Leibniz Institute for Natural Product Research and Infection Biology Hans Knöll Institute

3 ARTICLES PUBLISHED IN JoVE

Immunology and Infection

Highly Resolved Intravital Striped-illumination Microscopy of Germinal Centers

Zoltan Cseresnyes^*1,2, Laura Oehme^*3, Volker Andresen⁴, Anje Sporbert², Anja E. Hauser^*3,5, Raluca Niesner^*1

¹Biophysical Analytics, German Rheumatism Research Center, Leibniz Institute, ²Microscopy Core Facility, Max-Delbrück Center for Molecular Medicine, ³Immunodynamics, German Rheumatism Research Center, Leibniz Institute, ⁴LaVision Biotec GmbH, ⁵Immunodynamics and Intravital Imaging, Charité - University of Medicine

High-resolution intravital imaging with enhanced contrast up to 120 µm depth in lymph nodes of adult mice is achieved by spatially modulating the excitation pattern of a multi-focal two-photon microscope. In 100 µm depth we measured resolutions of 487 nm (lateral) and 551 nm (axial), thus circumventing scattering and diffraction limits.

Developmental Biology

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Sandra Zehentmeier¹, Zoltan Cseresnyes^2,3, Juan Escribano Navarro⁴, Raluca A. Niesner², Anja E. Hauser^1,5

¹Immunodynamics, German Rheumatism Research Center, a Leibniz Institute, ²Biophysical Analytics, German Rheumatism Research Center, a Leibniz Institute, ³Max-Delbrück Center for Molecular Medicine, ⁴Wimasis GmbH, ⁵Immunodynamics and Intravital Imaging, Charité - University of Medicine

A strategy to quantitatively analyze histological data in the bone marrow is presented. Confocal microscopy of fluorescently labeled cells in tissue sections results in 2-dimensional images, which are automatically analyzed. Co-localization analyses of different cell types are compared to data from simulated images, giving quantitative information about cellular interactions.

JoVE Core

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Fabian L. Kriegel^1,2, Ralf Köhler², Jannike Bayat-Sarmadi², Simon Bayerl³, Anja E. Hauser^2,3, Raluca Niesner², Andreas Luch^*1, Zoltan Cseresnyes^*4

¹Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), ²Deutsches Rheuma-Forschungszentrum (DRFZ) Berlin, a Leibniz Institute, ³Charité Universitätsmedizin Berlin, ⁴Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology Hans Knöll Institute

Here, we provide a workflow that allows the identification of healthy and pathological cells based on their 3-dimensional shape. We describe the process of using 2D projection outlines based on the 3D surfaces to train a Self-Organizing Map that will provide objective clustering of the investigated cell populations.