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   The availability of more specific dyes for a subset of endomembrane compartments, combined with the development of genetically encoded probes and advanced microscopy technologies, makes live cell imaging an approach that goes beyond the microscopically observation of cell structure. Here we describe the latest improved techniques to investigate protein–protein interaction, protein topology, and protein dynamics. 

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   Super-resolution imaging in confocal microscopy has traditionally relied on supervised deep-learning methods, which require large, high-quality paired datasets—a challenging and resource-intensive process. To overcome this limitation, we introduce the Cycle-Consistent Super-Resolution Confocal Scanning Microscopy (CCSRCSM) method, an unsupervised framework based on cycle-consistent generative adversarial networks (CycleGANs). The CCSRCSM method effectively translates low-resolution (LR) confocal images into super-resolved (SR) counterparts using unpaired datasets, eliminating the need for spatially aligned training data. We validated the method using confocal images of U-373MG human glioblastoma cells stained for actin and vimentin. Performance metrics, including spatial cutoff frequency, background noise analysis, and structural similarity index, demonstrate the superior fidelity of the CCSRCSM method compared to conventional image processing and deep-learning approaches. The model successfully enhances spatial frequency content and visual detail while reducing background noise, enabling the clear visualization of subcellular structures such as invadopodia and stress fibers. By leveraging unpaired data, the CCSRCSM method broadens accessibility to super-resolution microscopy, allowing low-cost confocal systems to achieve high-resolution performance without hardware modifications. 

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