@article{BerberichKurzReinhardetal.2021,
  author    = {Berberich, Andreas and Kurz, Andreas and Reinhard, Sebastian and Paul, Torsten Johann and Burd, Paul Ray and Sauer, Markus and Kollmannsberger, Philip},
  title     = {Fourier Ring Correlation and anisotropic kernel density estimation improve deep learning based SMLM reconstruction of microtubules},
  series = {Frontiers in Bioinformatics},
  volume    = {1},
  journal   = {Frontiers in Bioinformatics},
  doi       = {10.3389/fbinf.2021.752788},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-261686},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@article{KuhlemannBeliuJanzenetal.2021,
  author    = {Kuhlemann, Alexander and Beliu, Gerti and Janzen, Dieter and Petrini, Enrica Maria and Taban, Danush and Helmerich, Dominic A. and Doose, S{\"o}ren and Bruno, Martina and Barberis, Andrea and Villmann, Carmen and Sauer, Markus and Werner, Christian},
  title     = {Genetic Code Expansion and Click-Chemistry Labeling to Visualize GABA-A Receptors by Super-Resolution Microscopy},
  series = {Frontiers in Synaptic Neuroscience},
  volume    = {13},
  journal   = {Frontiers in Synaptic Neuroscience},
  issn      = {1663-3563},
  doi       = {10.3389/fnsyn.2021.727406},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-251035},
  year      = {2021},
  abstract  = {Fluorescence labeling of difficult to access protein sites, e.g., in confined compartments, requires small fluorescent labels that can be covalently tethered at well-defined positions with high efficiency. Here, we report site-specific labeling of the extracellular domain of γ-aminobutyric acid type A (GABA-A) receptor subunits by genetic code expansion (GCE) with unnatural amino acids (ncAA) combined with bioorthogonal click-chemistry labeling with tetrazine dyes in HEK-293-T cells and primary cultured neurons. After optimization of GABA-A receptor expression and labeling efficiency, most effective variants were selected for super-resolution microscopy and functionality testing by whole-cell patch clamp. Our results show that GCE with ncAA and bioorthogonal click labeling with small tetrazine dyes represents a versatile method for highly efficient site-specific fluorescence labeling of proteins in a crowded environment, e.g., extracellular protein domains in confined compartments such as the synaptic cleft.},
  language  = {en}
}
@phdthesis{Schlegel2021,
  author    = {Schlegel, Jan},
  title     = {Super-Resolution Microscopy of Sphingolipids and Protein Nanodomains},
  doi       = {10.25972/OPUS-22959},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229596},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2021},
  abstract  = {The development of cellular life on earth is coupled to the formation of lipid-based biological membranes. Although many tools to analyze their biophysical properties already exist, their variety and number is still relatively small compared to the field of protein studies. One reason for this, is their small size and complex assembly into an asymmetric tightly packed lipid bilayer showing characteristics of a two-dimensional heterogenous fluid. Since membranes are capable to form dynamic, nanoscopic domains, enriched in sphingolipids and cholesterol, their detailed investigation is limited to techniques which access information below the diffraction limit of light. In this work, I aimed to extend, optimize and compare three different labeling approaches for sphingolipids and their subsequent analysis by the single-molecule localization microscopy (SMLM) technique direct stochastic optical reconstruction microscopy (dSTORM). First, I applied classical immunofluorescence by immunoglobulin G (IgG) antibody labeling to detect and quantify sphingolipid nanodomains in the plasma membrane of eukaryotic cells. I was able to identify and characterize ceramide-rich platforms (CRPs) with a size of ~ 75nm on the basal and apical membrane of different cell lines. Next, I used click-chemistry to characterize sphingolipid analogs in living and fixed cells. By using a combination of fluorescence microscopy and anisotropy experiments, I analyzed their accessibility and configuration in the plasma membrane, respectively. Azide-modified, short fatty acid side chains, were accessible to membrane impermeable dyes and localized outside the hydrophobic membrane core. In contrast, azide moieties at the end of longer fatty acid side chains were less accessible and conjugated dyes localized deeper within the plasma membrane. By introducing photo-crosslinkable diazirine groups or chemically addressable amine groups, I developed methods to improve their immobilization required for dSTORM. Finally, I harnessed the specific binding characteristics of non-toxic shiga toxin B subunits (STxBs) and cholera toxin B subunits (CTxBs) to label and quantify glycosphingolipid nanodomains in the context of Neisseria meningitidis infection. Under pyhsiological conditions, these glycosphingolipids were distributed homogenously in the plasma membrane but upon bacterial infection CTxB detectable gangliosides accumulated around invasive Neisseria meningitidis. I was able to highlight the importance of cell cycle dependent glycosphingolipid expression for the invasion process. Blocking membrane accessible sugar headgroups by pretreatment with CTxB significantly reduced the number of invasive bacteria which confirmed the importance of gangliosides for bacterial uptake into cells. Based on my results, it can be concluded that labeling of sphingolipids should be carefully optimized depending on the research question and applied microscopy technique. In particular, I was able to develop new tools and protocols which enable the characterization of sphingolipid nanodomains by dSTORM for all three labeling approaches.},
  subject      = {Sphingolipide},
  language  = {en}
}