Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection.

Journal: Nature communications
PMID: 34140500

Abstract

High-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity by using nanopores together with artificial intelligence, a relatively simple procedure that does not require RNA extraction. Our final platform, which we call the artificially intelligent nanopore, consists of machine learning software on a server, a portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. We show that artificially intelligent nanopores are successful in accurately identifying four types of coronaviruses similar in size, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2. Detection of SARS-CoV-2 in saliva specimen is achieved with a sensitivity of 90% and specificity of 96% with a 5-minute measurement.

Authors

  • Masateru Taniguchi
    The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan. taniguti@sanken.osaka-u.ac.jp.
  • Shohei Minami
    Department of Orthopaedic Surgery, Seirei Sakura Citizen Hospital.
  • Chikako Ono
    Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan.
  • Rina Hamajima
    Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan.
  • Ayumi Morimura
    Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
  • Shigeto Hamaguchi
    Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
  • Yukihiro Akeda
    Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan.
  • Yuta Kanai
    Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan.
  • Takeshi Kobayashi
    Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan.
  • Wataru Kamitani
    Graduate School of Medicine, Gunma University, Maebashi, Gunma, Japan.
  • Yutaka Terada
    Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA, USA.
  • Koichiro Suzuki
    The Research Foundation for Microbial Diseases of Osaka University, Suita, Osaka, Japan.
  • Nobuaki Hatori
    The Research Foundation for Microbial Diseases of Osaka University, Suita, Osaka, Japan.
  • Yoshiaki Yamagishi
    Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
  • Nobuei Washizu
    ADVANTEST Corporation, Kazo, Saitama, Japan.
  • Hiroyasu Takei
    Aipore Inc., Shibuya, Tokyo, Japan.
  • Osamu Sakamoto
    Aipore Inc., Shibuya, Tokyo, Japan.
  • Norihiko Naono
    Aipore Inc., Shibuya, Tokyo, Japan.
  • Kenji Tatematsu
    The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, Japan.
  • Takashi Washio
    The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan.
  • Yoshiharu Matsuura
    Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan. matsuura@biken.osaka-u.ac.jp.
  • Kazunori Tomono
    Graduate School of Medicine, Osaka University, Suita, Osaka, Japan. tomono@hp-infect.med.osaka-u.ac.jp.

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