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Max-Delbrück Center for Molecular Medicine

5 ARTICLES PUBLISHED IN JoVE

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Biology

Cell Tracking Using Photoconvertible Proteins During Zebrafish Development
Verónica A. Lombardo 1, Anje Sporbert 1, Salim Abdelilah-Seyfried 1
1Max Delbrück Center for Molecular Medicine

Here, we present a method for the photoactivated switch of photoconvertible fluorescent proteins (PCFPs) in the living zebrafish embryo and further tracking of photoconverted protein at specific time points during development. This methodology allows monitoring of cell biological events underlying different developmental processes in a live vertebrate organism.

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Immunology and Infection

Highly Resolved Intravital Striped-illumination Microscopy of Germinal Centers
Zoltan Cseresnyes *1,2, Laura Oehme *3, Volker Andresen 4, Anje Sporbert 2, 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.

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Developmental Biology

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
Sandra Zehentmeier 1, Zoltan Cseresnyes 2,3, Juan Escribano Navarro 4, Raluca A. Niesner 2, 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.

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Genetics

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Sarah E. Ashley 1,2, Braydon A. Meyer 2,3, Justine A. Ellis 2,3,4, David J. Martino 2,3,5
1Molecular Genetics of Chronic Inflammation and Allergic Disease, Max-Delbrück Center for Molecular Medicine, 2Murdoch Childrens Research Institute, 3Department of Paediatrics, University of Melbourne, 4Centre for Social and Early Emotional Development, Faculty of Health, Deakin University, 5Department of Paediatrics, University of Western Australia

Identification of genetic variants contributing to complex human disease allows us to identify novel mechanisms. Here, we demonstrate a multiplex genotyping approach to candidate genes or gene pathway analysis that maximizes the coverage at low cost and is amenable to cohort-based studies.

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JoVE Core

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
Fabian L. Kriegel 1,2, Ralf Köhler 2, Jannike Bayat-Sarmadi 2, Simon Bayerl 3, Anja E. Hauser 2,3, Raluca Niesner 2, 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.

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