In the past decade, the field of biosensing has experienced an incredible pace of development, due to the compelling need for accurate and reliable tools for characterization of biomolecular kinetics. Specifically, label-free kinetic measurements are the most direct method for studying molecular binding, for example to establish the efficacy of drug-receptor interactions. For this reason, researchers in the pharmaceutical industry rely heavily on label-free detection for drug and antibody screening. Meanwhile, in the biosafety industry and healthcare, there is great demand for screening tools that can target biothreats, in order to accurately recognize the presence of toxins and pathogens with high sensitivity in diverse samples, such as bodily fluids, food and drinking water. This research topic has become particularly relevant during the recent pandemic, where vaccine development was carried out side by side with quantification and characterization of single viral particles. Here, we introduce a versatile biosensing platform capable of characterizing virtually any type of target compound, down to the single molecule level. For this work, we have improved the Interferometric Reflectance Imaging Sensor (IRIS) to perform accurate measurements of the binding kinetics of analytes ranging in molecular weight from less than 1kDa (small molecules) to more than 1MDa (biological nanoparticles). For the first time, we demonstrate multiplexed kinetic binding characterization of small molecules to surface immobilized antibody probes, as well as detection and phenotyping of large and complex analytes, on the same platform.
The IRIS platform utilizes the optical interference signal produced by thinly layered substrates in order to precisely measure the thickness of a transparent film atop a silicon chip. In the context of this work, dynamic characterization of a wide range of biomolecular and nanoparticle targets was made possible by a multidimensional optimization, in order to improve both the sensitivity and the dynamic range of the instrument. Analysis of low molecular weight compounds required a significant increase in signal to noise ratio, which was achieved through averaging, as well as complete elimination of background solution effects ('bulk effect’). Additionally, the best surface chemistry for each application was identified by a new technique which consists of immobilizing capture probes on a multiplexed array of active polymers functionalized on the same sensor surface, allowing for simultaneous side-by-side comparison of their performance. Surface chemistry plays a huge role in kinetic measurements, in terms of probe functionality, steric hindrance, charge distribution and diffusion effects.
Finally, imaging optics, illumination wavelength, and thickness of the silicon dioxide film were optimized to perform detection and phenotyping of large analytes, such as extracellular vesicles (EVs) and antibody-conjugated gold nanoparticles (mAb-GNPs). Results obtained from numerical simulations allowed for selection of the best experimental parameters for each application. Experimentally, mAb-GNPs were utilized to produce a real-time sandwich lateral flow assay. In this context, we demonstrated how the improved IRIS platform can bridge the gap between single-particle detection ('digital’ configuration) and bulk reflectance measurements ('analog’ configuration), creating a new 'hybrid' system (h-IRIS), which only requires minimal hardware adjustments to easily switch from one modality to the other. This brought a substantial improvement in sensitivity, improving the limit of detection by three orders of magnitude and enabling single-molecule level measurements. Finally, future system optimization ideas are presented to achieve even higher accuracy and further extend the range of target analytes.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/44707 |
| Date | 23 May 2022 |
| Creators | Chiodi, Elisa |
| Contributors | Ünlü, M. Selim |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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