@phdthesis{Aufmkolk2018, author = {Aufmkolk, Sarah}, title = {Super-Resolution Microscopy of Synaptic Proteins}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-151976}, school = {Universit{\"a}t W{\"u}rzburg}, pages = {X, 97}, year = {2018}, abstract = {The interaction of synaptic proteins orchestrate the function of one of the most complex organs, the brain. The multitude of molecular elements influencing neurological correlations makes imaging processes complicated since conventional fluorescence microscopy methods are unable to resolve structures beyond the diffraction-limit. The implementation of super-resolution fluorescence microscopy into the field of neuroscience allows the visualisation of the fine details of neural connectivity. The key element of my thesis is the super-resolution technique dSTORM (direct Stochastic Optical Reconstruction Microscopy) and its optimisation as a multi-colour approach. Capturing more than one target, I aim to unravel the distribution of synaptic proteins with nanometer precision and set them into a structural and quantitative context with one another. Therefore dSTORM specific protocols are optimized to serve the peculiarities of particular neural samples. In one project the brain derived neurotrophic factor (BDNF) is investigated in primary, hippocampal neurons. With a precision beyond 15 nm, preand post-synaptic sites can be identified by staining the active zone proteins bassoon and homer. As a result, hallmarks of mature synapses can be exhibited. The single molecule sensitivity of dSTORM enables the measurement of endogenous BDNF and locates BDNF granules aligned with glutamatergic pre-synapses. This data proofs that hippocampal neurons are capable of enriching BDNF within the mature glutamatergic pre-synapse, possibly influencing synaptic plasticity. The distribution of the metabotropic glutamate receptor mGlu4 is investigated in physiological brain slices enabling the analysis of the receptor in its natural environment. With dual-colour dSTORM, the spatial arrangement of the mGlu4 receptor in the pre-synaptic sites of parallel fibres in the molecular layer of the mouse cerebellum is visualized, as well as a four to six-fold increase in the density of the receptor in the active zone compared to the nearby environment. Prior functional measurements show that metabotropic glutamate receptors influence voltage-gated calcium channels and proteins that are involved in synaptic vesicle priming. Corresponding dSTORM data indeed suggests that a subset of the mGlu4 receptor is correlated with the voltage-gated calcium channel Cav2.1 on distances around 60 nm. These results are based on the improvement of the direct analysis of localisation data. Tools like coordinated based correlation analysis and nearest neighbour analysis of clusters centroids are used complementary to map protein connections of the synapse. Limits and possible improvements of these tools are discussed to foster the quantitative analysis of single molecule localisation microscopy data. Performing super-resolution microscopy on complex samples like brain slices benefits from a maximised field of view in combination with the visualisation of more than two targets to set the protein of interest in a cellular context. This challenge served as a motivation to establish a workflow for correlated structured illumination microscopy (SIM) and dSTORM. The development of the visualisation software coSIdSTORM promotes the combination of these powerful super-resolution techniques even on separated setups. As an example, synapses in the cerebellum that are affiliated to the parallel fibres and the dendrites of the Purkinje cells are identified by SIM and the protein bassoon of those pre-synapses is visualised threedimensionally with nanoscopic precision by dSTORM. In this work I placed emphasis on the improvement of multi-colour super-resolution imaging and its analysing tools to enable the investigation of synaptic proteins. The unravelling of the structural arrangement of investigated proteins supports the building of a synapse model and therefore helps to understand the relation between structure and function in neural transmission processes.}, subject = {Hochaufl{\"o}sende Mikroskopie}, language = {en} } @phdthesis{Wolter2014, author = {Wolter, Steve}, title = {Single-molecule localization algorithms in super-resolution microscopy}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-109370}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2014}, abstract = {Lokalisationsmikroskopie ist eine Methodenklasse der superaufl{\"o}senden Fluoreszenzmikroskopie, deren Methoden sich durch stochastische zeitliche Isolation der Fluoreszenzemission auszeichnen. Das Blinkverhalten von Fluorophoren wird so ver{\"a}ndert, dass gleichzeitige Aktivierung von einander nahen Fluorophoren unwahrscheinlich ist. Bekannte okalisationsmikroskopische Methoden umfassen dSTORM, STORM, PALM, FPALM, oder GSDIM. Lokalisationsmikroskopie ist von hohem biologischem Interesse, weil sie die Aufl{\"o}sung des Fluoreszenzmikroskops bei minimalem technischem Aufwand um eine Gr{\"o}ßenordnung verbessert. Der verbundene Rechenaufwand ist allerdings erheblich, da Millionen von Fluoreszenzemissionen einzeln mit Nanometergenauigkeit lokalisiert werden m{\"u}ssen. Der Rechen- und Implementationsaufwand dieser Auswertung hat die Verbreitung der superaufl{\"o}senden Mikroskopie lange verz{\"o}gert. Diese Arbeit beschreibt meine algorithmische Grundstruktur f{\"u}r die Auswertung lokalisationsmikroskopischer Daten. Die Echtzeitf{\"a}higkeit, d.h. eine Auswertegeschwindigkeit oberhalb der Datenaufnahmegeschwindigkeit an normalen Messaufbauten, meines neuartigen und quelloffenen Programms wird demonstriert. Die Geschwindigkeit wird auf verbrauchermarktg{\"a}ngigen Prozessoren erreicht und dadurch spezialisierte Rechenzentren oder der Einsatz von Grafikkarten vermieden. Die Berechnung wird mit dem allgemein anerkannten Gaussschen Punktantwortmodell und einem Rauschmodell auf Basis der gr{\"o}ßten Poissonschen Wahrscheinlichkeit durchgef{\"u}hrt. Die algorithmische Grundstruktur wird erweitert, um robuste und optimale Zweifarbenauswertung zu realisieren und damit korrelative Mikroskopie zwischen verschiedenen Proteinen und Strukturen zu erm{\"o}glichen. Durch den Einsatz von kubischen Basissplines wird die Auswertung von dreidimensionalen Proben vereinfacht und stabilisiert, um pr{\"a}zisem Abbilden von mikrometerdicken Proben n{\"a}her zu kommen. Das Grenzverhalten von Lokalisationsalgorithmen bei hohen Emissionsdichten wird untersucht. Abschließend werden Algorithmen f{\"u}r die Anwendung der Lokalisationsmikroskopie auf verbreitete Probleme der Biologie aufgezeigt. Zellul{\"a}re Bewegung und Motilit{\"a}t werden anhand der in vitro Bewegung von Myosin-Aktin-Filamenten studiert. Lebendzellbildgebung mit hellen und stabilen organischen Fluorophoren wird mittels SNAP-tag-Fusionsproteinen realisiert. Die Analyse des Aufbaus von Proteinklumpen zeigt, wie Lokalisationsmikroskopie neue quantitative Ans{\"a}tze jenseits reiner Bildgebung bietet.}, subject = {Fluoreszenzmikroskopie}, 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} } @phdthesis{Letschert2019, author = {Letschert, Sebastian}, title = {Quantitative Analysis of Membrane Components using Super-Resolution Microscopy}, doi = {10.25972/OPUS-16213}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-162139}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2019}, abstract = {The plasma membrane is one of the most thoroughly studied and at the same time most complex, diverse, and least understood cellular structures. Its function is determined by the molecular composition as well as the spatial arrangement of its components. Even after decades of extensive membrane research and the proposal of dozens of models and theories, the structural organization of plasma membranes remains largely unknown. Modern imaging tools such as super-resolution fluorescence microscopy are one of the most efficient techniques in life sciences and are widely used to study the spatial arrangement and quantitative behavior of biomolecules in fixed and living cells. In this work, direct stochastic optical reconstruction microscopy (dSTORM) was used to investigate the structural distribution of mem-brane components with virtually molecular resolution. Key issues are different preparation and staining strategies for membrane imaging as well as localization-based quantitative analyses of membrane molecules. An essential precondition for the spatial and quantitative analysis of membrane components is the prevention of photoswitching artifacts in reconstructed localization microscopy images. Therefore, the impact of irradiation intensity, label density and photoswitching behavior on the distribution of plasma membrane and mitochondrial membrane proteins in dSTORM images was investigated. It is demonstrated that the combination of densely labeled plasma membranes and inappropriate photoswitching rates induces artificial membrane clusters. Moreover, inhomogeneous localization distributions induced by projections of three-dimensional membrane structures such as microvilli and vesicles are prone to generate artifacts in images of biological membranes. Alternative imaging techniques and ways to prevent artifacts in single-molecule localization microscopy are presented and extensively discussed. Another central topic addresses the spatial organization of glycosylated components covering the cell membrane. It is shown that a bioorthogonal chemical reporter system consisting of modified monosaccharide precursors and organic fluorophores can be used for specific labeling of membrane-associated glycoproteins and -lipids. The distribution of glycans was visualized by dSTORM showing a homogeneous molecule distribution on different mammalian cell lines without the presence of clusters. An absolute number of around five million glycans per cell was estimated and the results show that the combination of metabolic labeling, click chemistry, and single-molecule localization microscopy can be efficiently used to study cell surface glycoconjugates. In a third project, dSTORM was performed to investigate low-expressing receptors on cancer cells which can act as targets in personalized immunotherapy. Primary multiple myeloma cells derived from the bone marrow of several patients were analyzed for CD19 expression as potential target for chimeric antigen receptor (CAR)-modified T cells. Depending on the patient, 60-1,600 CD19 molecules per cell were quantified and functional in vitro tests demonstrate that the threshold for CD19 CAR T recognition is below 100 CD19 molecules per target cell. Results are compared with flow cytometry data, and the important roles of efficient labeling and appropriate control experiments are discussed.}, subject = {Fluoreszenzmikroskopie}, 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{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} } @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{EndesfelderMalkuschFlottmannetal.2011, author = {Endesfelder, Ulrike and Malkusch, Sebastian and Flottmann, Benjamin and Mondry, Justine and Liguzinski, Piotr and Verveer, Peter J. and Heilemann, Mike}, title = {Chemically Induced Photoswitching of Fluorescent Probes - A General Concept for Super-Resolution Microscopy}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-74896}, year = {2011}, abstract = {We review fluorescent probes that can be photoswitched or photoactivated and are suited for single-molecule localization based super-resolution microscopy. We exploit the underlying photochemical mechanisms that allow photoswitching of many synthetic organic fluorophores in the presence of reducing agents, and study the impact of these on the photoswitching properties of various photoactivatable or photoconvertible fluorescent proteins. We have identified mEos2 as a fluorescent protein that exhibits reversible photoswitching under various imaging buffer conditions and present strategies to characterize reversible photoswitching. Finally, we discuss opportunities to combine fluorescent proteins with organic fluorophores for dual-color photoswitching microscopy.}, subject = {Super-Resolution Microscopy}, language = {en} } @article{EndesfelderMalkuschFlottmannetal.2011, author = {Endesfelder, Ulrike and Malkusch, Sebastian and Flottmann, Benjamin and Mondry, Justine and Liguzinski, Piotr and Verveer, Peter J. and Heilemann, Mike}, title = {Chemically Induced Photoswitching of Fluorescent Probes - A General Concept for Super-Resolution Microscopy}, series = {Molecules}, volume = {16}, journal = {Molecules}, number = {4}, doi = {10.3390/molecules16043106}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-134080}, pages = {3106-3118}, year = {2011}, abstract = {We review fluorescent probes that can be photoswitched or photoactivated and are suited for single-molecule localization based super-resolution microscopy. We exploit the underlying photochemical mechanisms that allow photoswitching of many synthetic organic fluorophores in the presence of reducing agents, and study the impact of these on the photoswitching properties of various photoactivatable or photoconvertible fluorescent proteins. We have identified mEos2 as a fluorescent protein that exhibits reversible photoswitching under various imaging buffer conditions and present strategies to characterize reversible photoswitching. Finally, we discuss opportunities to combine fluorescent proteins with organic fluorophores for dual-color photoswitching microscopy.}, language = {en} } @article{PaulPauliEhmannetal.2015, author = {Paul, Mila M. and Pauli, Martin and Ehmann, Nadine and Hallermann, Stefan and Sauer, Markus and Kittel, Robert J. and Heckmann, Manfred}, title = {Bruchpilot and Synaptotagmin collaborate to drive rapid glutamate release and active zone differentiation}, series = {Frontiers in Cellular Neuroscience}, volume = {9}, journal = {Frontiers in Cellular Neuroscience}, number = {29}, doi = {10.3389/fncel.2015.00029}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-148988}, year = {2015}, abstract = {The active zone (AZ) protein Bruchpilot (Brp) is essential for rapid glutamate release at Drosophila melanogaster neuromuscular junctions (NMJs). Quantal time course and measurements of action potential-waveform suggest that presynaptic fusion mechanisms are altered in brp null mutants (brp\(^{69}\)). This could account for their increased evoked excitatory postsynaptic current (EPSC) delay and rise time (by about 1 ms). To test the mechanism of release protraction at brp\(^{69}\) AZs, we performed knock-down of Synaptotagmin-1 (Syt) via RNAi (syt\(^{KD}\)) in wildtype (wt), brp\(^{69}\) and rab3 null mutants (rab3\(^{rup}\)), where Brp is concentrated at a small number of AZs. At wt and rab3\(^{rup}\) synapses, syt\(^{KD}\) lowered EPSC amplitude while increasing rise time and delay, consistent with the role of Syt as a release sensor. In contrast, syt\(^{KD}\) did not alter EPSC amplitude at brp\(^{69}\) synapses, but shortened delay and rise time. In fact, following syt\(^{KD}\), these kinetic properties were strikingly similar in wt and brp\(^{69}\), which supports the notion that Syt protracts release at brp\(^{69}\) synapses. To gain insight into this surprising role of Syt at brp\(^{69}\) AZs, we analyzed the structural and functional differentiation of synaptic boutons at the NMJ. At tonic type Ib motor neurons, distal boutons contain more AZs, more Brp proteins per AZ and show elevated and accelerated glutamate release compared to proximal boutons. The functional differentiation between proximal and distal boutons is Brp-dependent and reduced after syt\(^{KD}\). Notably, syt\(^{KD}\) boutons are smaller, contain fewer Brp positive AZs and these are of similar number in proximal and distal boutons. In addition, super-resolution imaging via dSTORM revealed that syt\(^{KD}\) increases the number and alters the spatial distribution of Brp molecules at AZs, while the gradient of Brp proteins per AZ is diminished. In summary, these data demonstrate that normal structural and functional differentiation of Drosophila AZs requires concerted action of Brp and Syt.}, language = {en} }