Novel affinity reagents and sensing methods for point-of-care biosensing
Abstract/Contents
- Abstract
- Recent advances in our understanding of the biology of health and disease have led to the rise of precision medicine -- the belief that by gaining deeper knowledge of the genomic, molecular, and cellular information of an individual patient it is possible to deliver more personalized treatments. Increased desire for molecular information has been a strong driver of technological development. Existing technologies for quantification of protein biomarkers can be divided into two categories. Laboratory assays employ sensitive and specific technologies that are generalizable to many target species that are of interest to clinicians, but this type of technology is not accessible to the clinician for biomarker quantification at the point-of-care -- it must be carried out carried out by specialized technicians and using bulky instrumentation, which results in long turnaround times. More accessible point-of-care quantification technologies do exist but are limited to a narrow class of high-abundance small-molecule targets. The work presented in this thesis focuses on the improving point-of-care biosensors for protein quantification by proposing fundamentally new approaches to molecular recognition, signal generation and target estimation. These studies advance the field of biosensing through innovations that hold promise to be generalizable to a plurality of analytes of clinical interest, and that always consider the end-use case first. The first project describes a novel sensor for rapid quantification of protein biomarkers, such as cytokines, directly in whole blood without the need for any sample preparation. This sensor is intended for use by the clinician at the bedside to address the need for rapid sample-to-answer assays. Using this sensor, we were able to quantify cytokine concentrations as low as ~100 pM in less than 45 minutes, directly in whole blood. The second project describes a new algorithm to process real-time biosensor data. This "pre-equilibrium" estimation algorithm can reconstruct an estimate of the continuously changing target concentration even when the kinetics of the sensor's affinity reagent are insufficiently rapid to track the changes in target concentration. We show that this algorithm could be useful in the realization of low-abundance, rapidly changing analytes such as insulin. Importantly, we show that even though, conventionally, antibody kinetics are considered to be too slow for real-time sensing applications, a typical antibody with k_on ~ 10^6 s-1 M-1 and K_D ~ 2 nM could be used with the pre-equilibrium algorithm for this application. The third part of this thesis is a survey of methods used to generate a class of affinity reagents called aptamer switches which are commonly used in real-time biosensing applications. In the fourth and final part of this thesis I introduce a novel affinity reagent that addresses the limitations of aptamers as receptors for real-time biosensing applications. This affinity reagent involves combining an aptamer switch with an antibody to leverage desirable properties of both reagents -- a built-in signal reporting mechanism, high affinity and specificity, and tunable thermodynamics. Preliminary experiments using a thrombin-specific model system show that this receptor has k_on ~ 10^6 s-1 M-1 and K_D ~ 1 nM, and thus retains the kinetic properties of the constituent antibody. Importantly, this implies that this type of receptor could be used in the context of a real-time biosensor that uses the pre-equilibrium estimation algorithm.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Maganzini, Nicolo |
|---|---|
| Degree supervisor | Soh, H. Tom |
| Thesis advisor | Soh, H. Tom |
| Thesis advisor | Appel, Eric (Eric Andrew) |
| Thesis advisor | Murmann, Boris |
| Degree committee member | Appel, Eric (Eric Andrew) |
| Degree committee member | Murmann, Boris |
| Associated with | Stanford University, Department of Electrical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Nicolo Maganzini. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/qy663wq4252 |
Access conditions
- Copyright
- © 2022 by Nicolo Maganzini
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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