ArticleScience advances2025
Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Boosting identification of microsporidian spores originating from different hosts: single-cell Raman spectroscopy combined with self-attention mechanism-driven convolutional neural network.Analytical and bioanalytical chemistry · 2026Article
- Photonic biosensors based on nanoparticle superstructures: from data analysis to artificial intelligence (AI) detection.Fundamental research · 2026Review
- Deep Learning-Enhanced Raman Microspectroscopy Enables Rapid Microbial Classification and Captures Phylogenetic Relationships.Microorganisms · 2026Article
- BactoRamanBioNet: A Multimodal Neural Network for Bacterial Species Identification Using Raman Spectroscopy and Biological Knowledge.Sensors (Basel, Switzerland) · 2026Article
- Emerging frontiers in SERS-integrated optical waveguides: advancing portable and ultra-sensitive detection for trace liquid analysis.Light, science & applications · 2025Review
- Raman spectroscopy detects trace bioaerosol particles.Science China. Life sciences · 2025Article
- Recognition Element-Based Strategies for Rapid Detection of Foodborne Pathogens: Recent Progress and Perspectives.Biosensors · 2025Review
- Physics-informed deep learning for plasmonic sensing of nanoscale protein dynamics in solution.Science advances · 2025Article
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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