Biology subjects

Grussmayer, K.

Publications and source records attributed to Grussmayer, K..

3 recordsLinked to original sources

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↗

bioRxiv2019

Identifying microbial species by single-molecule DNA optical mapping and resampling statistics

Single molecule DNA mapping has the potential to serve as a powerful complement to high-throughput sequencing in metagenomic analysis. Offering longer read lengths and forgoing the need for complex library preparation and amplification, mapping stands to provide an unbiased view into the composition of complex viromes and/or microbiomes. To fully enable mapping-based metagenomics, sensitivity and specificity of DNA map analysis and identification need to be improved. Using detailed simulations and experimental data, we first demonstrate how fluorescence imaging of surface stretched, sequence specifically labeled DNA fragments can yield highly sensitive identification of targets. Secondly, a new analysis technique is introduced to increase specificity of the analysis, allowing even closely related species to be resolved. Thirdly, we show how an increase in resolution improves sensitivity. Finally, we demonstrate that these methods are capable of identifying species with long genomes such as bacteria with high sensitivity.

genomics↗