Ultrafast Data Mining of Molecular Assemblies in Multiplexed High-Density Super-Resolution Images.

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 102-folds enhancement in computational speed, allowing for high-throughput Triple-Correlation analyses and robust quantification of molecular complexes in multiplexed super-resolution microscopy.