Imaging Network – Microscopy

Tools, tips and training for microscopy – we offer individual and comprehensive support to researchers at the University of Münster and beyond in the areas of cutting-edge microscopy, reproducible image analysis, and optimal strategies for data management and publication.

Our team provides a wide range of microscopy systems featuring the latest methods and technologies for biomedical research, and supports users in their application. In addition to continuous development, our goal is to familiarise you not only with state-of-the-art microscopy techniques but also with the underlying data management practices – promoting transparency, quality, and reusability in research. To achieve this, we offer tailored advice on experimental design and image analysis, hands-on support for your experiments, and guidance for publishing your results. Together with our preclinical imaging colleagues we form the Münster Imaging Network that is embedded into the Cells in Motion Interfaculty Centre (CiM). Our network is a core facility listed in RIsources, the research infrastructure registry of the German Research Foundation (DFG). In addition, our microscopy platform is part of NFDI4BioImage, a consortium within the DFG-funded National Research Data Infrastructure (NFDI).

DeepLearning based denoising with Noise2Void

Click on the image to open it with OMERO.iviewer

Noise2Void can be used for denoising your images without the need for a ground truth. Training can be done on single noisy images in Fiji. Noise2Void can also be used if deconvolution is not possible due to unsuitable imaging parameters. Furthermore it can be used for almost all kinds of images, like EM, RGB, photographs etc. Curious what it can do for your images? You are welcome to contact us via @Mattermost or by mail imaging@uni-muenster.de .

Read the paper: Noise2Void - Learning Denoising from Single Noisy Images | Noise2Void on Github

© Thomas Zobel

Super-resolution microscopy with DNA-PAINT

Reconstruction of a DNA-PAINT imaging sequence. 20 nm DNA origami nanorulers are shown in red. The original pixelsize during detection is shown in green.

In March 2020, we participated in the "Trends in Microscopy" (TiM-2020) Summer School – an intensive and hands-on event focused on modern microscopy techniques. Among other activities, we carried out DNA-PAINT experiments during full-day practical sessions, achieving an impressive resolution of around 4 nm, despite challenging conditions in a crowded and warm microscopy room. DNA-PAINT is relatively easy to implement, and our group has since gained extensive experience with the method.

Feel free to contact us via @Mattermost or by mail imaging@uni-muenster.de .

Original paper: Super-resolution microscopy with DNA-PAINT Joerg Schnitzbauer, Maximilian T Strauss, Thomas Schlichthaerle, Florian Schueder & Ralf Jungmann Nature Protocols volume 12, pages1198–1228(2017)