Abstract
Ripley’s K function was developed to analyze the spatial distribution characteristics in point pattern analysis, including geography, economics and biomedical research. In biomedical applications, it is popularly used to analyze the clusters of proteins on the cell plasma membrane in single molecule localization microscopy (SMLM), such as photo activated localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), universal point accumulation imaging in nanoscale topography (uPAINT), etc. Here, by varying the parameters of the simulated clusters on a modeled SMLM image, the effects of cluster size, cluster separation and protein ratio inside/outside the cluster on the accuracy of cluster analysis by analyzing Ripley’s K function were studied. Although the predicted radius of clusters by analyzing Ripley’s K function did not exactly correspond to the actual radius, we suggest the cluster radius could be estimated within a factor of 1.3. Employing peak analysis methods to analyze the experimental epidermal growth factor receptor (EGFR) clusters at fibroblast-like cell lines derived from monkey kidney tissue - COS7 cell surface observed by uPAINT method, the cluster properties were characterized with errors. Our results present quantification of clusters and can be used to enhance the understanding of clusters in SMLM.
Highlights
Single molecule localization microscopy (SMLM) is one of the most widely used imaging tools in molecular biology
Growing evidence suggests that the protein clustering at the cell membrane, one of the most important organization states, is associated with many cell functional changes and diseases: overexpression and clustering of epidermal growth factor receptor (EGFR) are observed in many cancers [4,5,6]; clustering of nicotinic acetylcholine receptors in high concentrations is critical for muscle function [7,8]; clustering of ion channels (Na+, Ka+, Ca2+ channels) is essential for signal transduction [9,10,11,12]; etc
The results indicated a nice agreement with the expectations [16]; the measured average radius of clusters was Rmes cluster = 0, which meant that no clustering occurred (Supplementary, Figure S1)
Summary
Single molecule localization microscopy (SMLM) is one of the most widely used imaging tools in molecular biology. Benefitting from the advantage of sub-diffraction-limit spatial resolution by localization of individual molecular blinking events from sequential detections, SMLM has been popularly used to characterize the spatial organization of membrane proteins [1,2,3]. The clusters detected by SMLM are commonly analyzed by Ripley’s K function (see in Materials and Methods) to give detailed information. In this approach, by analyzing the localization of proteins in a two dimensional super-resolution SMLM image, clustering is firstly identified if the density of proteins within a distance r of another protein is greater than that expected for a spatial point pattern distributed randomly within the same distance [13]
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