CIF:Small:Developing Theory of Spatiotemporal-Resolution and Spatiotemporal-Localization Algorithms for Single-Molecule Localization Microscopy

CIF：Small：发展单分子定位显微镜的时空分辨率和时空定位算法理论

• 批准号: 2313072
• 负责人: Yi Sun
• 金额: $ 58.02万
• 依托单位: CUNY City College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313072&HistoricalAwards=false
• 关键词: CIF; Small; Developing; Theory; Spatiotemporal

Single-molecule localization microscopy (SMLM) overcame the longstanding light-diffraction barrier to provide super-resolution optical imaging, significantly impacting biological research. Nevertheless, two important problems remain before SMLM can be advanced further. First, still needed is a theory of spatiotemporal resolution which speaks to the inherent power of an SMLM system in resolving a molecule in space and time, thereby quantifying how each part of an SMLM system affects the information-theoretical limit of spatiotemporal resolution. Thus, a spatiotemporal resolution theory would lay an information-theoretic foundation and provide guidance in the development of electro-optical hardware, fluorescence molecules, and localization algorithms to advance SMLM. Second, the majority of localization algorithms in literature exploit the information of only a single data frame. Advanced spatiotemporal localization algorithms need to be developed to fully exploit the information of the multiple frames of a data movie in order to approach the information-theoretic limit of spatiotemporal resolution. Advanced spatiotemporal localization algorithms can significantly enhance spatiotemporal resolution and fulfill the needs of both super spatial and temporal resolutions in biomedical research. The spatiotemporal resolution theory and spatiotemporal location algorithms to be developed in this project will broadly impact interdisciplinary research of SMLM in both theory as well as in applications to biomedical research.In this project, first, a conceptually novel theory of spatiotemporal resolution with respect to one-dimensional (1D), 2D, and 3D spatial resolutions will be developed based on the Fisher information of a universal model of a data movie. The effect of system parameters on the spatiotemporal resolution and the tradeoff between spatial and temporal resolutions 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. Second, two types of advanced spatiotemporal localization algorithms will be developed to fully exploit the spatial and temporal information in a data movie. One is the maximum movie-likelihood (UGIA-M) 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 SMLM images. The two types of algorithms will be evaluated via simulation in terms of spatiotemporal resolution using a universal partition-based metric driven by root-mean-square minimum distance and root-mean-square error with respect to a UGIA-M benchmark as well as by comparison to existing high-performance localization algorithms from the literature. The two types of algorithms will be applied to the analysis of real datasets of biological specimens, and source code for the developed theory and algorithms will be posted in public repositories for open access by the community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

单分子定位显微镜（SMLM）克服了长期的光划分屏障，以提供超分辨率的光学成像，从而显着影响生物学研究。然而，在SMLM可以进一步推进之前，仍然存在两个重要的问题。首先，仍然需要时空分辨率的理论，它表明了SMLM系统在解决时空中分子解决分子方面的固有力量，从而量化了SMLM系统的每个部分如何影响时空分辨率的信息理论限制。因此，时空分辨率理论将奠定信息理论基础，并为开发电光硬件，荧光分子和定位算法的发展提供指导。其次，文献中的大多数本地化算法仅利用单个数据框架的信息。需要开发先进的时空定位算法，以充分利用数据电影的多个框架的信息，以便接近时空分辨率的信息理论极限。晚期时空定位算法可以显着提高时空分辨率，并满足生物医学研究中超时空和时间分辨率的需求。该项目中要开发的时空分辨率理论和时空位置算法将在理论以及在生物医学研究中的应用中广泛影响SMLM的跨学科研究。首先，在该项目中，在概念上，一种概念性的新型理论，是一种新颖的空间分辨率，是针对一位差异（1D），2D和3D的渔民的依次开发的一定范围，并依次开发了3DDD，2D和3D的范围。数据电影。系统参数对时空分辨率以及空间和时间分辨率之间的权衡的影响将进行分析和数值研究。将开发和模拟实现数据电影Fisher信息的无偏高斯信息进攻估计器。其次，将开发两种类型的高级时空定位算法，以完全利用数据电影中的时空信息。一种是最大的电影（UGIA-M）算法，它最大程度地提高了整个数据电影的可能性。另一个是时间相关性增强算法，这些算法利用嵌入在逐帧局部化的SMLM图像中的时间相关性。这两种类型的算法将使用时空分辨率来评估，使用基于通用分区的度量标准，这是由根平方的最小距离驱动的，最小距离和根平方的误差以及与UGIA-M-M基准以及与现有的高性能本地化算法相比。两种类型的算法将应用于对生物标本的真实数据集的分析，而开发理论和算法的源代码将发布在公共存储库中，以供社区公开访问。该奖项反映了NSF的法定任务，并通过使用基金会的知识优点和广泛影响来评估NSF的法定任务。