High speed, high information content single-molecule techniques for studying protein interactions and organizations in cells - Project summary My research is focused on developing imaging tools and data analysis in single-particle tracking (SPT) and single molecule localization microscopy (SMLM). My long-term goal is to push their resolution limits to facilitate the study of protein interactions and organizations in cells at high spatiotemporal resolution. In the next five years, my lab will focus on two research directions: 1) developing high-resolution two-color SPT to study dimer formation and dissociation; 2) unifying data analysis for SMLM to extract maximum information and achieve expert-free analysis. SPT has been widely used in studying protein dynamics and interactions both in vitro and in live cells. However, the commonly used camera-based SPT methods are limited to low spatiotemporal resolution. Real-time SPT techniques, such as MINFLUX, offer significantly higher spatiotemporal resolution but are limited in their applications to two-color tracking. We will develop a two-color SPT microscope based on real-time SPT techniques and apply the developed system to study the interactions of epidermal growth factor receptor (EGFR) in live cells. EGFR, which plays an important role in regulating cell growth, motility and differentiation, is an attractive candidate for anticancer therapy. SMLM, as one of the major super-resolution microscopy techniques, can achieve a spatial resolution down to 2 nm and has been increasingly used in structural biology to bridge the gap between electron microscope and confocal microscope. Current SMLM data analysis pipeline consists of extracting the point spread function (PSF), localization using the obtained PSF model, drift correction and post-analysis such as clustering and particle averaging. This multi-step approach often complicates the data analysis procedure, as each step requires a certain level of expertise. Taking advantage of the large-data processing capability of deep neural network (DNN), we will develop a data-analysis framework based on DNN to achieve simultaneous localization and PSF extraction from SMLM data. This framework can be extended to any SMLM system and has the potential to extract all information from the data. I believe that the development of fast, high-resolution, two-color SPT techniques will significantly advance the study of protein interactions in cells. At the same time, a unified data-analysis framework for SMLM will render SMLM techniques more robust and accurate, thereby establishing them as a standard tool for structural biology.
