Adam Derr
Adam Derr
Adam Derr
Helios Scholar

School: Grand Canyon University

Hometown: Sun City, Arizona

Mentor: Kendall Jensen, PhD

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Identifying biomarkers for neurodegenerative disease through EV protein analysis

Over the past decade, extracellular vesicles (EV’s) and their cargo have become a topic of interest in research. These tiny containers are excreted from cells and are packed with a variety of protein and nucleic acid cargo which allows the cell to communicate with other cells and across the body. EV’s are involved in significant aspects of the body’s function such as homeostasis, cell behavior, and even disease development. Since EV’s often reflect the protein expression profile of the cell of origin, there is considerable interest in leveraging EV’s for prognostic, diagnostic, and therapeutic use. This would be especially helpful for neurodegenerative diseases like Alzheimer’s and Parkinson’s in which affected people do not show symptoms until much later in life. However, one of the challenging aspects of working with EV’s is their small size (40-200nm in diameter) which puts them outside the limit of detection for most instruments. Their nano-scale size also means they carry a minute amount of protein cargo, making protein expression analysis via traditional methods difficult. Therefore, in order to use EV cargo proteins as potential biomarkers, there is a need to develop quantitative, ultra-sensitive, high throughput methods for protein analysis. Here, we attempted to optimize JESS, an automated protein analysis instrument, for use with low protein inputs from EVs. We tested various EV protein lysis methods using human plasma as a source, and were able to detect target proteins held inside EVs. Our results demonstrate not only great potential for accurately locating target proteins, but additionally that results could be obtained in a period of approximately 5 hours from sample to statistics using the JESS system. The sensitivity, speed, and minimal sample volume needed would make this method ideal for rapid and robust analysis. We anticipate this process to be useful for quantifying EV protein expression and identifying biomarkers for disease.