@article{MrestaniLichterSirenetal.2023,
  author    = {Mrestani, Achmed and Lichter, Katharina and Sir{\´e}n, Anna-Leena and Heckmann, Manfred and Paul, Mila M. and Pauli, Martin},
  title     = {Single-molecule localization microscopy of presynaptic active zones in Drosophila melanogaster after rapid cryofixation},
  series = {International Journal of Molecular Sciences},
  volume    = {24},
  journal   = {International Journal of Molecular Sciences},
  number    = {3},
  issn      = {1422-0067},
  doi       = {10.3390/ijms24032128},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-304904},
  year      = {2023},
  abstract  = {Single-molecule localization microscopy (SMLM) greatly advances structural studies of diverse biological tissues. For example, presynaptic active zone (AZ) nanotopology is resolved in increasing detail. Immunofluorescence imaging of AZ proteins usually relies on epitope preservation using aldehyde-based immunocompetent fixation. Cryofixation techniques, such as high-pressure freezing (HPF) and freeze substitution (FS), are widely used for ultrastructural studies of presynaptic architecture in electron microscopy (EM). HPF/FS demonstrated nearer-to-native preservation of AZ ultrastructure, e.g., by facilitating single filamentous structures. Here, we present a protocol combining the advantages of HPF/FS and direct stochastic optical reconstruction microscopy (dSTORM) to quantify nanotopology of the AZ scaffold protein Bruchpilot (Brp) at neuromuscular junctions (NMJs) of Drosophila melanogaster. Using this standardized model, we tested for preservation of Brp clusters in different FS protocols compared to classical aldehyde fixation. In HPF/FS samples, presynaptic boutons were structurally well preserved with ~22\% smaller Brp clusters that allowed quantification of subcluster topology. In summary, we established a standardized near-to-native preparation and immunohistochemistry protocol for SMLM analyses of AZ protein clusters in a defined model synapse. Our protocol could be adapted to study protein arrangements at single-molecule resolution in other intact tissue preparations.},
  language  = {en}
}
@phdthesis{Reinhard2023,
  author    = {Reinhard, Sebastian},
  title     = {Improving Super-Resolution Microscopy Data Reconstruction and Evaluation by Developing Advanced Processing Algorithms and Artifcial Neuronal Networks},
  doi       = {10.25972/OPUS-31695},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-316959},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {The fusion of methods from several disciplines is a crucial component of scientific development. Artificial Neural Networks, based on the principle of biological neuronal networks, demonstrate how nature provides the best templates for technological advancement. These innovations can then be employed to solve the remaining mysteries of biology, including, in particular, processes that take place on microscopic scales and can only be studied with sophisticated techniques. For instance, direct Stochastic Optical Reconstruction Microscopy combines tools from chemistry, physics, and computer science to visualize biological processes at the molecular level. One of the key components is the computer-aided reconstruction of super-resolved images. Improving the corresponding algorithms increases the quality of the generated data, providing further insights into our biology. It is important, however, to ensure that the heavily processed images are still a reflection of reality and do not originate in random artefacts. Expansion microscopy is expanding the sample by embedding it in a swellable hydrogel. The method can be combined with other super-resolution techniques to gain additional resolution. We tested this approach on microtubules, a well-known filamentous reference structure, to evaluate the performance of different protocols and labelling techniques. We developed LineProfiler an objective tool for data collection. Instead of collecting perpendicular profiles in small areas, the software gathers line profiles from filamentous structures of the entire image. This improves data quantity, quality and prevents a biased choice of the evaluated regions. On the basis of the collected data, we deployed theoretical models of the expected intensity distribution across the filaments. This led to the conclusion that post-expansion labelling significantly reduces the labelling error and thus, improves the data quality. The software was further used to determine the expansion factor and arrangement of synaptonemal complex data. Automated Simple Elastix uses state-of-the-art image alignment to compare pre- and post-expansion images. It corrects linear distortions occurring under isotropic expansion, calculates a structural expansion factor and highlights structural mismatches in a distortion map. We used the software to evaluate expanded fungi and NK cells. We found that the expansion factor differs for the two structures and is lower than the overall expansion of the hydrogel. Assessing the fluorescence lifetime of emitters used for direct Stochastic Optical Reconstruction Microscopy can reveal additional information about the molecular environment or distinguish dyes emitting with a similar wavelength. The corresponding measurements require a confocal scanning of the sample in combination with the fluorescent switching of the underlying emitters. This leads to non-linear, interrupted Point Spread Functions. The software ReCSAI targets this problem by combining the classical algorithm of compressed sensing with modern methods of artificial intelligence. We evaluated several different approaches to combine these components and found, that unrolling compressed sensing into the network architecture yields the best performance in terms of reconstruction speed and accuracy. In addition to a deep insight into the functioning and learning of artificial intelligence in combination with classical algorithms, we were able to reconstruct the described non-linearities with significantly improved resolution, in comparison to other state-of-the-art architectures.},
  subject      = {Mikroskopie},
  language  = {en}
}
@article{ReinhardHelmerichBorasetal.2022,
  author    = {Reinhard, Sebastian and Helmerich, Dominic A. and Boras, Dominik and Sauer, Markus and Kollmannsberger, Philip},
  title     = {ReCSAI: recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy},
  series = {BMC Bioinformatics},
  volume    = {23},
  journal   = {BMC Bioinformatics},
  number    = {1},
  doi       = {10.1186/s12859-022-05071-5},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-299768},
  year      = {2022},
  abstract  = {Background Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a measured photon distribution. These algorithms make assumptions about the symmetry of the PSF and thus, do not work well with irregular, non-linear PSFs that occur for example in confocal lifetime imaging, where a laser is scanned across the sample. An alternative method for reconstructing sparse emitter sets from noisy, diffraction-limited images is compressed sensing, but due to its high computational cost it has not yet been widely adopted. Deep neural network fitters have recently emerged as a new competitive method for localization microscopy. They can learn to fit arbitrary PSFs, but require extensive simulated training data and do not generalize well. A method to efficiently fit the irregular PSFs from confocal lifetime localization microscopy combining the advantages of deep learning and compressed sensing would greatly improve the acquisition speed and throughput of this method. Results Here we introduce ReCSAI, a compressed sensing neural network to reconstruct localizations for confocal dSTORM, together with a simulation tool to generate training data. We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. We found that a U-Net with a recursive structure inspired by iterative compressed sensing showed the best results on realistic simulated datasets with noise, as well as on real experimentally measured confocal lifetime scanning data. Adding a trainable wavelet denoising layer as prior step further improved the reconstruction quality. Conclusions Our deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisition of multiple frames. In addition, our work offers generic insights on the reconstruction of sparse measurements from noisy experimental data by combining compressed sensing and deep learning. We provide the trained networks, the code for network training and inference as well as the simulation tool as python code and Jupyter notebooks for easy reproducibility.},
  language  = {en}
}
@techreport{Gross2022,
  author    = {Groß, Lennart},
  title     = {Advices derived from troubleshooting a sensor-based adaptive optics direct stochastic optical reconstruction microscope},
  doi       = {10.25972/OPUS-28995},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-289951},
  pages     = {20},
  year      = {2022},
  abstract  = {One rarely finds practical guidelines for the implementation of complex optical setups. Here, we aim to provide technical details on the decision making of building and revising a custom sensor-based adaptive optics (AO) direct stochastic optical reconstruction microscope (dSTORM) to provide practical assistance in setting up or troubleshooting similar devices. The foundation of this report is an instrument constructed as part of a master's thesis in 2021, which was built for deep tissue imaging. The setup is presented in the following way: (1) An optical and mechanical overview of the system at the beginning of this internship is given. (2) The optical components are described in detail in the order at which the light passes through, highlighting their working principle and implementation in the system. The optical component include (2A) a focus on even sample illumination, (2B) restoring telecentricity when working with commercial microscope bodies, (2C) the AO elements, namely the deformable mirror (DM) and the wavefront sensor, and their integration, and (2D) the separation of wavefront and image capture using fluorescent beads and a dichroic mirror. After addressing the limitations of the existing setup, modification options are derived. The modifications include the implementation of adjustment only light paths to improve system stability and revise the degrees of freedom of the components and changes in lens choices to meet the specifications of the AO components. Last, the capabilities of the modified setup are presented and discussed: (1) First, we enable epifluorescence imaging of bead samples through 180 µm unstained murine hippocampal tissue with wavefront error correction of ~ 90 \%. Point spread function, wavefront shape and Zernike decomposition of bead samples are presented. (2) Second, we move from epifluorescent to dSTORM imaging of tubulin stained primary mouse hippocampal cells, which are imaged through up to 180 µm of unstained murine hippocampal tissue. We show that full width at half maximum (FWHM) of prominent features can be reduced in size by nearly a magnitude from uncorrected epiflourescence images to dSTORM images corrected by the adaptive optics. We present dSTORM localization count and FWHM of prominent features as as a function of imaging depth.},
  subject      = {Einzelmolek{\"u}lmikroskopie},
  language  = {en}
}
@misc{Gross2022,
  type      = {Master Thesis},
  author    = {Groß, Lennart},
  title     = {Point-spread function engineering for single-molecule localization microscopy in brain slices},
  doi       = {10.25972/OPUS-28259},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-282596},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Single-molecule localization microscopy (SMLM) is the method of choice to study biological specimens on a nanoscale level. Advantages of SMLM imply its superior specificity due to targeted molecular fluorescence labeling and its enhanced tissue preservation compared to electron microscopy, while reaching similar resolution. To reveal the molecular organization of protein structures in brain tissue, SMLM moves to the forefront: Instead of investigating brain slices with a thickness of a few µm, measurements of intact neuronal assemblies (up to 100 µm in each dimension) are required. As proteins are distributed in the whole brain volume and can move along synapses in all directions, this method is promising in revealing arrangements of neuronal protein markers. However, diffraction-limited imaging still required for the localization of the fluorophores is prevented by sample-induced distortion of emission pattern due to optical aberrations in tissue slices from non-superficial planes. In particular, the sample causes wavefront dephasing, which can be described as a summation of Zernike polynomials. To recover an optimal point spread function (PSF), active shaping can be performed by the use of adaptive optics. The aim of this thesis is to establish a setup using a deformable mirror and a wavefront sensor to actively shape the PSF to correct the wavefront phases in a super-resolution microscope setup. Therefore, fluorescence-labeled proteins expressed in different anatomical regions in brain tissue will be used as experiment specimen. Resolution independent imaging depth in slices reaching tens of micrometers is aimed.},
  subject      = {Einzelmolek{\"u}lmikroskopie},
  language  = {en}
}
@phdthesis{Karus2022,
  author    = {Karus, Christine},
  title     = {Untersuchung der Architektur von Proteinstrukturen des Ranvier-Schn{\"u}rrings mittels der super-hochaufl{\"o}senden Mikroskopiemethode dSTORM},
  doi       = {10.25972/OPUS-27456},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-274568},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Ranvier-Schn{\"u}rringe spielen eine entscheidende Rolle bei der schnellen Weiterleitung von elektrischen Impulsen in Nervenzellen. Bei bestimmten neurologischen Erkrankungen, den Neuropathien, kann es zu St{\"o}rungen in der ultrastrukturellen Organisation verschiedener Schn{\"u}rring-Proteine kommen (Doppler et al., 2018, Doppler et al., 2016). Eine detailliertere Kenntnis der genauen Anordnung dieser Schn{\"u}rring-Proteine und eventueller Abweichungen von dieser Anordnung im Krankheitsfall, k{\"o}nnte der Schl{\"u}ssel zu einer vereinfachten Diagnostik von bestimmten Neuropathie- Formen sein. Ziel meiner Arbeit war es daher, die Untersuchung der ultrastrukturellen Architektur der (para-)nodalen Adh{\"a}sionsproteine Neurofascin-155 und Caspr1 unter Verwendung der super-hochaufl{\"o}senden Mikroskopiemethode dSTORM (direct Stochastic Optical Reconstruction Microscopy) an murinen Zupfnervenpr{\"a}paraten zu etablieren. Nach erster Optimierung der Probenpr{\"a}paration f{\"u}r die 2-Farben-dSTORM sowie der korrelationsbasierten Bildanalyse, konnte ich mittels modellbasierter Simulation die zugrundeliegende Molek{\"u}lorganisation identifizieren und mit Hilfe der Ergebnisse aus fr{\"u}heren Untersuchungen validieren. In einem translationalen Ansatz habe ich anschließend humane Zupfnervenpr{\"a}parate von 14 Probanden mit unterschiedlichen Formen einer Neuropathie mikroskopiert und ausgewertet, um die Anwendbarkeit dieses Ansatzes in der Diagnostik zu testen. Obgleich keine signifikanten Unterschiede zwischen physiologischem und pathologischem neurologischem Gewebe hinsichtlich Neurofascin-155 und Caspr1 festgestellt werden konnten, scheint der Ansatz grunds{\"a}tzlich dennoch vielversprechend zu sein, bedarf jedoch noch weiteren Anstrengungen hinsichtlich Probenpr{\"a}paration, Auswertungs- und Versuchsprotokollen und einer gr{\"o}ßeren Anzahl an humanen Biopsien mit homogenerem Krankheitsbild.},
  language  = {de}
}
@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}
}
@phdthesis{Waeldchen2020,
  author    = {W{\"a}ldchen, Sina},
  title     = {Super-Resolution-Mikroskopie zur Visualisierung und Quantifizierung von Glutamatrezeptoren und ADHS-assoziierten Proteinen},
  doi       = {10.25972/OPUS-19283},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-192834},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2020},
  abstract  = {Die Entwicklung hochaufl{\"o}sender Fluoreszenzmikroskopiemethoden hat die Lichtmikroskopie revolutioniert. Einerseits erm{\"o}glicht die h{\"o}here erzielte r{\"a}umliche Aufl{\"o}sung die Abbildung von Strukturen, die deutlich unterhalb der beugungsbedingten Aufl{\"o}sungsgrenze liegen. Andererseits erh{\"a}lt man durch Einzelmolek{\"u}llokalisationsmikroskopiemethoden wie dSTORM (Direct Stochastic Optical Reconstruction Microscopy) Informationen, welche man f{\"u}r quantitative Analysen heranziehen kann. Aufgrund der sich dadurch bietenden neuen M{\"o}glichkeiten, hat sich die hochaufl{\"o}sende Fluoreszenzmikroskopie rasant entwickelt und kommt mittlerweile zur Untersuchung einer Vielzahl biologischer und medizinischer Fragestellungen zum Einsatz. Trotz dieses Erfolgs ist jedoch nicht zu verleugnen, dass auch diese neuen Methoden ihre Nachteile haben. Dazu z{\"a}hlt die Notwendigkeit relativ hoher Laserleistungen, welche Voraussetzung f{\"u}r hohe Aufl{\"o}sung ist und bei lebenden Proben zur Photosch{\"a}digung f{\"u}hren kann. Diese Arbeit widmet sich sowohl dem Thema der Photosch{\"a}digung durch Einzelmolek{\"u}llokalisationsmikroskopie, als auch der Anwendung von dSTORM und SIM (Structured Illumination Microscopy) zur Untersuchung neurobiologischer Fragestellungen auf Proteinebene. Zur Ermittlung der Photosch{\"a}digung wurden lebende Zellen unter typischen Bedingungen bestrahlt und anschließend f{\"u}r 20-24 h beobachtet. Als quantitatives Maß f{\"u}r den Grad der Photosch{\"a}digung wurde der Anteil sterbender Zellen bestimmt. Neben der zu erwartenden Intensit{\"a}ts- und Wellenl{\"a}ngenabh{\"a}ngigkeit, zeigte sich, dass die Schwere der Photosch{\"a}digung auch von vielen weiteren Faktoren abh{\"a}ngt und dass sich Einzelmolek{\"u}llokalisationsmikroskopie bei Ber{\"u}cksichtigung der gewonnenen Erkenntnisse durchaus mit Lebendzellexperimenten vereinbaren l{\"a}sst. Ein weiteres Projekt diente der Untersuchung der A- und B-Typ-Glutamatrezeptoren an der neuromuskul{\"a}ren Synapse von Drosophila melanogaster mittels dSTORM. Dabei konnte eine ver{\"a}nderte Anordnung beider Rezeptortypen infolge synaptischer Plastizit{\"a}t beobachtet, sowie eine absolute Quantifizierung des A-Typ-Rezeptors durchgef{\"u}hrt werden. Im Mittelpunkt eines dritten Projekts standen Cadherin-13 (CDH13) sowie der Glucosetransporter Typ 3 (GluT3), welche beide mit der Aufmerksamkeitsdefizit-Hyperaktivit{\"a}tsst{\"o}rung in Verbindung gebracht werden. CDH13 konnte mittels SIM in serotonergen Neuronen, sowie radi{\"a}ren Gliazellen der dorsalen Raphekerne des embryonalen Mausgehirns nachgewiesen werden. Die Rolle von GluT3 wurde in aus induzierten pluripotenten Stammzellen differenzierten Neuronen analysiert, welche verschiedene Kopienzahlvariation des f{\"u}r GluT3-codierenden SLC2A3-Gens aufwiesen. Die Proteine GluT3, Bassoon und Homer wurden mittels dSTORM relativ quantifiziert. W{\"a}hrend die Deletion des Gens zu einer erwartenden Verminderung von GluT3 auf Proteinebene f{\"u}hrte, hatte die Duplikation keinen Effekt auf die GluT3-Menge. F{\"u}r Bassoon und Homer zeigte sich weder durch die Deletion noch die Duplikation eine signifikante Ver{\"a}nderung.},
  subject      = {Mikroskopie},
  language  = {de}
}