TY - JOUR A1 - Derakhshani, Shaghayegh A1 - Kurz, Andreas A1 - Japtok, Lukasz A1 - Schumacher, Fabian A1 - Pilgram, Lisa A1 - Steinke, Maria A1 - Kleuser, Burkhard A1 - Sauer, Markus A1 - Schneider-Schaulies, Sibylle A1 - Avota, Elita T1 - Measles virus infection fosters dendritic cell motility in a 3D environment to enhance transmission to target cells in the respiratory epithelium JF - Frontiers in Immunology N2 - Transmission of measles virus (MV) from dendritic to airway epithelial cells is considered as crucial to viral spread late in infection. Therefore, pathways and effectors governing this process are promising targets for intervention. To identify these, we established a 3D respiratory tract model where MV transmission by infected dendritic cells (DCs) relied on the presence of nectin-4 on H358 lung epithelial cells. Access to recipient cells is an important prerequisite for transmission, and we therefore analyzed migration of MV-exposed DC cultures within the model. Surprisingly, enhanced motility toward the epithelial layer was observed for MV-infected DCs as compared to their uninfected siblings. This occurred independently of factors released from H358 cells indicating that MV infection triggered cytoskeletal remodeling associated with DC polarization enforced velocity. Accordingly, the latter was also observed for MV-infected DCs in collagen matrices and was particularly sensitive to ROCK inhibition indicating infected DCs preferentially employed the amoeboid migration mode. This was also implicated by loss of podosomes and reduced filopodial activity both of which were retained in MV-exposed uninfected DCs. Evidently, sphingosine kinase (SphK) and sphingosine-1-phosphate (S1P) as produced in response to virus-infection in DCs contributed to enhanced velocity because this was abrogated upon inhibition of sphingosine kinase activity. These findings indicate that MV infection promotes a push-and-squeeze fast amoeboid migration mode via the SphK/S1P system characterized by loss of filopodia and podosome dissolution. Consequently, this enables rapid trafficking of virus toward epithelial cells during viral exit. KW - dendritic cell KW - cell migration KW - measles virus KW - 3D tissue model KW - sphingosine-1-phosphate Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-201818 VL - 10 IS - 1294 ER - TY - JOUR A1 - Berberich, Andreas A1 - Kurz, Andreas A1 - Reinhard, Sebastian A1 - Paul, Torsten Johann A1 - Burd, Paul Ray A1 - Sauer, Markus A1 - Kollmannsberger, Philip T1 - Fourier Ring Correlation and anisotropic kernel density estimation improve deep learning based SMLM reconstruction of microtubules JF - Frontiers in Bioinformatics N2 - Single-molecule super-resolution microscopy (SMLM) techniques like dSTORM can reveal biological structures down to the nanometer scale. The achievable resolution is not only defined by the localization precision of individual fluorescent molecules, but also by their density, which becomes a limiting factor e.g., in expansion microscopy. Artificial deep neural networks can learn to reconstruct dense super-resolved structures such as microtubules from a sparse, noisy set of data points. This approach requires a robust method to assess the quality of a predicted density image and to quantitatively compare it to a ground truth image. Such a quality measure needs to be differentiable to be applied as loss function in deep learning. We developed a new trainable quality measure based on Fourier Ring Correlation (FRC) and used it to train deep neural networks to map a small number of sampling points to an underlying density. Smooth ground truth images of microtubules were generated from localization coordinates using an anisotropic Gaussian kernel density estimator. We show that the FRC criterion ideally complements the existing state-of-the-art multiscale structural similarity index, since both are interpretable and there is no trade-off between them during optimization. The TensorFlow implementation of our FRC metric can easily be integrated into existing deep learning workflows. KW - dSTORM KW - deep learning–artificial neural network (DL-ANN) KW - single molecule localization microscopy KW - microtubule cytoskeleton KW - super-resolution Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-261686 VL - 1 ER - TY - JOUR A1 - Böck, Julia A1 - Maurus, Katja A1 - Gerhard-Hartmann, Elena A1 - Brändlein, Stephanie A1 - Kurz, Katrin S. A1 - Ott, German A1 - Anagnostopoulos, Ioannis A1 - Rosenwald, Andreas A1 - Zamò, Alberto T1 - Targeted panel sequencing in the routine diagnosis of mature T- and NK-cell lymphomas BT - report of 128 cases from two German reference centers JF - Frontiers in Oncology N2 - Diagnosing any of the more than 30 types of T-cell lymphomas is considered a challenging task for many pathologists and currently requires morphological expertise as well as the integration of clinical data, immunophenotype, flow cytometry and clonality analyses. Even considering all available information, some margin of doubt might remain using the current diagnostic procedures. In recent times, the genetic landscape of most T-cell lymphomas has been elucidated, showing a number of diagnostically relevant mutations. In addition, recent data indicate that some of these genetic alterations might bear prognostic and predictive value. Extensive genetic analyses, such as whole exome or large panel sequencing are still expensive and time consuming, therefore limiting their application in routine diagnostic. We therefore devoted our effort to develop a lean approach for genetic analysis of T-cell lymphomas, focusing on maximum efficiency rather than exhaustively covering all possible targets. Here we report the results generated with our small amplicon-based panel that could be used routinely on paraffin-embedded and even decalcified samples, on a single sample basis in parallel with other NGS-panels used in our routine diagnostic lab, in a relatively short time and with limited costs. We tested 128 available samples from two German reference centers as part of our routine work up (among which 116 T-cell lymphomas), which is the largest routine diagnostic series reported to date. Our results showed that this assay had a very high rate of technical success (97%) and could detect mutations in the majority (79%) of tested T-cell lymphoma samples. KW - T-cell lymphoma KW - panel-sequencing KW - NGS KW - diagnostics KW - mutation KW - FFPE Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-326478 SN - 2234-943X VL - 13 ER -