Single-molecule localization microscopy (SMLM) can significantly improve the image of a biological sample at a molecular scale. dNAM uses the technique of SMLM to store data into DNA. dNAM encodes information into 6 x 8 grids of biological structure, also known as DNA origami. The robustness and data density of the dNAM algorithm highly depends on the performance of SMLM. Emitter localization, drift correction, error correction, and origami identifications are some of the core steps in this system. The main goal of this manuscript is to propose three research objectives to make both dNAM and SMLM more robust and efficient. First, we proposed a deep neural network for emitter localization. Here we will also study the impact of overlapped emitters on dNAM accuracy. Second, we proposed a novel drift correction algorithm. We will study the impact of our algorithm in three different metrics. Finally, we will analyze the effect of incorporating photon intensity in the dNAM error correction algorithm.
Timothy Andersen, Ph.D. (Chair), William L. Hughes, Ph.D. (Co-chair), Reza Zadegan, Ph.D.