Surface-enhanced Raman spectroscopy (SERS) has enormous potential for a range of applications where high sensitivity needs to be combined with good discrimination between molecular targets. However, the SERS technique has trouble finding its industrial development, as was the case with the surface plasmon resonance technology. The main reason is the difficulty to produce stable, reproducible, and highly efficient substrates for quantitative measurements. In this paper, we report a method to obtain two-dimensional regular nanopatterns of gold nanoparticles (AuNPs). The resulting patterns were evaluated by SERS. Our bottom-up strategy was divided into two steps: (a) nanopatterning of the substrate by e-beam lithography and (b) electrostatic adsorption of AuNPs on functionalized substrates. This approach enabled us to highlight the optimal conditions to obtain monolayer, rows, or ring of AuNPs, with homogeneous distribution and high density (800 AuNPs/μm 2 ). The nanostructure distributions on the substrates were displayed by scanning electron microscopy and atomic force microscopy images. Optical properties of our nanostructures were characterized by visible extinction spectra and by the measured enhancements of Raman scattering. Finally, we tried to demonstrate experimentally that, to observe a significant enhancement of SERS, the gold diffusers must be extremely closer. If electron beam lithography is a very attractive technique to perform reproducible SERS substrates, the realization of pattern needs a very high resolution, with distances between nanostructures probably of less than 20 nm.

A glyco-array platform has been developed, in which glycans are attached to plasmonic nanoparticles through strain-promoted azide-alkyne cycloaddition. Glycan–protein binding events can then be detected in a label-free manner employing surface-enhanced Raman spectroscopy (SERS). As proof of concept, we have analyzed the binding of Gal1, Gal3, and influenza hemagglutinins (HAs) to various glycans and demonstrated that binding partners can be identified with high confidence. The attraction of SERS for optical sensing is that it can provide unique spectral signatures for glycan–protein complexes, confirm identity through statistical validation, and minimizes false positive results common to indirect methods. Furthermore, SERS is very sensitive and has multiplexing capabilities thereby allowing the simultaneous detection of multiple analytes.

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.