Oligonucleotides corresponding to neuraminidase (NA) stalk motifs that have been associated with enhanced influenza virulence have been identified using surface-enhanced Raman spectroscopy (SERS). 5′-Thiolated ssDNA oligonucleotides were immobilized onto a hexadecyltrimethylammonium bromide (CTAB) coated Au nanoparticles (AuNP). Three synthetic RNA sequences corresponding to specific amino acid deletions in the influenza NA stalk region were attached to the CTAB-modified AuNPs. Two of these sequences were specific to sequences with amino acid deletions associated with increased virulence, and one was a low virulence sequence with no amino acid deletions. Hybridization of synthetic matched and mismatched DNA–RNA complexes were detected based on the intrinsic SERS spectra. In addition, this platform was used to analyze RNA sequences isolated from laboratory grown influenza viruses having the NA stalk motif associated with enhanced virulence, including A/WSN/33/H1N1, A/Anhui/1/2005/H5N, and A/Vietnam/1203/2004/H5N1 strains. Multivariate feature selection methods were employed to determine the specific wavenumbers in the Raman spectra that contributed the most information for class discrimination. A one-way analysis of variance (ANOVA) test identified 884 and 1196 wavenumbers as being highly significant in the high and low virulence spectra, respectively (p < 0.01). A post-hoc Tukey Honestly Significance Difference (HSD) test identified the wavenumbers that played a major role in differentiating the DNA–RNA hybrid classes. An estimate of the spectral variability, based on the Wilcoxon rank sum test, found the major source of variation to be predominately between the different classes, and not within the classes, thus confirming that the spectra reflected real class differences and not sampling artifacts. The multivariate classification methods partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA) were able to distinguish between different NA stalk-motifs linked to NA-enhanced influenza virus virulence (NA-EIV) with >95% sensitivity and specificity in both synthetic RNA sequences as well as the isolated viral RNA. This study demonstrates the feasibility of SERS for direct identification of influenza NA stalk mutations associated with virulence without sample amplification or labeling.

To date there is no rapid method to screen for highly pathogenic avian influenza strains that may be indicators of future pandemics. We report here the first development of an oligonucleotide-based spectroscopic assay to rapidly and sensitively detect a N66S mutation in the gene coding for the PB1-F2 protein associated with increased virulence in highly pathogenic pandemic influenza viruses. 5′-Thiolated ssDNA oligonucleotides were employed as probes to capture RNA isolated from six influenza viruses, three having N66S mutations, two without the N66S mutation, and one deletion mutant not encoding the PB1-F2 protein. Hybridization was detected without amplification or labeling using the intrinsic surfaced-enhanced Raman spectrum of the DNA-RNA complex. Multivariate analysis identified target RNA binding from noncomplementary sequences with 100% sensitivity, 100% selectivity, and 100% correct classification in the test data set. These results establish that optical-based diagnostic methods are able to directly identify diagnostic indicators of virulence linked to highly pathogenic pandemic influenza viruses without amplification or labeling.

Mycoplasma pneumoniae is a major cause of respiratory disease in humans and accounts for as much as 20% of all community-acquired pneumonia. Existing mycoplasma diagnosis is primarily limited by the poor success rate at culturing the bacteria from clinical samples. There is a critical need to develop a new platform for mycoplasma detection that has high sensitivity, specificity, and expediency. Here we report the layer-by-layer (LBL) encapsulation of M. pneumoniae cells with Ag nanoparticles in a matrix of the polyelectrolytes poly(allylamine hydrochloride) (PAH) and poly(styrene sulfonate) (PSS). We evaluated nanoparticle encapsulated mycoplasma cells as a platform for the differentiation of M. pneumoniae strains using surface enhanced Raman scattering (SERS) combined with multivariate statistical analysis. Three separate M. pneumoniae strains (M129, FH and II-3) were studied. Scanning electron microscopy and fluorescence imaging showed that the Ag nanoparticles were incorporated between the oppositely charged polyelectrolyte layers. SERS spectra showed that LBL encapsulation provides excellent spectral reproducibility. Multivariate statistical analysis of the Raman spectra differentiated the three M. pneumoniae strains with 97–100% specificity and sensitivity, and low (0.1–0.4) root mean square error. These results indicated that nanoparticle and polyelectrolyte encapsulation of M. pneumoniae is a potentially powerful platform for rapid and sensitive SERS-based bacterial identification.

This report examines lipophilic extracts containing mycolic acids isolated from tuberculosis (MTB) and non-tuberculosis (NTM) mycobacterial strains using chromatography, mass spectrometry (MS), nuclear magnetic resonance (NMR), and Raman spectroscopy. Gas chromatography-MS was used to identify major fatty acid mycolate components, while proton NMR confirmed the presence of characteristic cis- and trans-cyclopropane rings within different mycolic acid sub-types. Surface-enhanced Raman (SERS) spectra were obtained from the mycolic acids extracted from the bacterial cell envelopes of the MTB or NTM mycobacterial species. The Raman spectral profiles were used to develop a classification method based on chemometrics for identification of the mycobacterial species. Multivariate statistical analysis methods, including principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least squares discriminant analysis (PLS-DA) of the SERS spectra enabled differentiation of NTM mycobacteria from one another with 100% accuracy. These methods are also sensitive enough to differentiate clinically-isolated MTB strains that differed only by the presence or absence of a single extracytoplasmic sigma factor with 83–100% sensitivity and 80–100% specificity. The current work is the first report on discrimination of mycobacteria strains based on the SERS spectra of the constituent mycolic acids in lipophilic extracts. These results suggest that SERS can be used as an accurate and sensitive method for species and strain discrimination in mycobacteria.

We have developed a method for the detection of genetic markers associated with high pathogenicity in influenza. The assay consists of an array of 5′-thiolated ssDNA oligonucleotides immobilized on the surface of a Ag nanorod substrate that serve as capture probes for the detection of synthetic RNA sequences coding for a genetic mutation in the influenza PB1-F2 protein. Hybridization of the DNA probes to their complementary RNA sequences was detected using surface-enhanced Raman spectroscopy (SERS). Multivariate statistical analysis was used to differentiate the spectra of the complementary DNA probe-RNA target hybrids from those of the non-complementary DNA probes containing a single base pair polymorphism. Hierarchical cluster analysis (HCA) was able to distinguish with 100% accuracy the spectra of the complementary DNA probe–RNA target from the spectra of the immobilized DNA probes alone, or the DNA probes incubated with non-complementary RNA sequences. Linearity of response and limits of sensitivity of the SERS-based assays were determined using a partial least squares (PLS) regression model; detection limits computed by PLS was determined to be ∼10 nM. The binding affinity of the DNA probes to their complementary RNA sequences was confirmed using enzyme-linked immunosorbent assay (ELISA); however, the detection limits observed using ELISA were approximately 10× higher (∼100 nM) than those determined by PLS analysis of the SERS spectra.

We have demonstrated label-free optical detection of viral nucleoprotein binding to a polyvalent anti-influenza aptamer by monitoring the surface-enhanced Raman (SERS) spectra of the aptamer-nucleoprotein complex. The SERS spectra demonstrated that selective binding of the aptamer-nucleoprotein complex could be differentiated from that of the aptamer alone based solely on the direct spectral signature for the aptamer-nucleoprotein complex. Multivariate statistical methods, including principal components analysis, hierarchical clustering, and partial least squares, were used to confirm statistically significant differences between the spectra of the aptamer-nucleoprotein complex and the spectra of the unbound aptamer. Two separate negative controls were used to evaluate the specificity of binding of the viral nucleoproteins to this aptamer. In both cases, no spectral changes were observed that showed protein binding to the control surfaces, indicating a high degree of specificity for the binding of influenza viral nucleoproteins only to the influenza-specific aptamer. Statistical analysis of the spectra supports this interpretation. AFM images demonstrate morphological changes consistent with formation of the influenza aptamer-nucleoprotein complex. These results provide the first evidence for the use of aptamer-modified SERS substrates as diagnostic tools for influenza virus detection in a complex biological matrix.

A highly sensitive surface-enhanced Raman (SERS)-based method for detection of influenza viral nucleoproteins is described. The intrinsic SERS spectrum of the aptamer–nucleoprotein complex provides direct evidence of binding between a polyvalent anti-influenza aptamer and the nucleoproteins of three influenza strains.

A spectroscopic assay based on surface-enhanced Raman spectroscopy (SERS) has been developed for rapid genotyping of the measles virus (MeV). Silver nanorods fabricated using an oblique angle vapor deposition method acted as the SERS-active substrate. The SERS spectra of four separate MeV genotypes, i.e. A, H1, D4 and D9, and two separate negative media control samples were analyzed using multivariate statistical methods. Principal components analysis (PCA) and hierarchical cluster analysis (HCA) successfully separated three of the four MeV genotypes studied. The MeV genotypes used in this study had >96% sequence similarity as monitored using the MeV hemagglutinin (H) gene, and the clustering seen in PCA and HCA mirrored this sequence diversity. For example, the MeV genotypes with the highest sequence diversity (∼3%, A and H1) were the most widely separated in the PCA scores plot and HCA dendogram. Conversely, the MeV genotypes with the lowest sequence diversity (∼0.5%, D4 and D9) could not be statistically differentiated. However, a supervised chemometric method, partial least squares-discriminant analysis (PLS-DA) was able to separate each of the four MeV strains, the two negative controls, and the background, with >90% sensitivity and >96% selectivity based solely on their inherent SERS spectra. These results demonstrate that SERS, in combination with multivariate statistical methods, is a highly sensitive and rapid viral identification and classification method that can be applied to MeV genotyping.

There is a critical need for a rapid and sensitive means of detecting viruses. Recent reports from our laboratory have shown that surface-enhanced Raman spectroscopy (SERS) can meet these needs. In this study, SERS was used to obtain the Raman spectra of respiratory syncytial virus (RSV) strains A/Long, B1, and A2. SERS-active substrates composed of silver nanorods were fabricated using an oblique angle vapor deposition method. The SERS spectra obtained for each virus were shown to posses a high degree of reproducibility. Based on their intrinsic SERS spectra, the four virus strains were readily detected and classified using the multivariate statistical methods principal component analysis (PCA) and hierarchical cluster analysis (HCA). The chemometric results show that PCA is able to separate the three virus strains unambiguously, whereas the HCA method was able to readily distinguish an A2 strain-related G gene mutant virus (ΔG) from the A2 strain. The results described here demonstrate that SERS, in combination with multivariate statistical methods, can be utilized as a highly sensitive and rapid viral identification and classification method.

A highly sensitive surface-enhanced Raman (SERS)-based method for detection of influenza viral nucleoproteins is described. The intrinsic SERS spectrum of the aptamer–nucleoprotein complex provides direct evidence of binding between a polyvalent anti-influenza aptamer and the nucleoproteins of three influenza strains.