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Protein structures are now being resolved at the atomic level, but deciphering their molecular organization in the cell remains a challenge.
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.
More recent updates to SuperResNET allow two-channel interaction distance analysis to determine how two proteins interact within macromolecular assemblies.
SuperResNET can be effectively and easily applied to any SMLM event list from which it rapidly learns macromolecular architecture in the intact cell.
SuperResNET makes super-resolution microscopy accessible to biologists, with a GUI version for analysis of individual data sets and a batch analysis version for statistical analysis of multiple replicates and conditions.
SuperResNET is an analysis tool to discover protein architecture and structural diversity in the cell
SuperResNET is an analysis tool to discover protein architecture and structural diversity in the cell
SuperResNET Features
GUI and non-GUI batch analysis versions
Imports file formats: .bin;.ascii; .xyz; .txt; .mat; .csv
Exports high-resolution figures (tiff, png, pdf, eps…) and quantitative data (for Excel, R, Matlab, Python…)
Load and easily switch between multiple datasets
SuperResNET-specific analysis methods
Alpha filtering of random network-like blinks
Merge analysis to correct for multiple blinking
30 features (Size, Shape, Topology, Network)
Feature selection and normalization
Convex hull analysis
Modularity analysis
Dual-channel interaction distance analysis
Established Analysis Methods
Ripley’s H function
K-means and DBSCAN clustering
Mean shift segmentation
Network analysis
Visualization
2D and 3D point clouds
Networks
Pairwise feature visualization
Convex hull
Retrieval of most representative blobs
Identification of blob communities (modularity)
Paired datasets loaded simultaneously
Publications
Publications
Timothy H. Wong, Ismail M. Khater, Christian Hallgrimson, Y. Lydia Li, Ghassan Hamarneh, and Ivan Robert Nabi. SuperResNET – single-molecule network analysis detects changes to clathrin structure induced by small-molecule inhibitors (Wong and Khater: Joint first authors; Hamarneh and Nabi: Joint senior authors). Journal of Cell Science (JCS), 138(4):1-11, 2025.
Kailasam Mani, Nicolas Tardif, Olivier Rossier, Ismail Khater, Xuesi Zhou, Filipe Nunes Vicent, Radhakrishnan Av, Céline Gracia, Pamela Gonzalez Troncoso, Isabel Brito, Richard Ruez, Melissa Dewulf, Ghassan Hamarneh, Ivan Robert Nabi, Pierre Sens, Irina S Moreira, Grégory Giannone, and Cédric M Blouin, and Christophe Lamaze. Remote Control of Cell Signaling through Caveolae Mechanics. Technical report biorxiv:2024.03.12.584716, 7 2025.
Ivan Robert Nabi, Ben Cardoen, Ismail M. Khater, Guang Gao, Timothy H. Wong, and Ghassan Hamarneh. AI-based analysis of super-resolution microscopy: Biological discovery in the absence of ground truth. The Journal Of Cell Biology (JCB), Aug 5;223(8):e202311073, 2024.
Ismail M. Khater, Ivan Robert Nabi, and Ghassan Hamarneh. A Review of Super-resolution Single Molecule Localization Microscopy Cluster Analysis and Quantification Methods. Cell Patterns, 1(3):2666-3899, 2020.
Y. Lydia Li, Ismail M. Khater, Christian Hallgrimson, Timothy H. Wong, Ghassan Hamarneh, and Ivan Robert Nabi. SuperResNET: model-free single molecule network analysis software achieves molecular resolution of Nup96 (Li and Khater: Joint first authors; Hamarneh and Nabi: Joint senior authors). Advanced Intelligent Systems (AISY), 2400521:1-14, 2024.
Timothy H. Wong, Ismail M. Khater, Bharat Joshi, Mona Shahsavari, Ghassan Hamarneh, and Ivan Robert Nabi. Single molecule network analysis identifies structural changes to caveolae and scaffolds due to mutation of the caveolin-1 scaffolding domain (Wong and Khater: Joint first authors; Hamarneh and Nabi: Joint senior authors). Nature - Scientific Reports, 11(7810):1-14, 2021.
Ismail M. Khater, Qian Liu, Keng C. Chou, Ghassan Hamarneh, and Ivan Robert Nabi. Super-resolution modularity analysis shows polyhedral caveolin-1 oligomers combine to form scaffolds and caveolae. Nature - Scientific reports, 9(9888):1-10, 2019.
Tamako Nishimura and Shiro Suetsugu. Super-resolution analysis of PACSIN2 and EHD2 at caveolae. PLoS One. 2022 Jul 14;17(7):e0271003
Ismail M. Khater, Fanrui Meng, Timothy H. Wong, Ivan Robert Nabi, and Ghassan Hamarneh. Super Resolution Network Analysis Defines the Molecular Architecture of Caveolae and Caveolin-1 Scaffolds. Nature - Scientific reports, 8(9009):1-15, 2018.
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