Optical sensor for the analysis of biomarkers

Al-Shemary, Rana S.

Abstract

The continuous measurement of biomarkers such as proteins and hormones in blood and serum are the cornerstone of healthcare monitoring and optimising treatment plans. Often such biomarkers are present in minute concentrations. Conventional analysis methods involve assays with costly labelled antibodies and lengthy protocols on microtitre well plates. For example fluorescence detection is often favoured for antibody based assays as it is very sensitive, however considerable sample clean-up is required (they will contains interfering materials such as other proteins, salts, etc.) and a protocol is required that requires fluorescence labels. Label-free biosensors are therefore an attractive alternative to improve biomarker analysis. In this work, a label-free optical biosensor was developed based on dye-doped leaky waveguides (DDLW). Chitosan was selected as the porous waveguide material because it has a large surface area, offering the probability of immobilizing a large number of biomolecules, which significantly increases the possibility of capturing target species.

The DDLW biosensor consisted of a 1% of chitosan layer, deposited by spin coating onto a glass substrate from a solution of 0.1 M acetic acid. Reactive Blue 4 dye 100 mM was then incorporated in the chitosan waveguide via reaction with the free amines of the chitosan. The chitosan waveguide on the glass substrate was then sealed within a microfluidics flow cell to allow controlled flushing with reagents. The Reactive Blue 4 dye absorbs light most strongly at the resonance angle and thus generates a dip in reflectivity at that angle which could be used to make measurement.

The DDLW sensing mechanism is based on shifts in resonance angle as a result of changes in refractive index in the waveguide, which may change when molecules bind to antibodies immobilised in the waveguide. The surface flatness and thickness of the spin-coated chitosan layer was characterised via white light interferometer and a Dektak profiler. The porosity of the waveguide layer is the main factor determining the maximum number of antibodies that can be immobilized in its entire volume. The pore size of the waveguide film needed to be large enough to allow the antibodies to enter the waveguide layer. The pore size of the waveguide layer was tailored using different methodologies. Silica nanoparticles were investigated as well as porogens, but the chemicals required to dissolve the silica nanoparticles and the porogens used were found to damage the chitosan waveguide. Hence, we have developed a facile method of tailoring the porosity of the waveguide layer by controlling a dry time of chitosan to some extend that can provide a waveguide mode with an enhancement in the porosity of has achieved and shown fascinating results where the pore size was being large enough to different molecular weight of biomolecules. Where we optimised the drying time following different concentrations of chitosan film fabrication by spin coating. The detection method was initially tested using the binding of rabbit IgG to anti-rabbit IgG as a model example. Initially, carboxylic group of the antibody can bind to the amino groups of chitosan film through an activation process using different ratio of EDC-Sulfo-NHS linkage chemistry; the efficiency of immobilisation was investigated with (confocal) fluorescence microscopy, but it was found that the amount of attachment was insufficient. To increase the sensitivity an alternative approach was used in which the antibodies were immobilised into the waveguide layer by first attaching Streptavidin to the amine groups of the chitosan waveguide using glutaraldehyde. Biotinylated anti-rabbit IgG was then immobilised by binding between streptavidin and biotin. Finally, the binding of rabbit-IgG and the immobilised anti-rabbit IgG was studied.

Once the immobilisation chemistry is successfully developed, the waveguide system was intended and used to measure the release of tissue factor (TF) that expressed on pancreatic cell lines. Two types of pancreatic cells (Aspc-1 and Miapaca-2 cells) were used to investigate the concentration of TF in a real-time. There were a strong correlations between the level of TF antigen and activity in these cells, showing that the TF present on the cancer cells was active. The calibration curve of Tissue factor (TF) has been created by preparing a range of TF standard solutions (7.825, 15.65, 31.3, 62.5, 125, 250 pg mL-1) and recorded by ELISA plate reader at 450 nm to inspect the concentration of TF that expressed from unknown sample solutions. The highest expression of tissue factor (107.82 pg mL-1) was recorded on the unknown sample solution of Aspc-1, whereas, TF concentration was very low (57.27 pg mL-1) on unknown sample solution of Miapaca-2 cell lines. A leaky waveguide system was also used to investigate the lowest concentration of TF that released from pancreatic cell lines (7.86±1 pg mL-1) that was expressed on unknown sample solution of Aspc-1 cell lines and (6.89±1 pg mL-1) on unknown sample solution of Miapaca-2 cell lines. Different concentrations of TF standard solutions (0.003, 0.03, 0.3, 3, and 30 pg mL-1) were applied using dye-doped leaky waveguide sensor. Then, the results were compared to the conventional microwell-based ELISA system. In conclusion, when applying lowest concentrations of TF that were expressed on unknown sample solutions of (Aspc-1 and Miapaca-2 cell lines) the degrees of shifting in angles were very close to the shifting in angles of standard solutions TF that were prepared to investigate the detection limit.

Citation

Al-Shemary, R. S. (2018). Optical sensor for the analysis of biomarkers. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4220996

• Thesis Type: Thesis
• Deposit Date: Nov 12, 2018
• Publicly Available Date: Feb 23, 2023
• Keywords: Chemistry
• Public URL: https://hull-repository.worktribe.com/output/4220996
• Additional Information: Department of Chemistry, The University of Hull
• Award Date: Jan 1, 2018