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Rapid single-cell detection and identification of pathogens by using surface-enhanced Raman spectroscopy
N E Dina1, H Zhou2, A Colniţă1
1Department of Molecular and Biomolecular Physics, National Institute of R&D of Isotopic and Molecular Technologies, Donat 67-103, Cluj-Napoca 400293, Romania.
The Analyst
|April 22, 2017
Summary
This study introduces a rapid, label-free biosensor using surface-enhanced Raman scattering (SERS) for identifying pathogenic bacteria. The developed method achieves single-cell detection in under five minutes, crucial for timely infection diagnosis.
Area of Science:
- Biomedical Engineering
- Analytical Chemistry
- Microbiology
Background:
- Timely and accurate identification of pathogenic bacteria is critical for effective infection treatment.
- Current diagnostic methods often lack the required speed, sensitivity, or cost-effectiveness for real-time analysis.
Purpose of the Study:
- To develop a rapid, label-free biosensor for the identification and discrimination of pathogenic bacteria at the single-cell level.
- To leverage surface-enhanced Raman scattering (SERS) for high sensitivity and multiplex capacity in bacterial detection.
Main Methods:
- Utilized surface-enhanced Raman scattering (SERS) with in situ synthesized silver nanoparticles (NPs) as the SERS substrate.
- Developed a direct detection platform enabling intimate contact between NPs and bacterial membranes for enhanced signals.
- Applied the method to identify various bacterial genera, including Gram-negative and Gram-positive species.
Main Results:
- Successfully identified several genera of bacteria commonly found in bloodstream infections within 5 minutes.
- Achieved single-cell level detection with high reproducibility of SERS spectra.
- Demonstrated the method's effectiveness on diverse microorganisms like *E. coli*, *M. morganii*, *E. lactis*, and *L. casei*.
Conclusions:
- Developed a cost-effective, label-free SERS-based biosensor for rapid pathogen detection.
- The biosensor offers minimal sample preparation, high accuracy, and a significantly reduced analysis time (less than 5 min).
- This technology holds significant promise for improving the speed and efficiency of infection diagnosis.
