Super-Resolution Geometric Barcoding With Programmable Self-Assembly and Programmable Autonomous Blinking
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CitationXu, Weidong. 2020. Super-Resolution Geometric Barcoding With Programmable Self-Assembly and Programmable Autonomous Blinking. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
AbstractGeometric barcoding has shown promise in fluorescence imaging applications for multiplexed, specific and sensitive detection of single molecules. This dissertation presents a super-resolution geometric barcoding scheme, which combines the advantages of DNA nanotechnology and super-resolution imaging. First, I presented a DNA nanostructure based geometric barcoding scheme compatible with super-resolution imaging with DNA-PAINT (Point Accumulation for Imaging in Nanoscale Topography), which enabled the capture and visualization of multiple distinct molecular targets inside each individual DNA nanostructure. I summarized the design principle and technical considerations of our geometric barcoding platform, and demonstrated 2 strategies for increasing the multiplexing capability of our geometric barcoding system. Next, I applied this super-resolution geometric barcoding system to multiplexed miRNA profiling. We used synthetic DNA nanostructures as programmable miRNA capture "nano-array", and visualized our geometric barcoding platform with captured miRNAs by applying DNA-PAINT method. We demonstrated high-specificity (single nucleotide mismatch discrimination), multiplexed (8-plex, 2 panels) and sensitive measurements on synthetic miRNA samples, and applied one 8-plex panel to measure endogenous miRNAs levels in total RNA extract from HeLa cells. Finally, I developed a MATLAB DNA-PAINT data analysis software package based on an earlier version, as an effort to improve the accessibility and reproducibility of DNA-PAINT method and technologies based on it, including our super-resolution geometric barcoding platform. I summarized the key features that we implemented for DNA-PAINT data processing and data quality assurance, including their principle and usage, and discussed the architecture design choices and the future development of the software.
Citable link to this pagehttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37365991
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