Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY, 10016, USA.
Nat Commun. 2019 Jan 10;10(1):119. doi: 10.1038/s41467-018-08048-2.
Multicolor single-molecule localization super-resolution microscopy has enabled visualization of ultrafine spatial organizations of molecular assemblies within cells. Despite many efforts, current approaches for distinguishing and quantifying such organizations remain limited, especially when these are contained within densely distributed super-resolution data. In theory, higher-order correlation such as the Triple-Correlation function is capable of obtaining the spatial configuration of individual molecular assemblies masked within seemingly discorded dense distributions. However, due to their enormous computational cost such analyses are impractical, even for high-end computers. Here, we developed a fast algorithm for Triple-Correlation analyses of high-content multiplexed super-resolution data. This algorithm computes the probability density of all geometric configurations formed by every triple-wise single-molecule localization from three different channels, circumventing impractical 4D Fourier Transforms of the entire megapixel image. This algorithm achieves 10-folds enhancement in computational speed, allowing for high-throughput Triple-Correlation analyses and robust quantification of molecular complexes in multiplexed super-resolution microscopy.
多色单分子定位超分辨率显微镜使人们能够可视化细胞内分子组装体的超精细空间组织。尽管已经做出了许多努力，但目前用于区分和量化这些组织的方法仍然有限，特别是当这些组织包含在密集分布的超分辨率数据中时。从理论上讲，高阶相关（如三重相关函数）能够获得单个分子组装体的空间配置，这些组装体被掩盖在看似不和谐的密集分布中。然而，由于其计算成本巨大，即使是高端计算机也无法进行此类分析。在这里，我们开发了一种用于高内涵多路复用超分辨率数据的三重相关分析的快速算法。该算法计算了来自三个不同通道的每三个单分子定位的所有几何构型形成的概率密度，避免了整个百万像素图像不切实际的 4D 傅里叶变换。该算法在计算速度上提高了 10 倍，允许在多路复用超分辨率显微镜中进行高通量三重相关分析和对分子复合物进行稳健的量化。
