Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)

Graduate Group

Mechanical Engineering & Applied Mechanics

First Advisor

David D. Issadore


Extracellular vesicles (EVs) have shown great potential in diagnostics, therapeutics, and have been discovered to play a key role in intercellular communication. The study of EVs in biological fluids has proven challenging due to the nanoscale size of EVs (30 nm-1 µm diameter), the enormous quantity of EVs present in clinical samples (e.g. 1010 /mL in plasma), and the heterogeneous properties of EVs, even within those that originate from the same cell. My thesis has developed two distinct, but related, technologies to address these challenges. The first half of my thesis focuses on isolation and interpretation of specific subsets of EVs from biological samples, such as plasma, based on particular expressions of surface proteins. From these isolated EVs we have demonstrated, across multiple diseases, that there are signatures of disease states encoded in the EV RNA cargo, which we identified using supervised machine learning. To this end, building on prior work from our group, we developed a multichannel nanofluidic system that could analyze crude clinical plasma samples with nanoscale precision, which was coined Track Etched Magnetic Nanopore (TENPO). We evaluated the clinical potential TENPO by first applying it to diagnosing and staging pancreatic cancer, where current biomarkers have proven elusive to achieve sufficient sensitivity and specificity. In this work, we algorithmically combined tumor-associated EV mRNA and miRNA, isolated from plasma using TENPO, with ccfDNA levels, KRAS mutation detection, and CA19-9 via an ensemble machine learning model to form a multi-analyte panel. On an independent, blinded validation set (N = 136), we were able to distinguish patients with pancreatic cancer from those without at an accuracy of 92% (AUC=0.95). Moreover, among patients with pancreatic cancer, my model achieved significantly higher accuracy for disease staging (84%) than the current standard imaging method (64%). In addition to pancreatic cancer, I have also applied this approach to traumatic brain injury and to Alzheimer’s Disease to explored its diagnostic value in neurodegenerative diseases. Though TENPO was successful in isolating specific subsets of EVs for downstream analysis, it was not able to resolve the heterogeneity that is known to exist between individual EVs. Existing single EV analysis methods can only analyze a small number of EVs (< 20,000), limiting their ability to evaluate rare EV subsets due to subsampling error when searching for these rare EVs amongst the high EV background present in plasma. To address this challenge, I have developed a high throughput, droplet based optofluidic platform to quantify specific single EVs. The key innovation of my platform is parallelization of droplet generation, processing, and analysis to achieve a throughput >100x greater than typical in microfluidic systems, using only simple optics and accessible soft-lithography fabrication. I demonstrated that this improvement in throughput can be leveraged to quantify human neuron derived EVs at a limit of detection LOD = 9 EVs/µL, a >100x improvement over gold standard single EV characterization methods. Additionally, I demonstrated the potential of this system for use in clinical samples by detecting EVs in a complex media, containing up to 4,000 fold more background EVs, and achieved an LOD = 11 EVs/µL. Beyond extracellular vesicles, I was also inspired to apply this immunospecific, nanoscale detection and analysis modality to other subcellular materials, namely mitochondria. I have developed a pipeline to isolate and amplify single mitochondrion DNA from individual cells with 20x higher yield than with conventional tools. With the improved yield, we were also able to reveal the pervasive single nucleotide variation on mitochondrion DNA within single cells. We also compared the genomic variation within neuron mitochondria versus that within astrocyte mitochondria, which is impossible via traditional methodology.


Available to all on Saturday, July 05, 2025

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