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

doi:10.1126/sciadv.adp7991

	Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments
	Zhu, Longji 1; Yang, Yunan 1,2; Xu, Fei 1; Lu, Xinyu 3; Shuai, Mingrui 4,5; An, Zhulin 4; Chen, Xiaomeng 2; Li, Hu 1; Martin, Francis L.6,7; Vikesland, Peter J.8; Ren, Bin 3; Tian, Zhong-Qun 3; Zhu, Yong-Guan 1,9; Cui, Li 1
	2025-01-08
发表期刊	SCIENCE ADVANCES
ISSN	2375-2548
卷号	11 期号:2 页码:14
摘要	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.
DOI	10.1126/sciadv.adp7991
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[42021005] ; National Natural Science Foundation of China[32100083] ; National Key R&D Program of China[2022YFF0713100] ; National Natural Science Foundation of China[22176186] ; Chinese Academy of Sciences[ZDBS-LY- DQC027]
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:001392723500024
出版者	AMER ASSOC ADVANCEMENT SCIENCE
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40789
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhu, Yong-Guan; Cui, Li
作者单位	1.Chinese Acad Sci, Key Lab Urban Environm & Hlth, Inst Urban Environm, Xiamen 361021, Peoples R China 2.Northeast Agr Univ, Coll Life Sci, Harbin 150030, Peoples R China 3.Xiamen Univ, Coll Chem & Chem Engn, State Key Lab Phys Chem Solid Surfaces, Xiamen 361005, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 5.Anhui Univ, Hefei 230601, Peoples R China 6.Biocel UK Ltd, Kingston Upon Hull HU10 6TS, England 7.Blackpool Teaching Hosp NHS Fdn Trust, Dept Cellular Pathol, Whinney Heys Rd, Blackpool FY3 8NR, England 8.Virginia Tech, Dept Civil & Environm Engn, Blacksburg, VA 24061 USA 9.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Longji,Yang, Yunan,Xu, Fei,et al. Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments[J]. SCIENCE ADVANCES,2025,11(2):14.
APA	Zhu, Longji.,Yang, Yunan.,Xu, Fei.,Lu, Xinyu.,Shuai, Mingrui.,...&Cui, Li.(2025).Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments.SCIENCE ADVANCES,11(2),14.
MLA	Zhu, Longji,et al."Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments".SCIENCE ADVANCES 11.2(2025):14.