BME PhD Preliminary Exam Announcement for Li Fang (F. Huang, advisor) Everyone is invited to attend the public presentation beginning at 2:30pm. Title: Molecular localization of single molecule switching sequences with deep learning Date: December 12, 2022 Time: 2:30 pm Location: MJIS 2001 Virtual link: https://purdue-edu.zoom.us/j/99210793060?pwd=WnlHbVlSUlNGWE1KaGN4QWRCS0lEUT0... Meeting ID: 992 1079 3060 Passcode: 158594 Committee members: Dr. Fang Huang, Chair; Dr. Leopold N. Green; Dr. Young L. Kim; Dr. Chongli Yuan Abstract: Single molecule localization microscopy (SMLM) has become an essential tool in imaging nanoscale biological structures. It breaks the diffraction limit by utilizing photo-switchable or photo-convertible fluorophores to obtain isolated single molecule emission patterns (i.e. PSFs) and subsequently localize the molecule’s position with a precision down to ~ 25 to 80 nm laterally-axially. However, standard SMLM algorithms require sparse activation to minimize emission pattern overlapping, which limits imaging speed and temporal resolution and hinders its application in dynamic live cell imaging. Some multi-emitter fitting algorithms have been developed to analyze higher density data and some methods have shown improved localization precision when utilizing multiple frames. But these methods require an accurate PSF model and prior knowledge of photo-switching behavior and fail to fully extract the information in the sequence data. Here, we propose to develop a high-density single molecule localization algorithm through molecule blinking sequence analysis with deep learning to achieve high localization precision approaching the theoretical limit at high density in three dimensions. We aim to apply the algorithm to resolve nanoscale intracellular structures and fast dynamic processes in live cells with high temporal and spatial resolution.