Optical sensor systems that integrate Long-Period-Gratings (LPG) as the detection arm have been proven to be highly sensitive and reliable in many applications. With increasing public recognition of threats from bacteria-induced diseases and their potential outbreak among densely populated communities, an intrinsic, low-cost biosensor device that can perform quick and precise identification of the infection type is in high demand to respond to such challenging situations and control the damage those diseases could possibly cause.
This dissertation describes the development of a biosensor platform that utilizes polymer thin films, known as ionic self-assembled multilayer (ISAM) films, to be the sensitivity- enhancing medium between an LPG fiber and specific, recognition layer. With the aid of cross- linking reactions, monoclonal antibodies (IgG) or DNA probes are immobilized onto the surface of the ISAM-coated fiber, which form the core component of the biosensor.
By immersing such biosensor fiber into a sample suspension, the immobilized antibody molecules will bind the specific antigen and capture the target cells or cell fragments onto the surface of the fiber sensor, resulting in increasing the average thickness of the fiber cladding and changing the refractive index of the cladding. This change occurring at the surface of the fiber results in a decrease of optical power emerging from the LPG section of the fiber. By comparing the transmitted optical power before and after applying the sample suspension, we are able to determine whether or not certain bacterial species have attached to the surface of the fiber, and as a consequence, we are able to determine whether or not the solution contains the targeted bacteria.
This platform has the potential for detection of a wide range of bacteria types. In our study, we have primarily investigated the sensitivity and specificity of the biosensor to methicillin- resistant Staphlococcus aureus (MRSA). The data we obtained have shown a sensitive threshold at as low as 102 cfu/ml with pure culture samples. A typical MRSA antibody-based biosensor assay with MRSA sample at this concentration has shown optical power reduction of 21.78%. In a detailed study involving twenty-six bacterial strains possessing the PBP2a protein that enables antibiotic resistance and sixteen strains that do not, the biosensor system was able to correctly identify every sample in pure culture samples at concentration of 104 cfu/ml. Further studies have also been conducted on infected mouse tissues and clinical swab samples from human ears, noses, and skin, and in each case, the system was in full agreement with the results of standard culture tests. However, the system is not yet able to correctly distinguish MRSA and non-MRSA infections in clinical swab samples taken from infected patient wounds. It is proposed that nonspecific binding due to insufficient blocking methods is the key issue.
Other bacterial strains, such as Brucella and Francisella tularensis have also been studied using a similar biosensor platform with DNA probes and antibodies, respectively, and the outcomes are also promising. The Brucella DNA biosensor is able to reflect the existence of 3 Brucella strains at 100 cfu/ml with an average of 12.2% signal reduction, while negative control samples at 106cfu/ml generate an average signal reduction of -2.1%. Similarly, the F. tularensis antibodies biosensor has shown a 25.6% signal reduction to LVS strain samples at 100 cfu/ml, while for negative control samples at the same concentration, it only produces a signal reduction of 0.05%. In general, this biosensor platform has demonstrated the potential of detecting a wide range of bacteria in a rapid and relatively inexpensive manner. / Ph. D.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/54590 |
| Date | 29 January 2014 |
| Creators | Zuo, Ziwei |
| Contributors | Physics, Heflin, James R., Heremans, Jean J., Inzana, Thomas J., Robinson, Hans D. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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