Abstract
Single molecule localization microscopy has become a prominent technique to quantitatively study biological processes below the optical diffraction limit. By fitting the intensity profile of single sparsely activated fluorophores, which are often attached to a specific biomolecule within a cell, the locations of all imaged fluorophores are obtained with ∼20 nm resolution in the form of a coordinate table. While rendered super-resolution images reveal structural features of intracellular structures below the optical diffraction limit, the ability to further analyze the molecular coordinates presents opportunities to gain additional quantitative insights into the spatial distribution of a biomolecule of interest. For instance, pair-correlation or radial distribution functions are employed as a measure of clustering, and cross-correlation analysis reveals the colocalization of two biomolecules in two-color SMLM data. Here, we present an efficient filtering method for SMLM data sets based on pair- or cross-correlation to isolate localizations that are clustered or appear in proximity to a second set of localizations in two-color SMLM data. In this way, clustered or colocalized localizations can be separately rendered and analyzed to compare other molecular properties to the remaining localizations, such as their oligomeric state or mobility in live cell experiments. Current matrix-based cross-correlation analyses of large data sets quickly reach the limitations of computer memory due to the space complexity of constructing the distance matrices. Our approach leverages k-dimensional trees to efficiently perform range searches, which dramatically reduces memory needs and the time for the analysis. We demonstrate the versatile applications of this method with simulated data sets as well as examples of two-color SMLM data. The provided MATLAB code and its description can be integrated into existing localization analysis packages and provides a useful resource to analyze SMLM data with new detail.
Highlights
The subcellular localization of proteins and their interaction with other biomolecules is a critical determinant of their function
We show the application to two-color single molecule localization microscopy (SMLM) data of UNC51-like kinase 1 (ULK1) and Atg13, two proteins that have been recently shown to be involved in the initiation of autophagy when co-clustered
The second step is the function “cc_separation_pipeline”, which accepts as arguments each list of coordinates, a vector of cutoff distances for clustering for each dataset [(0, 0) if no clustering is to be performed], the cutoff distance for the crosscorrelation filtering, and a vector of minimum stoichiometries considered for colocalization for each dataset [(1, 1) for no minimum]
Summary
The subcellular localization of proteins and their interaction with other biomolecules is a critical determinant of their function. Based on the cross-correlation, a distance cutoff can be defined to separate localizations that cluster or colocalize with a second protein of interest These separated molecule lists can be separately visualized and further analyzed with any existing secondary analysis algorithm to e.g. determine the number of molecules in and the size of a cluster, their diffusive state in live-cell data, or their degree of crosscorrelation (Owen et al, 2010; Sengupta et al, 2011; Veatch et al, 2012; Puchner et al, 2013; Stone and Veatch, 2015; Pageon et al, 2016a, 2016b; Hummer et al, 2016; Lagache et al, 2018; Banerjee et al, 2020; Heydarian et al, 2021; Marenda et al, 2021). Since our described method can be paired with any existing downstream SMLM data analysis algorithm, it presents a useful and modular way to improve SMLM analysis results e.g. by suppressing randomly localized noise localizations and by providing a refined comparison between clustered and nonclustered localization
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