Developing Spatiotemporal Resolution Theory and Localization Algorithms for Superresolution Microscopy to Elucidate Endothelial Surface Glycocalyx (ESG) Ultrastructure
Abstract
The super-resolution microscopy (SRM) technique, invented in 2006, has been significantly impacting on the biological research and won the 2014 Nobel Prize in Chemistry. While the SRM has been tremendously successful, there is no theory about spatiotemporal resolution from the information-theoretical point of view. It is unknown how to develop an advanced localization algorithm to approach the theoretical limit of spatiotemporal resolution. While the endothelial surface glycocalyx (ESG) plays important roles in maintaining normal cardiovascular functions, the current technique is unable to reveal its spatial-molecular organization which is eager to be revealed in the past half century. This project will carry out research in three aspects. First, a conceptually novel spatiotemporal resolution theory with respect to 1D, 2D, and 3D spatial resolutions will be established based on the Fisher information of a universal model of data movie. The tradeoff of spatial resolution and temporal resolution and the effect of system parameters on the spatiotemporal resolution will be analytically and numerically investigated. The unbiased Gaussian information-achieving estimator that achieves the Fisher information of a data movie will be developed and simulated. A manual of guidelines for setup of a SRM system will be written on the basis of the spatiotemporal resolution theory. Second, two types of novel advanced spatiotemporal localization algorithms will be developed to fully exploit the spatial and temporal information. One is the maximum movie-likelihood algorithm that maximizes the likelihood of an entire data movie. The other is the temporal correlation enhancement algorithms that exploit the temporal correlation embedded in the frame-by-frame localized SRM images. These algorithms will be evaluated in simulation in terms of spatiotemporal resolution by the universal metric of root mean square minimum distance with benchmark of the theoretical spatiotemporal resolution and in comparison of the high-performance localization algorithms in literature. These algorithms will also be applied to analysis of real datasets of biological samples. A set of programming codes for the developed theory and algorithms will be posted in public repositories for open access by the community. Third, the two types of developed localization algorithms will be applied to elucidate the ESG ultrastructure. The data movies of ESG samples will be acquired by the N-STORM superresolution microscope in our Nanoscopy Laboratory. The developed localization algorithms with customer codes will be applied to imaging and analyzing the ESG ultrastructure. The proposed activities will broadly impact on the interdisciplinary research of SRM technique and the application in biomedical research. The spatiotemporal resolution theory will provide a theoretical foundation of spatiotemporal resolution for hardware and software development. The manual of setup guidelines and the advanced algorithms with customer codes will be broadly applied in the SRM community. The elucidation of ESG ultrastructure will impact on designing therapeutic agents for prevention and treatment of various cardiovascular diseases. The research activities, which include three underrepresented undergraduate students, in joint activities of several undergraduate research programs will broaden the participation of underrepresented students in advanced research and education at the City College of New York, a minority-serving institution.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 24, 2023
- Source ID
- W911NF2310189
Entities
People
- Yi Sun
Organizations
- Army Contracting Command
- City University of New York
- Office of the Secretary of Defense