Simultaneous detection and quantification of three bacterial meningitis pathogens by SERS

Gracie, Kirsten and Correa, Elon and Mabbott, Samuel and Dougan, Jennifer A. and Graham, Duncan and Goodacre, Royston and Faulds, Karen (2014) Simultaneous detection and quantification of three bacterial meningitis pathogens by SERS. Chemical Science, 5 (3). pp. 1030-1040. ISSN 2041-6520

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Abstract

Bacterial meningitis is well known for its rapid onset and high mortality rates, therefore rapid detection of bacteria found in cerebral spinal fluid (CSF) and subsequent effective treatment is crucial. A new quantitative assay for detection of three pathogens that result in bacterial meningitis using a combination of lambda exonuclease (λ-exonuclease) and surface enhanced Raman scattering (SERS) is reported. SERS challenges current fluorescent-based detection methods in terms of both sensitivity and more importantly the detection of multiple components in a mixture, which is becoming increasingly more desirable for clinical diagnostics. λ-Exonuclease is a processive enzyme that digests one strand of double stranded DNA bearing a terminal 5′-phosphate group. The new assay format involves the simultaneous hybridisation of two complementary DNA probes (one containing a SERS active dye) to a target sequence followed by λ-exonuclease digestion of double stranded DNA and SERS detection of the digestion product. Three meningitis pathogens were successfully quantified in a multiplexed test with calculated limits of detection in the pico-molar range, eliminating the need for time consuming culture based methods that are currently used for analysis. Quantification of each individual pathogen in a mixture using SERS is complex, however, this is the first report that this is possible using the unique spectral features of the SERS signals combined with partial least squares (PLS) regression. This is a powerful demonstration of the ability of this SERS assay to be used for analysis of clinically relevant targets with significant advantages over existing approaches and offers the opportunity for future deployment in healthcare applications.