Light microscopy

Our service group hosts a broad range of light microscopes which are operated as shared equipment and are open for all members of the institute. This includes conventional widefield epifluorescence microscopes (Z1 Observer, Z1 Imager), automated screening microscopes (Cellomics ArrayScan, Celldiscoverer 7, Celldiscoverer 7 with confocal unit), confocal laser scanning microscopes (LSM700, 1x LSM880 with Fast-Airyscan detector, 1x LSM800 with Fast-Airyscan detector, 2-photon laser and NDD Big2 detector), one STED super-resolution microscope (Stellaris Tau-STED), and one 3D light sheet microscope. Furthermore, we support additional instruments located in individual departments (Observer 7, Dept. Meissner; Z1 Observer and V16 Stereo-Zoom, Dept. Herrmann) and other equipment such as basic cell culture and stereo microscopes, which can be accessed by users upon request.

Besides technical support, the mission our group is supporting all users in performing their imaging experiments, might it be a simple routine task or the implementation of a complex imaging workflow including sophisticated data analysis pipelines. In fact, rather than offering a standard service we think of service as a highly interactive process for developing and supporting a full imaging workflow that involves planning, sample preparation, data acquisition, image processing and data analysis up to visualization, statistical analysis and data interpretation (Figure 1).



Frankly speaking, there is no universal imaging procedure available. A simple widefield experiment might be fast and sufficient to detect a certain signal, but might not provide enough resolution to localize it unambiguously. Confocal microscopy might provide higher resolution, but the high laser intensity might bleach the sample and prevent long-term live cell imaging. In particular, imaging large three-dimensional samples requires careful balancing sensitivity, light intensity, resolution, and speed, but also elaborate optimization of sample preparation including sample staining and clearing. Once experiments are running, we are open to discuss the results to further optimize individual steps including sample preparation, data acquisition, image processing as well as data analysis and interpretation.

Within recent projects, we implemented a broad range of imaging techniques including automated live cell and multiwell plate-based high-throughput screening, Airyscan super-resolution microscopy as well as 3D imaging approaches using 2-photon imaging, Fast-Airyscan confocal imaging and light sheet microscopy. Furthermore, we provide access to image processing workstations and various and a broad range of software packages such as Image J, FIJI, Zen Intellesis, Arivis Vision 4D, Cell Profiler, Columbus, and HCS Studio and Voreen amongst others. Combining image acquisition and data analysis tools, we setup various imaging pipelines driven by specific needs of individual users. One established, these workflows are then made available for a broader user community.

For example, in collaboration with Melissa Bothe (former AG Meijsing; Bothe et al., 2021, Life Sci Alliance), we established an automated pipeline for RNA and DNA-Fish imaging including acquisition of z-stacks, image segmentation, quantification and statistical analysis which is now used by several research groups throughout the institute. In a similar approach, we established a workflow to quantify phase separation events inside the nucleus accompanying gene regulation (AG Hnisz; Basu et al., 2020, Cell; Asimi et al. ,2022, Nat Genet). In collaboration with the Yaspo lab and Zeiss, we established semi-automated workflows to screen and analyze cancer sphaeroids which involve targeted acquisition schemes to identify and characterize sphaeroid formation in 3D cell culture systems as well as drug response and end point assays.  Similar approaches have been established to monitor growth, morphometry, tissue formation, and cell composition in large multidimensional data sets to study stem cell differentiation or embryogenesis using gastruloids (Bolondi et al., 2021, Bio Protoc; Veenvliet et al., 2020, Science). In short, these experiments are performed in multiwell formats, and time-series are recorded automatically using various autofocus strategies. AI-based segmentation methods are then applied to identify objects in transmitted light images, whereas fluorescence images are used for segmentation and quantification of individual cell populations. These High Content imaging and analysis strategies became a standard tool for analyzing stem cell derived gastruloids and trunk like structures as well as many other 3D model systems in the institute (Rosebrock et al., 2022, Nat Cell Biol).

Our research focus lies on the implementation and improvement of new imaging and image analysis techniques that could be of strategical interest for our users, but are not yet available at our institute. One example are correlative microscopy techniques that bridge both, light and electron microscopy (Figure 2) or targeted acquisitions schemes that combine automated screening and image analysis procedures to identify objects of interest that are than successively imaged applying confocal microscopy at higher magnification. Other examples are the implementation of super-resolutions cryo-expansion techniques, and the development of imaging methods and assay that are based on fluorescence life-time measurements (FLIM; Hochmair et al., 2022, EMBO J). Moreover, we now also aim at implementing clustering-based image analysis approaches that enable us to identify e.g. cell cycle intermediates or different cell populations from large data sets comprising information on thousands of individual cells.



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