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Abstract
Quantitative approaches to analyze the large data sets generated by single molecule localization super-resolution microscopy (SMLM) are limited. We developed a computational pipeline and applied it to analyzing 3D point clouds of SMLM localizations (event lists) of the caveolar coat protein, caveolin-1 (Cav1), in prostate cancer cells differentially expressing CAVIN1 (also known as PTRF), that is also required for caveolae formation. High degree (strongly-interacting) points were removed by an iterative blink merging algorithm and Cav1 network properties were compared with randomly generated networks to retain a sub-network of geometric structures (or blobs). Machine-learning based classification extracted 28 quantitative features describing the size, shape, topology and network characteristics of ∼80,000 blobs. Unsupervised clustering identified small S1A scaffolds corresponding to SDS-resistant Cav1 oligomers, as yet undescribed larger hemi-spherical S2 scaffolds and, only in CAVIN1-expressing cells, spherical, hollow caveolae. Multi-threshold modularity analysis suggests that S1A scaffolds interact to form larger scaffolds and that S1A dimers group together, in the presence of CAVIN1, to form the caveolae coat.
Highlights
Development of analytical tools to interpret the point distributions generated by SMLM is in its infancy[4]
Other methods focused on molecular counting by studying photoactivatable fluorescent proteins (FPs) and their blinking behavior, which is based on the photokinetic model of the FPs21,22
We modeled the SMLM data as a 3D point cloud[29,30,31], a well-established representation used in 3D visual computing
Summary
Development of analytical tools to interpret the point distributions generated by SMLM is in its infancy[4]. Griffié et al.[19,20] proposed a parameter-free cluster analysis method that is based on Bayesian prior probabilities Their method is sensitive to the prior settings and processing time to analyze one data set, 30 2D ROIs of size 3 × 3 μm[2] each at low density, is ∼19 hours, and does not scale to large SMLM datasets[19]. Virtual connections between points transform the point cloud into a network modeled computationally as a graph with nodes (or vertices); edges are connections between nodes (points) that weight the distance between nodes Such network representations have been widely and successfully adopted for analysis of brain, social and computer networks[32,33,34,35,36]. We apply point cloud network analysis to SMLM data sets to define the molecular architecture of plasma membrane-associated caveolae and caveolin-1 (Cav1) scaffolds
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