Hallgrimson, C. | Search bioRxiv

bioRxiv2024

SuperResNET single molecule localization microscopy model-free network analysis of Nup96 achieves molecular resolution

SuperResNET is an integrated machine learning-based analysis software for visualizing and quantifying 3D point cloud data acquired by single molecule localization microscopy (SMLM). The computational modules of SuperResNET include correction for multiple blinking of a single fluorophore, denoising, segmentation (clustering), and feature extraction, which are then used for cluster group identification, modularity analysis, blob retrieval and visualization in 2D and 3D. Using publicly available dSTORM data, we apply a graphical user interface (GUI) version of SuperResNET to nucleoporin Nup96 structures, that present a highly organized octagon structure comprised of eight corners. SuperResNET GUI effectively segments nuclear pores and Nup96 corners based on differential proximity threshold analysis. SuperResNET GUI quantitatively analyzes features from segmented nuclear pore structures, including complete structures with 8-fold symmetry, and from segmented corners. SuperResNET GUI modularity analysis of segmented corners distinguishes two modules at 11.1 nm distance, corresponding to two individual Nup96 molecules. SuperResNET GUI is therefore a model-free tool that can reconstruct network architecture and molecular distribution of subcellular structures without the bias of a specified prior model, attaining molecular resolution from dSTORM data. SuperResNET GUI provides flexibility to report on structural diversity in situ within the cell without model-fitting, providing opportunities for biological discovery.

cell biology↗

bioRxiv2024

Super-resolution single molecule network analysis (SuperResNET) detects changes to clathrin structure by small molecule inhibitors

Specificity of small molecules for their target molecule in the cell is critical to determine their effective use as biologics and therapeutics. Small molecule inhibitors of clathrin endocytosis, Pitstop 2, and the dynamin inhibitor Dynasore, have off-target effects and their specificity has been challenged. Here, we used SuperResNET to apply network analysis to 20 nm resolution dSTORM single-molecule localization microscopy (SMLM) to test whether Pitstop 2 and Dynasore alter the morphology of clathrin coated pits in intact cells. SuperResNET analysis of dSTORM data from HeLa and Cos7 cells identifies three classes of clathrin structures: small oligomers (Class I); pits and vesicles (Class II); and larger clusters corresponding to fused clathrin pits and clathrin plaques (Class III). SuperResNET analysis of high resolution MinFlux imaging identifies Class 1 oligomers as well as Class 2 structures including morphologically identifiable clathrin pits and vesicles. SuperResNET feature analysis of dSTORM data shows that Pitstop 2 and Dynasore induce the formation of distinct homogenous populations of clathrin structures in HeLa cells. Pitstop 2 blobs are smaller and more elongated than those induced by Dynasore, indicating that these two clathrin inhibitors arrest clathrin endocytosis at distinct stages. Pitstop 2 and Dynasore are not impacting clathrin structure via actin depolymerization as the actin depolymerizing agent latrunculin A (LatA) induced larger heterogeneous clathrin structures. Ternary analysis of SuperResNET shape features presents a distinct profile for Pitstop 2 Class II structures. The most representative Pitstop blobs align with and resemble MinFlux clathrin pits while control structures resemble Minflux clathrin vesicles. SuperResNET analysis of SMLM data is therefore a highly sensitive approach to detect the effect of small molecules on target molecule structure in situ in the cell.

cell biology↗