Author ORCID Identifier

0000-0002-4818-2638

Date of Award

5-31-2026

Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Integrative Biosciences

First Advisor

Kimberly Hamad-Schifferli

Second Advisor

Nichola Hill

Abstract

Rapid and reliable diagnostics are a cornerstone of outbreak response, yet current diagnostic paradigms are fundamentally limited by their dependence on prior knowledge of a pathogen. Most rapid tests are designed to detect a single known target, leaving public health systems vulnerable to newly emerging and re-emerging viruses for which no reagents yet exist. As the frequency of zoonotic spillover and viral evolution continues to increase, there is an urgent need for diagnostic platforms that can not only identify known pathogens, but also detect and flag the unknown. In this thesis, I develop and demonstrate a new class of multiplexed lateral flow assays (LFAs) that leverage antibody repurposing and cross-reactivity, nanoparticle color encoding, and multivariate analytical techniques to enable pattern-based detection of emerging viral threats.

First, foundational strategies for robust immunoassay design were established through the development of an H5N1 assay capable of operating across complex matrices including serum, milk, eggs, and wild bird samples, and through machine-learning-guided optimization of multiplexed SARS-CoV-2 serology tests. These studies demonstrate how computational tools can expand LFA performance from binary detection toward richer, multidimensional outputs.

Building on these foundations, this thesis introduces a sensor-array paradigm for the detection of unknown viral variants and species. Using SARS-CoV-2 variants as a proof of concept, a two-color, two-spot assay was developed that could detect previously unseen variants through pattern recognition rather than target-specific binding. This concept was fully realized in a multiplexed immunosensing platform for flaviviruses, where five cross-reactive antibodies and multicolor nanoparticle labels generated distinct colorimetric fingerprints for viruses spanning the Orthoflavivirus genus. Using principles from chemical olfaction and unsupervised machine learning methods, including principal component analysis and hierarchical clustering, enabled both known viruses and novel signatures to be identified. Optimization of color space and antibody contributions further improved assay performance, demonstrating that pattern-based immunosensing is a viable strategy for detecting emerging pathogens without requiring sequence-specific reagents.

Finally, mechanisms to expand the capabilities of these system were explored, like adding modularity through the Ampli microfluidic system or like adding automation to the color pattern interpretation through computer vision systems.

Together, this work establishes a generalizable framework for diagnostics of the unknown, enabling rapid, deployable, and adaptive detection of emerging and re-emerging infectious diseases.

Comments

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Available for download on Tuesday, December 01, 2026

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