Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments.

Journal: Science advances
Published Date: Jan 8, 2025

Abstract

Pathogenic bioaerosols are critical for outbreaks of airborne disease; however, rapidly and accurately identifying pathogens directly from complex air environments remains highly challenging. We present an advanced method that combines open-set deep learning (OSDL) with single-cell Raman spectroscopy to identify pathogens in real-world air containing diverse unknown indigenous bacteria that cannot be fully included in training sets. To test and further enhance identification, we constructed the Raman datasets of aerosolized bacteria. Through optimizing OSDL algorithms and training strategies, Raman-OSDL achieves 93% accuracy for five target airborne pathogens, 84% accuracy for untrained air bacteria, and 36% reduction in false positive rates compared to conventional close-set algorithms. It offers a high detection sensitivity down to 1:1000. When applied to real air containing >4600 bacterial species, our method accurately identifies single or multiple pathogens simultaneously within an hour. This single-cell tool advances rapidly surveilling pathogens in complex environments to prevent infection transmission.

Authors

  • Longji Zhu
    Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China.
  • Yunan Yang
    Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China.
  • Fei Xu
    GeoHealth Initiative, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Enschede, 7500, the Netherlands; International Initiative on Spatial Lifecourse Epidemiology (ISLE), the Netherlands; Nanjing Municipal Center for Disease Control and Prevention, Nanjing, Jiangsu, 210003, China; Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211100, China.
  • Xinyu Lu
    State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
  • Mingrui Shuai
    Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
  • Zhulin An
    Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
  • Xiaomeng Chen
    College of Life Science, Northeast Agricultural University, Harbin 150030, China.
  • Hu Li
    School of Business, Qingdao University, Qingdao, Shandong, China.
  • Francis L Martin
    Biocel UK Ltd., Hull HU10 6TS, UK.
  • Peter J Vikesland
    Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.
  • Bin Ren
  • Zhong-Qun Tian
    State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China.
  • Yong-Guan Zhu
    Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315830, China; State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China. Electronic address: ygzhu@rcees.edu.cn.
  • Li Cui
    Institute of Computing Technology(ICT), Chinese Academy of Sciences(CAS), Beijing, China. lcui@ict.ac.cn.

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