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Biomolecular Processing and Quantitative Localization of Single Cell Analysis


A technology for biomolecular processing and localization of gene expression at the single cell level is essential to understanding cellular heterogeneity. Traditional methods of tissue analysis cannot identify the stochastic variation that makes individual cells unique because these differences are masked by bulk measurements. In this dissertation, two technologies are described for different aspects of single cell analysis. The first part is a sample preparation method for biomolecular processing from single cell using advanced microfluidic devices with permeable polymer barriers; the second one is digital quantification and localization of biomolecules at the single cell level. Microfluidic devices provide a powerful tool for cells and biomolecular processing due to their ability to compartmentalize reactions at physiologically relevant concentrations in a highly parallelizable manner. However, the serial biochemical reaction required for efficient and low-loss single-cell analysis cannot be performed in a single fluidic chamber. In the first part of the dissertation a novel microfluidic architecture is described that uses semipermeable polymer barriers to enables the capture and transportation of DNA and cells, solution exchange, and serial biochemical reactions to be carried out in a single microfluidic chamber. Meanwhile, advancements in single-cell sequencing techniques have allowed the full gene expression of single cell to be profiled. However, the native spatial context of the cell is lost because of the process of cell lysis. In the second part of the dissertation, a highly multiplexed method to selectively detect and localize hundreds of targeted genes in-situ is presented. This method retains local information of targeted genes by processing enzymatic reactions in-situ without destroying cells. It also utilizes the multiplexing capability and high specificity of padlock probes to detect targets of interest at a high signal-to-noise ratio by selectively amplifying biomolecules of interest

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