Komen, J. | bioRxiv Science

bioRxiv2026

Open Blink: Low-cost TIRF microscopy for super-resolutionimaging via μManager

Super-resolution localization microscopy (SMLM) has become a central tool for nanoscale biological research for its high spatial resolution and compatibility with wide-field microscopy. Achieving quantitative SMLM, however, requires homogeneous high-power illumination, nanometric axial stability, and precise multi-channel detection, features typically restricted to high-end commercial instruments or custom solutions in specialized laboratories. The cost of such microscopes and their technical complexity still limit the accessibility of these advanced imaging techniques. Several home-made single molecule microscopes and their submodules have been demonstrated as opensource, highly-customizable, and cost-effective alternatives for their commercial counterparts. Yet, implementation of such systems often requires expert knowledge in optics, electronics, and control system engineering. We introduce Open Blink, a compact open-source TIRF microscope integrating powerful homogeneous quad-line laser illumination, dual-channel detection, and active focus-lock stabilization for quantitative multi-color super-resolution imaging. Open Blink achieves a localization precision below 10 nm in dSTORM, supports a tunable, large field of view from 105 x 105 {micro}m2 up to 257 x 257 {micro}m2, and maintains axial stability over hours, enabling high-throughput super-resolution acquisition. Built with predominantly off-the-shelf components, and full integration into the open-source software {micro}Manager where metadata registration ensures reproducibility, Open Blink offers a low threshold for adoption by easing implementation, use and maintenance. At a substantially reduced cost of approximately 70 000 Euros, among which the high-power laser combiner alone is less than 20 000 euros, Open Blink greatly improves accessibility for laboratories who wish to implement scalable high performance super-resolution microscopy based on single molecules.

biophysics↗

bioRxiv2025

Enabling Real-Time Fluctuation-Based Super ResolutionImaging

Live-cell imaging captures dynamic cellular processes, yet many structures remain beyond the diffraction limit. Fluctuation-based super-resolution techniques overcome this limit by exploiting correlations in fluorescence blinking, but they typically require hundreds of frames and computationally intensive post-processing, prohibiting real-time imaging of fast cellular events. Recent deep learning approaches aim to increase the temporal resolution; however, many rely on extensive pre-processing or large, complex models that increase training cost and inference latency, preventing real-time deployment. To address this, we employ a light-weight recurrent neural network model, which integrates sequential low-resolution frames to extract spatio-temporally correlated signals. Our method is taylored for live-cell imaging under extreme signal-to-noise ratio conditions. It significantly improves temporal resolution by reducing the required number of frames down to as few as 8 frames while doubling the spatial resolution in an inference time below 30 ms. By combining simulation based training with an efficient network architecture, we introduce RESURF, a deep-learning based real-time super-resolution fluctuation imaging framework. We demonstrate that RESURF generalizes across different biological structures and can be readily adapted to various microscope setups using transfer learning. The accompanying dataset, comprising simulations and experiments across multiple subcellular structures and labeling strategies, establishes a benchmarking platform for fluctuation-based super-resolution techniques. RESURF offers a practical, low-latency deep-learning framework for high-throughput and real-time live-cell super-resolution imaging.

biophysics↗