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Abstract：
Single-molecule localization microscopy (SMLM) is a relatively new imaging modality, winning the 2014 Nobel Prize in Chemistry, and considered as one of the key super-resolution techniques. SMLM resolution goes beyond the diffraction limit of light microscopy and achieves resolution on the order of 10-20 nm. SMLM thus enables imaging single molecules and study of the low-level molecular interactions at the subcellular level. In contrast to standard microscopy imaging that produces 2D pixel or 3D voxel grid data, SMLM generates big data of 2D or 3D point clouds with millions of localizations and associated uncertainties. This unprecedented breakthrough in imaging helps researchers employ SMLM in many fields within biology and medicine, such as studying cancerous cells and cell-mediated immunity and accelerating drug discovery. However, SMLM data quantification and interpretation methods have yet to keep pace with the rapid advancement of SMLM imaging. Researchers have been actively exploring new computational methods for SMLM data analysis to extract biosignatures of various biological structures and functions. In this survey, we describe the state-of-the-art clustering methods adopted to analyze and quantify SMLM data and examine the capabilities and shortcomings of the surveyed methods. We classify the methods according to (1) the biological application (i.e., the imaged molecules/structures), (2) the data acquisition (such as imaging modality, dimension, resolution, and number of localizations), and (3) the analysis details (2D versus 3D, field of view versus region of interest, use of machine-learning and multi-scale analysis, biosignature extraction, etc.). We observe that the majority of methods that are based on second-order statistics are sensitive to noise and imaging artifacts, have not been applied to 3D data, do not leverage machine-learning formulations, and are not scalable for big-data analysis. Finally, we summarize state-of-the-art methodology, discuss some key open challenges, and identify future opportunities for better modeling and design of an integrated computational pipeline to address the key challenges.
摘要：
单分子定位显微镜（SMLM）是一种相对较新的成像模式，获得2014年诺贝尔化学奖,并被认为是关键的超分辨率技术之一。SMLM分辨率超出了光学显微镜的衍射极限，并达到了10-20nm的分辨率。因此，SMLM能够对单个分子进行成像并研究亚细胞水平的低水平分子相互作用。与产生2D像素或3D体素网格数据的标准显微镜成像相反，SMLM生成2D或3D点云的大数据，具有数百万个本地化和相关不确定性。这一前所未有的成像突破有助于研究人员在生物学和医学的许多领域采用SMLM，例如研究癌细胞和细胞介导的免疫力以及加速药物发现。然而,SMLM数据量化和解释方法尚未跟上SMLM成像的快速发展。研究人员一直在积极探索SMLM数据分析的新计算方法，以提取各种生物结构和功能的生物特征。在这次调查中,我们描述了用于分析和量化SMLM数据的最新聚类方法，并检查了所调查方法的功能和缺点。我们根据(1)生物应用(即，成像的分子/结构)，(2)数据采集(如成像模态、维度,决议,和本地化数量)，和(3)分析细节(2D与3D，视野与感兴趣的区域，使用机器学习和多尺度分析，生物特征提取，等。).我们观察到，大多数基于二阶统计量的方法对噪声和成像伪影敏感，尚未应用于3D数据，不要利用机器学习公式，并且对于大数据分析而言是不可扩展的。最后,我们总结了最先进的方法论，讨论一些关键的开放挑战，并确定未来的机会，以更好地建模和设计集成计算管道，以解决关键挑战。
