A Method for Mapping miRNA Transience as Biomarkers for Myocardial Infarction

Master's Thesis


Ischemic heart disease perennially is the most common cause of death in the US and the world. Myocardial infarction (MI) is the most lethal and rapid pathology in this category and requires rapid diagnosis, time-critical treatment, and disease progression monitoring. Herein a process to mimic the MI experience in vitro through iteratively more complex culture models is described in lieu of a microRNA (miR) detection platform and extracellular vesicle (EV) lysis chip. This platform will be compared against clinical plasma samples to better inform the iterative process as well as study the temporal miR transients in ST-elevated MI (STEMI) and how these compare to miR profiles in coronary artery disease (CAD).

Of the miRs examined miR-1, 208, and 499 exhibit the most promising indicators of myocardial irregularities. These targets show differences in expression based on tissues affected, flow conditions, oxygen content, and miR release mechanisms. miR- 08 appears to rule in cardiac pathology, miR-1 serves to discriminate between STEMI and CAD as well as between STEMI and Control, and miR-499 differentiates between cardiomyocyte and endothelial cell disruptions. Herein a procedure to lyse extracellular vesicles with minimal processing time and no additional buffers confounding miR quantification.

Expanding the pool of clinical samples to Multiplexed sensors measuring these miRs in combination could provide clinically relevant information. As the tissue culture models better approximate clinical samples they may branch out beyond MI modeling and serve to model intersections between MI, CAD, and other cardiac pathologies. Employing miR quantification, EV lysis, and nm-sized particle fractionation could open a series of pathology indicators in a simple point-of-care apparatus.


Attribute NameValues
Author Stuart Blood
Contributor Hsueh-Chia Chang, Research Director
Contributor Pinar Zorlutuna, Research Director
Degree Level Master's Thesis
Degree Discipline Bioengineering
Degree Name Master of Science in Bioengineering
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Submission Date 2020-07-17
Record Visibility Public
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