Quantification of fibrous spatial point patterns from single-molecule localization microscopy (SMLM) data
Quantification of fibrous spatial point patterns from single-molecule localization microscopy (SMLM) data
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DOI：
10.1093/bioinformatics/btx026
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发表时间：
2017-06-01
期刊：
影响因子：
5.8
通讯作者：
Owen, Dylan M.
中科院分区：
文献类型：
--
作者：
Peters, Ruby;Muniz, Marta Benthem;Owen, Dylan M.
动机：与生成像素化图像的传统显微镜不同，SMLM 以定位坐标列表（空间点图案（SPP））的形式生成数据。通常，使用聚类分析算法来分析此类 SPP，以量化质膜等内的分子聚类。虽然 SMLM 聚类分析现已得到很好的发展，但用于分析纤维结构的技术仍然很少被探索。结果：在这里，我们展示了一种基于 Ripley 的 K 函数的统计方法，用于定量评估 2D SMLM 数据集中的纤维结构。使用模拟数据，我们提出了描述纤维空间排列的基础理论，并展示了如何从点画数据集中定量地得出这些描述。我们还展示了在 T 细胞免疫突触的纤维肌动蛋白网络的背景下，通过将可交换单分子定位 (IRIS) 方法集成到 SMLM，使用图像重建获得的实验数据技术，其结构已被证明对 T 细胞激活很重要。
Motivation: Unlike conventional microscopy which produces pixelated images, SMLM produces data in the form of a list of localization coordinates-a spatial point pattern (SPP). Often, such SPPs are analyzed using cluster analysis algorithms to quantify molecular clustering within, for example, the plasma membrane. While SMLM cluster analysis is now well developed, techniques for analyzing fibrous structures remain poorly explored.Results: Here, we demonstrate a statistical methodology, based on Ripley's K-function to quantitatively assess fibrous structures in 2D SMLM datasets. Using simulated data, we present the underlying theory to describe fiber spatial arrangements and show how these descriptions can be quantitatively derived from pointillist datasets. We also demonstrate the techniques on experimental data acquired using the image reconstruction by integrating exchangeable single-molecule localization (IRIS) approach to SMLM, in the context of the fibrous actin meshwork at the T cell immunological synapse, whose structure has been shown to be important for T cell activation.