Elucidation of the molecular conformation of a biomolecule is an important step in determining the relationship between physical structure and biochemical function. In addition, the elucidation of conformational structure for biological macromolecules invariably leads to insights concerning the function of the molecule. Techniques such as X-ray diffraction, nuclear magnetic resonance (NMR), electron spin resonance (ESR), fluorescence, infrared, and Raman have been used to obtain detailed information about biomolecular structure. This chapter describes the use of Fourier transform infrared spectrometer (FT-IR) to monitor the molecular conformation of model membranes. It mentions two advantages of FT-IR spectrometers. First, while the fact that high signal-to-noise ratio (SNRs) can readily be achieved is well known, the precision of the frequency scale is not so widely recognized. Second, the instruments are extremely versatile. Spectral ranges from the near ultraviolet (UV) to the far infrared can be selected by changing the source, detector, and beam splitter.

A method to acquire the Raman spectra of sub-surface components using diffusely focused radiation in a microscope sampling configuration is described. This procedure generates Raman scattering at various sample depths by producing a converging beam at the back aperture of the objective lens. This method requires illumination of the sample with a defocused laser, while simultaneously increasing the number of CCD pixels that are binned along the spatial axis of the detector. We applied this diffuse sampling method to the analysis of stored red blood cells (RBCs). During storage, biochemical changes to RBCs occur (the "storage lesion"). However, there are no existing non-invasive methods to assess this. We evaluated the instrumental parameters needed to maximize the diffusely scattered signal, including pixel binning, slit width, and bandwidth. We demonstrated the effectiveness of this diffuse resonance Raman spectroscopy (DRRS) method by detecting RBCs through a blood bag segment (1 mm wall thickness). We directly compared the DRRS method to the more common stand-off Raman spectroscopy (SORS) method using both 633 nm and 785 nm excitation. Time-dependent DRRS spectra were used in a multivariate model for classification of RBCs in polymer segments by storage age. Young (6-8 day) RBCs were differentiated from old (35-40) RBCs with 100% sensitivity and 98.5% selectivity. These data indicated that DRRS is a promising, non-invasive technique for acquiring the spectra of sub-surface components, and is particularly applicable when the underlying sample can be resonantly enhanced.

Colloidal silver (Ag) nanoparticles (AgNP) have been widely used for surface-enhanced Raman spectroscopy (SERS) applications. We report a simple, rapid and effective method to prepare AgNP colloids for SERS using the classic organic chemistry Ag mirror reaction with Tollens' reagent. The AgNP colloid prepared with this process was characterized using SEM, and the reaction conditions further optimized using SERS measurements. It was found that Ag mirror reaction conditions that included 20 mM AgNO3, 5 min reaction time, and 0.5 M glucose produced AgNP colloids with an average size of 319.1 nm (s.d +/- 128.1). These AgNP colloids exhibited a significant SERS response when adenine was used as the reporter molecule. The usefulness of these new AgNP colloids was demonstrated by detecting the nucleotides adenosine 5'-mono-phosphate (AMP), guanosine 5'monophosphate (GMP), cytidine 5'-monophosphate (CMP), and uridine 5'-monophosphate (UMP). A detection limit of 500 nM for AMP was achieved with the as-prepared AgNP colloid. The bacterium Mycoplasma pneumoniae was also easily detected in laboratory culture with these SERS substrates. These findings attest to the applicability of this AgNP colloid for the sensitive and specific detection of both small biomolecules and microorganisms.

Mycoplasma pneumoniae is a human respiratory tract pathogen causing chronic bronchitis and atypical or walking pneumonia. The major surface protein P1 must form complexes with proteins P30 and P40/P90 to function in receptor binding and gliding motility, and variability in P1 and P40/P90 distinguishes the two major M. pneumoniae genotypes. Strains belonging to each genotype can be differentiated with high sensitivity and specificity by utilizing surface-enhanced Raman spectroscopy on silver nanorod arrays. Here, we used the variable selection method of variable importance in projection (VIP) to identify Raman bands important in M. pneumoniae strain classification. Furthermore, VIP analysis of mutants lacking P40/P90, or P1 and P40/P90, correlated certain Raman bands important in distinguishing genotypes, with specific mycoplasma surface protein composition and presentation. Variable selection, and its correlation with specific mycoplasma surface components, is an important next step in developing this platform for M. pneumoniae detection and genotyping.

Duncan Graham opened a general discussion of the paper by Pavel Matousek: Regarding the temperature measurements, how did you do them? Stokes/anti-Stokes measurement? The values are not what I'd expect and seem lower. What was the wavelength and power of the laser and did you calculate what you thought the temperature measurement should be based on that? How far does the temperature gradient reach? The scattering absorption ration changes with nanoparticle size and composition. Have you thought about using more highly absorbing nanoparticles such as hollow gold nanoshells?

Royston Goodacre, Matthew J. Baker, Duncan Graham, Zachary D. Schultz, Max Diem, Maria Paula Marques, Gianfelice Cinque, Robbin Vernooij, Josep Sulé-Suso, Hugh J. Byrne, Karen Faulds, Michael Hermes, Holly Fleming, Alois Bonifacio, Richard Dluhy, Peter Gardner, Samir El-Mashtoly, Bayden Wood, Kathleen Gough, Stefano Fornasaro, Sergei Kazarian, Lauren Jamieson, Wolfgang Petrich, Ganesh Dhruvananda Sockalingum, Nick Stone, Catherine Kendall, Faris Sinjab, Parvez Haris, Abdu Subaihi, Stan Remiszewski, Petra Hellwig, Valter Sergo, Klaus Gerwert, Christopher Phillips and Colin J. Campbell

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.

Parvez Haris opened the discussion of the introductory lecture by Max Diem: Varying degrees of accuracy have been obtained for discrimination between cancerous and non-cancerous tissues using vibrational spectroscopic methods. What are the explanations for these variations in accuracy between c

The first page of this article is displayed as the abstract.

Mycoplasma pneumoniae is a cell wall-less bacterial pathogen of the human respiratory tract that accounts for > 20% of all community-acquired pneumonia (CAP). At present the most effective means for detection and strain-typing is quantitative polymerase chain reaction (qPCR), which can exhibit excellent sensitivity and specificity but requires separate tests for detection and genotyping, lacks standardization between available tests and between labs, and has limited practicality for widespread, point-of-care use. We have developed and previously described a silver nanorod array-surface enhanced Raman Spectroscopy (NA-SERS) biosensing platform capable of detecting M. pneumoniae with statistically significant specificity and sensitivity in simulated and true clinical throat swab samples, and the ability to distinguish between reference strains of the two main genotypes of M. pneumoniae. Furthermore, we have established a qualitative lower endpoint of detection for NA-SERS of < 1 genome equivalent (cell/μl) and a quantitative multivariate detection limit of 5.3 ± 1 cells/μl. Here we demonstrate using partial least squares- discriminatory analysis (PLS-DA) of sample spectra that NA-SERS correctly identified M. pneumoniae clinical isolates from globally diverse origins and distinguished these from a panel of 12 other human commensal and pathogenic mycoplasma species with 100% cross-validated statistical accuracy. Furthermore, PLS-DA correctly classified by strain type all 30 clinical isolates with 96% cross-validated accuracy for type 1 strains, 98% cross-validated accuracy for type 2 strains, and 90% cross-validated accuracy for type 2V strains.

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.

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.

Mycoplasma pneumoniae is a cell wall-less bacterial pathogen of the human respiratory tract that accounts for up to 20% of community-acquired pneumonia. At present, the standard for detection and genotyping is quantitative polymerase chain reaction (qPCR), which can exhibit excellent sensitivity but lacks standardization and has limited practicality for widespread, point-of-care use. We previously described a Ag nanorod array-surface enhanced Raman spectroscopy (NA-SERS) biosensing platform capable of detecting M. pneumoniae in simulated and true clinical throat swab samples with statistically significant specificity and sensitivity. We report here that differences in sample preparation influence the integrity of mycoplasma cells for NA-SERS analysis, which in turn impacts the resulting spectra. We have established a multivariate detection limit (MDL) using NA-SERS for M. pneumoniae intact-cell sample preparations. Using an adaptation of International Union of Pure and Applied Chemistry (IUPAC)-recommended methods for analyzing multivariate data sets, we found that qPCR had roughly 10× better detection limits than NA-SERS when expressed in CFU ml(-1) and DNA concentration (fg). However, the NA-SERS MDL for intact M. pneumoniae was 5.3 ± 1.0 genome equivalents (cells per μl). By comparison, qPCR of a parallel set of samples yielded a limit of detection of 2.5 ± 0.25 cells per μl. Therefore, for certain standard metrics NA-SERS provides a multivariate detection limit for M. pneumoniae that is essentially identical to that determined via qPCR.

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.

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.

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 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.

Recent progress in substrate nanofabrication has led to the development of Ag nanorod arrays as uniform, reproducible, large area SERS-active substrates with high signal enhancement. These novel nanostructures fabricated by oblique angle vapor deposition (OAD) offer a robust platform for the rapid detection of biological agents and open new perspectives for the development and integration of biomedical diagnostic for clinical and therapeutic applications. Ag nanorod arrays have been investigated as SERS-active substrates for the detection and identification of pathogens, including bacteria and viruses, as well as to evaluate the potential of this biosensing platform for bio-recognition of high affinity events using oligonucleotide-modified substrates. This review summarizes the various nanostructured substrates designed for SERS-based applications, highlights the nanofabrication methodology used to produce Ag nanorod arrays, outlines their morphological and physical properties, and provides a summary of the most recent uses of these substrates for clinical diagnostic and biomedical applications.