Back to Search Start Over

Rapid ultra-sensitive diagnosis of clostridium difficile infection using a SERS-based lateral flow assay.

Authors :
Hassanain, Waleed A.
Spoors, Julia
Johnson, Christopher L.
Faulds, Karen
Keegan, Neil
Graham, Duncan
Source :
Analyst. 7/21/2021, Vol. 146 Issue 14, p4495-4505. 11p.
Publication Year :
2021

Abstract

Clostridium difficile (C. diff) infection is one of the most contagious diseases associated with high morbidity and mortality rates in hospitalised patients. Accurate diagnosis can slow its spread by determining the most effective treatment. Herein, we report a novel testing platform as a proof-of-concept for the selective, sensitive, rapid and cost-effective diagnosis of C. diff infection (CDI) based on a duplex measurement. This was achieved by detecting two specific biomarkers, surface layer protein A (SlpA) and toxin B (ToxB), using a surface enhanced Raman scattering-based lateral flow assay (SERS-based LFA). The simultaneous duplex detection of SlpA with ToxB has not been described for the clinical diagnosis of CDI previously. The SlpA biomarker "AKDGSTKEDQLVDALA" was first reported by our group in 2018 as a species-specific identification tool. The second biomarker, ToxB, is the essential virulence biomarker of C. diff pathogenic strains and is required to confirm true infection pathogenicity. The proposed SERS-based LFA platform enabled rapid duplex detection of SlpA and ToxB on separate test lines using a duplex LF test strip within 20 minutes. The use of a handheld Raman spectrometer to scan test lines allowed for the highly sensitive quantitative detection of both biomarkers with a lowest observable concentration of 0.01 pg μL−1. The use of a handheld device in this SERS-based LFA instead of benchtop machine paves the way for rapid, selective, sensitive and cheap clinical evaluation of CDI at the point of care (POC) with minimal sample backlog. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00032654
Volume :
146
Issue :
14
Database :
Academic Search Index
Journal :
Analyst
Publication Type :
Academic Journal
Accession number :
151363492
Full Text :
https://doi.org/10.1039/d1an00726b