Title:
Electronic antibody microarrays for label-free cell immunophenotyping

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Author(s)
Liu, Ruxiu
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Advisor(s)
Sarioglu, A. Fatih
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Abstract
Immunophenotyping (i.e., identifying cell membrane antigens) is widely used to characterize cell populations in basic research and to diagnose diseases from surface biomarkers in the clinic. This process usually requires complex instruments such as flow cytometers or fluorescence microscopes, which are typically housed in centralized laboratories. Microfluidic devices can be used as immunophenotyping assays through immunoaffinity-based capture of the target cells, and are able to analyze a limited-volume sample in a small-footprint, with the ultimate goal of replacing the need for centralized laboratories. However, microfluidic devices are typically designed for qualitative analysis, and almost always require external tools for the readout, which negates the cost and portability benefits of the chip itself. In the thesis, we introduce a microfluidic technology that employs a network of integrated electrical sensors to identify cell subpopulations based on their membrane antigens in a quantitative manner. To realize this technology, we develop a scalable electronic sensor network called microfluidic CODES (microfluidic coded orthogonal detection by electrical sensing), which combines code division multiple access (CDMA), a spread spectrum telecommunications technique, with Coulter sensing for the distributed detection of cells at strategic nodes across the microfluidic device from a single electrical output. By integrating the Microfluidic CODES technique with microfluidic cell capture chambers pre-functionalized with antibodies against target antigens, the device will achieve all-electronic cell immunophenotyping through the combinatorial arrangement of antibody sequences along microfluidic paths. Our technology will not only provide an integrated platform for label-free combinatorial immunophenotyping of cell populations against multiple antigen targets, but also be built on affordable hardware well suited for resource limited settings or point-of-care applications.
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Date Issued
2020-04-25
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Dissertation
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