a 2-color 100 nm circular (purple) and square (green) clusters with inter-cluster distances of 0, 50 and 125 nm (scale bar 50 nm). Voronoï-based colocalization analysis of 2-color simulation data. Moreover, we demonstrated that the relative molecular densities between the two channels don’t influence the Manders coefficients (Fig. The normalized 1st rank density \(\widehat = 1\) for all the channels, both for the simulated and the experimental data. Our method relies on the computation and overlay of the Voronoï diagrams of two independent color channels (Fig. They have proven to be efficient at quantifying biological data with very different molecular organizations in a robust and automatic manner 14, 15, 16, 17, by comparing the local molecular density with the average density of a complete spatially random distribution. Tessellation-based methods, such as SR-Tesseler 12 and ClusterVisu 13, have been recently introduced to quantify SMLM data from the molecules’ coordinates. Tesselation-based colocalization analysis We validate our method on 2D and 3D synthetic data as well as on experimental λSMLM data of tubulin and nuclear pore complexes in mammalian cells, and actin cytoskeleton regulators in neuronal synapses. Compared with existing localization-based solutions, it is very efficient in terms of computation speed and it comes with a powerful graphical user interface enabling user interactive feedback at the single localization level, making it an ideal tool for routine colocalization analysis of biological data. It allows computing the popular image-based colocalization quantifiers, such as the Manders and Spearman’s coefficients, directly from the molecular coordinates in a straightforward manner. Coloc-Tesseler relies on the normalized pair-density parameter computed from the overlapping Voronoï diagrams of the two molecular species to quantify their spatial co-organization in a robust to density and parameter free manner. We here present a simple and efficient parameter-free colocalization method, called Coloc-Tesseler (CT), using polytopes (polygons in 2D or polyhedrons in 3D) embedding the localizations to compute the molecular co-organization of 2- and 3-dimensional λSMLM data. However, while these approaches are quite robust to the local molecular density, they all require model-dependent parameters which may be difficult to tune and strongly influence the colocalization values. Getis and Franklin (GF) 8, coordinate-based colocalization (CBC) 9 and cluster detection with degree of colocalization (ClusDoc) 10 methods all employ a user defined local distance parameter, combined either with the Getis and Franklin function 8, the Spearman rank correlation 9, 10 or the density-based spatial clustering of applications with noise (DBSCAN) 11 to quantify the level of colocalization around each localization. 6, 7 used an extension of the bivariate K-Ripley’s function computed on previously segmented clusters’ barycenters to determine the most likely interaction distance between each molecular specie. Recently, several coordinate-based techniques have emerged to compute the colocalization directly from the molecule coordinates 6, 7, 8, 9, 10. The first biological applications used the popular image-based colocalization analysis to quantify the level of interaction between two fluorescent markers at the pixel level 4, 5. However, while λSMLM can be acquired in routine, performing robust quantitative colocalization analysis still remains a challenging problem. Multicolor SMLM (λSMLM) enables investigating the relative organization and potential interaction between several subcellular components at the nanoscale. Over the last decade, single-molecule localization microscopy 1, 2, 3 (SMLM) has revolutionized cell biology, making it possible to decipher the nanoscale organization of fluorescently labelled proteins.
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