Rapid single-cell detection and identification of pathogens by using surface-enhanced Raman spectroscopy. Issue 10 (21st April 2017)

• Record Type: Journal Article
• Authors: Dina, N. E.; Zhou, H.; Colniţă, A.; Leopold, N.; Szoke-Nagy, T.; Coman, C.; Haisch, C.
• Is Part Of: Analyst. Volume 142:Issue 10(2017)
• Page Start: 1782
• Page End: 1789
• Publication Date: 2017-04-21
• Subjects: Chemistry, Analytic -- Periodicals; 543
• DOI: 10.1039/c7an00106a
• Languages: English
• ISSNs: 0003-2654

Abstract

Abstract : For the successful treatment of infections, real-time analysis and enhanced multiplex capacity, sensitivity and cost-effectiveness of the developed detection method are critical. Abstract : For the successful treatment of infections, real-time analysis and enhanced multiplex capacity, sensitivity and cost-effectiveness of the developed detection method are critical. In this work, surface-enhanced Raman scattering (SERS) was employed with the final aim of identification and discrimination of pathogenic bacteria, based on their detected SERS fingerprint at the single-cell level. Several genera of bacteria that are found in most of the isolated infections in bacteraemia were successfully identified in less than 5 minutes without the use of antibodies or other specific receptors. The key element of the SERS direct detection platform is the SERS substrate, which combines easy production at low costs with a high enhancement enabling single-cell detection. The innovative approach of detection required the in situ synthesis of silver nanoparticles (NPs), ensuring an intimate contact with the bacterial membrane. This protocol provided a good reproducibility of the single-cell SERS spectra and was successfully applied both on Gram-negative and Gram-positive microorganisms ( E. coli, M. morganii, E. lactis, L. casei ). Thus, a label-free SERS-based biosensor for pathogen detection was developed with low costs, minimal sample preparation, high-accuracy and a very short analysis time of less than 5 min, which is crucial for infection diagnosis.