Cytokines are small-molecule signaling proteins involved in cell-cell regulation. The detection of low-abundance cytokines is challenging since the currently available techniques are limited by sensitivity and are time-consuming. Nanopore sensing is an emerging technique in nanotechnology that is catalyzing key breakthroughs in many areas, including the analysis and study of proteins at the single-molecule level. Solid-state nanopore sensing has the advantage of analyzing small copy numbers of biomolecules, such as DNA, with high throughput. However, protein detection using nanopores is still in in infancy because the mechanisms of native protein translocation inside the solid-state nanopore are highly complicated. The goal of this project is to develop a novel solid-state nanopore device for identification and quantification of cancer cytokines directly from cell culture. Vascular endothelial growth factor (VEGF) is chosen as a model cytokine due to its high abundance in cancerous tissue, and its well-characterized molecular structure.
Firstly, we used a nanopore sensor to monitor individual VEGF proteins in solution while simultaneously obtaining tertiary and quaternary structural information. Next, we used the translocation signature to identify VEGF secreted directly from the culture media of the breast cancer cell line. A series of DNA and RNA aptamers was screened to selectively bind to secreted VEGF, enhancing the detection rate and creating a unique translocation signature for easy protein discrimination. Finally, we integrated the nanopore with a hard microfluidic device designed to facilitate the on-chip sample preparation prior to nanopore sensing. This nanopore-microfluidic device may allow scientists and clinicians to directly detect biomarkers secreted from a small population of cultured cells, which would revolutionize cancer diagnostics and prognostics. / 2020-10-22T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/32676 |
| Date | 23 October 2018 |
| Creators | Varongchayakul, Nitinun |
| Contributors | Grinstaff, Mark, Meller, Amit |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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