Viral Immunology: Tripp Laboratory
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Virus/Pathogen Biosensing
Surface-Enhanced Raman Spectroscopy (SERS): Spectroscopic-Based Detection of Viruses and Pathogens
Overview
There is a critical need for a rapid and sensitive method to detect viral infections that inflict significant disease burdens and to detect those that may be associated with bioterrorism. Current detection assays are limited in sensitivity, have a poor predictive value, and are time-consuming. Development of bioanalytical methods that can rapidly, accurately and cost-effectively detect very low levels of virus are urgently needed to accelerate disease intervention strategies that protect public health.
Surface-enhanced Raman spectroscopy (SERS) has emerged as a superior biomedical sensing method that can be readily applied to detection of a variety of pathogens, particularly viruses. The trace analytical capability of SERS has reached the single molecule level, thus detection of pathogens does not require amplification steps that are currently associated with other detection methods such as polymerase chain reaction (PCR) methods.
Our research group has demonstrated that a novel nanofabrication technique based on glancing angle vapor deposition produces nanorod substrates that exhibit extremely high SERS activity. This method offers several strategic advantages for nanofabrication that enhance SERS biomedical sensing. Using these nanorod substrates, we have shown that SERS can not only easily distinguish between virus types, but even among individual virus strains in different biological media.
Preliminary Experiments
Our preliminary virology experiments with these novel SERS substrates were designed to answer four questions: i) Can SERS detect surface-bound viruses? ii) Can SERS distinguish between different viruses based on their Raman spectra? iii) Can SERS detect viruses in biological media, and iv) Can SERS differentiate between different strains of the same virus?
The summary of the results of these experiments are presented below.
Our experiments have shown that novel SERS substrates composed of nanorods can be used to detect binding of virus supported by antibodies (Fig.1).
Using the self-assembled, multilayer nanorod-antibody SERS substrate used to detect virus in Figure 1, we show that we can SERS can be used to differentiate between different viruses, including both RNA and DNA viruses. The baseline corrected enhanced Raman spectra of adenovirus, rhinovirus and HIV viruses are shown in Fig. 2.
We have demonstrated that nanorod-based SERS is able to sense the presence of virus in biological media. To demonstrate this, we compared the SERS spectra of uninfected Vero cell lysate, RSV-infected cell lysate and purified RSV (Fig. 3). The results show that major Raman bands can be assigned to different constituents of the cell lysate and the virus, such as nucleic acids, proteins, protein secondary structure units and amino acid residues present in the side chains and the backbone. However, our most significant result was that vibrational modes due to the virus could be unambiguously identified in the SERS spectrum of the Vero cell lysate after infection.
The influenza viruses, HKX-31(H3N2), A/WSN/33 (H1N1) and A/PR/8/34 (H1N1) belonging to the strain A, were analyzed using SERS and the corresponding baseline corrected spectra (1300 cm-1 - 500 cm-1) are shown in Fig. 4. Influenza A is an enveloped virus with two distinct glycoproteins on the surface (i.e. hemagglutinin and neuraminidase) that are embedded in a lipid bilayer. These two proteins are entirely responsible for the all the different subtypes of influenza, with 14 different known hemagglutinins and 9 different known neuraminidases. As can be seen in Fig. 4, the SERS spectra of the three strains within a single virus type are more similar than are the SERS spectra between virus types.
Although the influenza virus Raman spectra for the strains appear similar in Fig. 4, small differences are also apparent that may allow us to identify individual flu strains in a complex mixture. For example, the spectral regions between 900 - 700 cm-1 (highlighted in yellow in Fig. 4) show intensity differences as well as frequency shifts in the spectra of the three strains.
Preliminary Results Summary
We have developed an easily implemented nanofabrication method using glancing angle vapor deposition that produces surfaces uniformly covered with Ag nanorod arrays. These substrates consist of randomly nucleated Ag nanorods of a length defined by the experimenter. We have evaluated these nanorod arrays and showed that Ag nanorods exhibit extremely high SERS activity, with SERS enhancement factors routinely approaching 10^9 - 10^10.
We have evaluated these highly sensitive SERS substrates as bioanalytical sensors for virus detection. The results obtained show that we can easily detect viruses bound to the surface of these Ag nanorods. In addition, experiments on various virus samples show that Raman spectra of viruses can be used to rapidly and readily distinguish between different viruses, virus strains, and can serve as molecular fingerprints for classification and identification of viruses. Importantly, using these Ag nanorod arrays, SERS can detect even minor spectral differences within strains of single virus type, suggesting that this technology can be applied to detect genetically modified viruses that may be agents of bioterrorism.
Our preliminary experiments indicate that it possible to use SERS to collect spectra of RNA and DNA viruses and potentially other pathogens such as bacteria to develop reference libraries of vibrational Raman fingerprints that can be used to identify these pathogens with a high degree of specificity. Compared to the current state-of-the-art in virus detection, the SERS results presented here have the following advantages: i) observation of virus spectra using extremely small amounts of analyte without biochemical amplification, and ii) acquisition of the virus spectra in an extremely short time frame. The speed, specificity and relative ease of implementation of the SERS technique make it a highly promising alternative to current viral diagnostic tools and methodologies, and offer the possibility of designing new virus detection schemes.
This page last updated May 14, 2010 .
