examined the feasibility of a machine learning strategy to pattern-analyse the sub-nanoampere corrugations in each ionic current waveform and identify characteristic electrical signatures signifying nanoscopic differences in the microbial shape, thereby demonstrating discrimination of single-bacterial cells with accuracy up to 90{\%}. This data-analytics-driven microporescopy capability opens new applications of resistive pulse analyses for screening viruses and bacteria by their unique morphologies at a single-particle level.",
author = "Makusu Tsutsui and Takeshi Yoshida and Kazumichi Yokota and Hirotoshi Yasaki and Takao Yasui and Akihide Arima and Wataru Tonomura and Kazuki Nagashima and Takeshi Yanagida and Noritada Kaji and Masateru Taniguchi and Takashi Washio and Yoshinobu Baba and Tomoji Kawai",
year = "2017",
month = "12",
day = "1",
doi = "10.1038/s41598-017-17443-6",
language = "English",
volume = "7",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",

}

TY - JOUR

T1 - Discriminating single-bacterial shape using low-aspect-ratio pores

AU - Tsutsui, Makusu

AU - Yoshida, Takeshi

AU - Yokota, Kazumichi

AU - Yasaki, Hirotoshi

AU - Yasui, Takao

AU - Arima, Akihide

AU - Tonomura, Wataru

AU - Nagashima, Kazuki

AU - Yanagida, Takeshi

AU - Kaji, Noritada

AU - Taniguchi, Masateru

AU - Washio, Takashi

AU - Baba, Yoshinobu

AU - Kawai, Tomoji

PY - 2017/12/1

Y1 - 2017/12/1

N2 - Conventional concepts of resistive pulse analysis is to discriminate particles in liquid by the difference in their size through comparing the amount of ionic current blockage. In sharp contrast, we herein report a proof-of-concept demonstration of the shape sensing capability of solid-state pore sensors by leveraging the synergy between nanopore technology and machine learning. We found ionic current spikes of similar patterns for two bacteria reflecting the closely resembled morphology and size in an ultra-low thickness-to-diameter aspect-ratio pore. We examined the feasibility of a machine learning strategy to pattern-analyse the sub-nanoampere corrugations in each ionic current waveform and identify characteristic electrical signatures signifying nanoscopic differences in the microbial shape, thereby demonstrating discrimination of single-bacterial cells with accuracy up to 90%. This data-analytics-driven microporescopy capability opens new applications of resistive pulse analyses for screening viruses and bacteria by their unique morphologies at a single-particle level.

AB - Conventional concepts of resistive pulse analysis is to discriminate particles in liquid by the difference in their size through comparing the amount of ionic current blockage. In sharp contrast, we herein report a proof-of-concept demonstration of the shape sensing capability of solid-state pore sensors by leveraging the synergy between nanopore technology and machine learning. We found ionic current spikes of similar patterns for two bacteria reflecting the closely resembled morphology and size in an ultra-low thickness-to-diameter aspect-ratio pore. We examined the feasibility of a machine learning strategy to pattern-analyse the sub-nanoampere corrugations in each ionic current waveform and identify characteristic electrical signatures signifying nanoscopic differences in the microbial shape, thereby demonstrating discrimination of single-bacterial cells with accuracy up to 90%. This data-analytics-driven microporescopy capability opens new applications of resistive pulse analyses for screening viruses and bacteria by their unique morphologies at a single-particle level.

UR - http://www.scopus.com/inward/record.url?scp=85037727986&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85037727986&partnerID=8YFLogxK

U2 - 10.1038/s41598-017-17443-6

DO - 10.1038/s41598-017-17443-6

M3 - Article

C2 - 29234023

AN - SCOPUS:85037727986

VL - 7

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 17371

ER -