ADVANCING NANOPORE CHARACTERIZATION OF CARBOHYDRATES WITH CHEMICAL SYNTHESIS AND MACHINE-LEARNING
Date of Award
Doctor of Philosophy in Chemistry
Jason R. Dwyer
The work presented in this thesis covers advancements made in carbohydrate analysis, a topic relevant for pharmaceutical testing, medical diagnostics, food adulteration analysis, and numerous other glycomics-related purposes. While carbohydrates have been extracted and commodified by humans for several millennia,1,2 and their associated chemical identity and structural motifs have been known for over a century,3,4 reliable and readily available methods for full characterization of this prevalent and naturally occurring group of molecules are lacking and under-developed. Conventional methods of analysis show promise but are constrained by several obstacles to overcome. This thesis work answers a still-relevant 2012 call by the National Research Council to invest in inventing and developing technologies that can facilitate direct detection and full characterization of carbohydrates and adds a low cost and easy-to-use package to the performance needs that were called for. Nanopore sensors, the focus of this work, have been optimized for real-time DNA sequencing to the point where scientists from any field can purchase this technology off the shelf. The technology was lacking, from the molecular-scale to the nanoscale, for nanopore carbohydrate analysis. Thus, we undertook the challenge to discover, invent and develop suitable tools and approaches. This focused on advancing and integrating three areas in particular: developing and optimizing methods for chemically customized nanopores, using high quality biological standards to test and extend our performance horizons, and adapting and applying sophisticated methods for data analysis.
Silicon nitride (SiNx) nanopore sensors fabricated by controlled dielectric breakdown were used, both directly after fabrication and after applying robust, custom synthetic chemical methods for rational molecular-level control over the nanopore structure and performance. This process greatly improved sensor lifetimes and the ability to apply unique sensing conditions to dramatically improve sensor performance. The use of high-quality chemical and chemoenzymatic standards facilitated the collection of data used to tease out chemical identity and structural information with optimized machine learning methods attaining accuracies >90%, even resulting in differentiation between samples at the level of monomer composition – a necessary first step towards carbohydrate sequencing. The result is a nanoscale sensor that can be controlled with molecular-level precision to detect and differentiate between a wide variety of biologically relevant carbohydrate samples, and that is profoundly enabling for glycomics carried out even down to the level of single molecules.
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- Vliegenthart, J. F. G. D or L That Remained the Question: A Note on Emil Fischer (1852–1919) on the Occasion of His 100th Dying-Day. Carbohyd Res 2021, 500, 108245. https://doi.org/10.1016/j.carres.2021.108245.
- Lichtenthaler, F. W. Emil Fischer’s Proof of the Configuration of Sugars: A Centennial Tribute. Angewandte Chemie Int Ed Engl 1992, 31 (12), 1541–1556. https://doi.org/10.1002/anie.199215413.
Hagan, James Thomas, "ADVANCING NANOPORE CHARACTERIZATION OF CARBOHYDRATES WITH CHEMICAL SYNTHESIS AND MACHINE-LEARNING" (2023). Open Access Dissertations. Paper 1501.
Available for download on Thursday, May 08, 2025