Integrated Microfluidic System for Digital Detection of Extracellular Vesicles

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Issue Date
2020-08-31Author
Ravichandran, Gopi chandran chandran
Publisher
University of Kansas
Format
69 pages
Type
Thesis
Degree Level
M.S.
Discipline
Bioengineering
Rights
Copyright held by the author.
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Show full item recordAbstract
Investigating the components of extracellular vesicles (EV) has shown substantial promise for cancer diagnosis. However, detecting them individually and profiling its membranal proteins is still challenging. Isolating EVs individually can help us track their protein expression and to study their heterogeneity. We propose a microfluidic platform for capture of EVs and digitally detect them with the help of a digital microarray and a smart actuation device for sealing the microwells. The chip achieves the EV quantification by digital Enzyme-Linked Immunosorbent Assay (ELISA) and enzymatic signal amplification. Specific areas of glass slide beneath the detection area of the chip coated with anti-CD81 antibodies are used to capture EVs. A cocktail mixture of biotin-labeled anti-CD9 and anti-CD63 antibodies were used as detection antibodies to detect the captured EVs. Enzymatic amplification of the detection signal was achieved with the help of Streptavidin Beta Galactosidase (SßG) as a reporter enzyme and Fluorescein-di-ß-D-galactopyranoside (FDG) as a substrate. A closed-loop actuator device that is controlled by a microcontroller using the feedback from a force sensor was developed to seal the microwell array to conduct discrete enzymatic reactions for digital quantification of single captured targets. An automatic image processing algorithm in MATLAB has been developed for digital signal image analysis. It automatically detects individual microwells and determines the average intensity of each microwell by calculating the average of intensities around the center of the microwell. The digital detection and quantification of captured EVs were achieved. Automation was incorporated in sealing and analyzing images, which makes it closer to fully automated microfluidic systems in the future.
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