Jeremy D. Driskell

Rapid microRNA (miRNA) detection and classification via surface-enhanced Raman spectroscopy (SERS)

microRNAs (miRNA) are recognized as regulators of gene expression during development and cell differentiation as well as biomarkers of disease. Development of rapid and sensitive miRNA profiling methods is essential for evaluating the pattern of miRNA expression that varies across normal and diseased states. The ability to identify miRNA expression patterns is limited to cumbersome assays that often lack sensitivity and specificity to distinguish between different miRNA families and members. We evaluated a surface-enhanced Raman scattering (SERS) platform for detection and classification of miRNAs. The strength of the SERS-based sensor is its sensitivity to detect extremely low levels of analyte and specificity to provide the molecular fingerprint of the analyte. We show that the SERS spectra of related and unrelated miRNAs can be detected in near-real time, that detection is sequence dependent, and that SERS spectra can be used to classify miRNA patterns with high accuracy.

One-step assay for detecting influenza virus using dynamic light scattering and gold nanoparticles

Herein we detail the development of a simple, rapid, and sensitive method for quantitative detection of influenza A virus using dynamic light scattering (DLS) and gold nanoparticle (AuNP) labels. Influenza-specific antibodies are conjugated to AuNPs, and aggregation of the AuNP probes is induced upon addition of the target virus. DLS is used to measure the extent of aggregation and the mean hydrodynamic diameter is correlated to virus concentration. The effects of nanoparticle concentration and size on the analytical performance of the assay were systematically investigated. It was determined that decreasing the AuNP probe concentration improves the detection limit while the effect of changing the AuNP size is minimal. Optimization of the assay provided a detection limit of <100 TCID(50)/mL which is 1-2 orders of magnitude improved over commercial diagnostic kits without increasing the assay time or complexity. Additionally, this assay was demonstrated to perform equivalently for influenza virus prepared in different biological matrices.

Identification and classification of respiratory syncytial virus (RSV) strains by surface-enhanced Raman spectroscopy and multivariate statistical techniques

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.

Label-free SERS detection of microRNA based on affinity for an unmodified silver nanorod array substrate

A silver nanorod array has been used for the intrinsic SERS detection of microRNAs based on different binding affinities of ssRNA, thiolated ssDNA, and the RNA:DNA duplex, eliminating the need for a labelling step.

Spectroscopic Analysis of Metal Ion Binding in Spiropyran Containing Copolymer Thin Films

In this article, we describe the synthesis and characterization of a series of spiropyran containing copolymers that were used as colorimetric sensors for a series of divalent metal ions. The composition of spiropyran contained in the polymer backbone was varied from 10-100 mol % to investigate the influence of free volume and sterics on the photochromic response. Fourier transform-infrared (FT-IR) spectroscopy was used to characterize the photoinduced conversion, as well as the merocyanine-metal ion (MC-M(2+)) interaction. FT-IR spectra were analyzed using chemometric methods to elucidate the chemical binding environment between MC and M(2+) and to selectively identify different metal ions bound to MC. By means of UV-vis absorption spectroscopy, we also demonstrate that each metal ion gives rise to a unique colorimetric response that is dependent on the amount of spiropyran comonomer contained in the polymer backbone and that by increasing the concentration of chromophore in the copolymer, the selectivity between different metal ions decreases. With the use of chemometric methods, UV-vis spectra can be analyzed to quantitatively identify metal ions in a concentration range from 1 microM to 100 mM.

Fabrication and characterization of a multiwell array SERS chip with biological applications.

Uniform, large surface area substrates for surface-enhanced Raman spectroscopy (SERS) are fabricated by oblique angle deposition. The SERS-active substrates are patterned by a polymer-molding technique to provide a uniform array for high throughput biosensing and multiplexing. Using a conventional SERS-active molecule, 1,2-di(4-pyridyl)ethylene (BPE) >or=98%, we show that this device provides a uniform Raman signal enhancement from well to well with a detection limit of at least 10(-8)M of the BPE solution or 10(-18)mol of BPE. The SERS intensity is also demonstrated to vary logarithmically with the log of BPE concentration and the apparent sensitivity of the patterned substrate is compared to previous reports from our group on non-patterned substrates. Avian influenza is analyzed to demonstrate the utility of SERS multiwell patterned substrates for biosensing. The spectra acquired from patterned substrates show better reproducibility and less variation compared to the unpatterned substrates according to multivariate analysis. Our results highlight potential advantages of the patterned substrate.

Label-Free Detection of Micro-RNA Hybridization Using Surface-Enhanced Raman Spectroscopy and Least-Squares Analysis

Label-free surface-enhanced Raman spectroscopy (SERS) detection of nucleic acid hybridization is impeded by poor spectral reproducibility and the fact that the chemical signatures of hybridized and unhybridized sequences are highly similar. To overcome these issues, highly reproducible silver nanorod SERS substrates along with a straightforward least-squares (LS) technique have been employed for the quantitative determination of the relative ratios of the four nucleotide components A, C, G, and T/U before and after hybridization using a clinically relevant micro-RNA sequence.

Rapid and Sensitive Detection of Rotavirus Molecular Signatures Using Surface Enhanced Raman Spectroscopy

Human enteric virus infections range from gastroenteritis to life threatening diseases such as myocarditis and aseptic meningitis. Rotavirus is one of the most common enteric agents and mortality associated with infection can be very significant in developing countries. Most enteric viruses produce diseases that are not distinct from other pathogens, and current diagnostics is limited in breadth and sensitivity required to advance virus detection schemes for disease intervention strategies. A spectroscopic assay based on surface enhanced Raman scattering (SERS) has been developed for rapid and sensitive detection of rotavirus. The SERS method relies on the fabrication of silver nanorod array substrates that are extremely SERS-active allowing for direct structural characterization of viruses. SERS spectra for eight rotavirus strains were analyzed to qualitatively identify rotaviruses and to classify each according to G and P genotype and strain with >96% accuracy, and a quantitative model based on partial least squares regression analysis was evaluated. This novel SERS-based virus detection method shows that SERS can be used to identify spectral fingerprints of human rotaviruses, and suggests that this detection method can be used for pathogen detection central to human health care.

Detection of Mycoplasma pneumoniae in simulated and true clinical throat swab specimens by nanorod array-surface-enhanced Raman spectroscopy.

The prokaryote Mycoplasma pneumoniae is a major cause of respiratory disease in humans, accounting for 20% of all community-acquired pneumonia and the leading cause of pneumonia in older children and young adults. The limitations of existing options for mycoplasma diagnosis highlight a critical need for a new detection platform with high sensitivity, specificity, and expediency. Here we evaluated silver nanorod arrays (NA) as a biosensing platform for detection and differentiation of M. pneumoniae in culture and in spiked and true clinical throat swab samples by surface-enhanced Raman spectroscopy (SERS). Three M. pneumoniae strains were reproducibly differentiated by NA-SERS with 95%–100% specificity and 94–100% sensitivity, and with a lower detection limit exceeding standard PCR. Analysis of throat swab samples spiked with M. pneumoniae yielded detection in a complex, clinically relevant background with >90% accuracy and high sensitivity. In addition, NA-SERS correctly classified with >97% accuracy, ten true clinical throat swab samples previously established by real-time PCR and culture to be positive or negative for M. pneumoniae. Our findings suggest that the unique biochemical specificity of Raman spectroscopy, combined with reproducible spectral enhancement by silver NA, holds great promise as a superior platform for rapid and sensitive detection and identification of M. pneumoniae, with potential for point-of-care application.

Quantitative Surface-Enhanced Raman Spectroscopy Based Analysis of MicroRNA Mixtures

We have developed a rapid, sensitive, and quantitative method for identification of microRNA (miRNA) sequences in multicomponent mixtures using surface-enhanced Raman spectroscopy (SERS). The method uses Ag nanorod array substrates prepared by oblique angle vapor deposition as the SERS platform. We show that Ag nanorod-based SERS spectra are uniquely characteristic for each miRNA sequence studied, and that the spectral reproducibility is sufficient for quantitative analysis of miRNA profiles in multicomponent mixtures using partial least squares (PLS) regression analysis. This method was applied to individual sample mixtures consisting of two, three, and five miRNAs. Separate PLS models were generated for the two-, three-, and five-component mixtures from >150 calibration spectra covering a concentration range of 6 to 150 μM for each miRNA. The PLS models were externally validated with independent test samples resulting in root mean square errors of prediction (RMSEP) of 7.4, <7.4, and <10 μM for the two-, three-, and five-component models, respectively. These results demonstrate the applicability of SERS for quantitative detection and profiling of miRNAs and suggest that SERS may prove to be a novel, label-free method for identification of disease biomarkers.