@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}
}
@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{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}
}
@phdthesis{Franke2019,
  author    = {Franke, Christian},
  title     = {Advancing Single-Molecule Localization Microscopy: Quantitative Analyses and Photometric Three-Dimensional Imaging},
  doi       = {10.25972/OPUS-15635},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-156355},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2019},
  abstract  = {Since its first experimental implementation in 2005, single-molecule localization microscopy (SMLM) emerged as a versatile and powerful imaging tool for biological structures with nanometer resolution. By now, SMLM has compiled an extensive track-record of novel insights in sub- and inter- cellular organization.\\ Moreover, since all SMLM techniques rely on the analysis of emission patterns from isolated fluorophores, they inherently allocate molecular information \$per\$ \$definitionem\$.\\ Consequently, SMLM transitioned from its origin as pure high-resolution imaging instrument towards quantitative microscopy, where the key information medium is no longer the highly resolved image itself, but the raw localization data set.\\ The work presented in this thesis is part of the ongoing effort to translate those \$per\$ \$se\$ molecular information gained by SMLM imaging to insights into the structural organization of the targeted protein or even beyond. Although largely consistent in their objectives, the general distinction between global or segmentation clustering approaches on one side and particle averaging or meta-analyses techniques on the other is usually made.\\ During the course of my thesis, I designed, implemented and employed numerous quantitative approaches with varying degrees of complexity and fields of application.\\ \\ In my first major project, I analyzed the localization distribution of the integral protein gp210 of the nuclear pore complex (NPC) with an iterative \textit{k}-means algorithm. Relating the distinct localization statistics of separated gp210 domains to isolated fluorescent signals led, among others, to the conclusion that the anchoring ring of the NPC consists of 8 homo-dimers of gp210.\\ This is of particular significance, both because it answered a decades long standing question about the nature of the gp210 ring and it showcased the possibility to gain structural information well beyond the resolution capabilities of SMLM by crafty quantification approaches.\\ \\ The second major project reported comprises an extensive study of the synaptonemal complex (SNC) and linked cohesin complexes. Here, I employed a multi-level meta-analysis of the localization sets of various SNC proteins to facilitate the compilation of a novel model of the molecular organization of the major SNC components with so far unmatched extend and detail with isotropic three-dimensional resolution.\\ In a second venture, the two murine cohesin components SMC3 and STAG3 connected to the SNC were analyzed. Applying an adapted algorithm, considering the disperse nature of cohesins, led to the realization that there is an apparent polarization of those cohesin complexes in the SNC, as well as a possible sub-structure of STAG3 beyond the resolution capabilities of SMLM.\\ \\ Other minor projects connected to localization quantification included the study of plasma membrane glycans regarding their overall localization distribution and particular homogeneity as well as the investigation of two flotillin proteins in the membrane of bacteria, forming clusters of distinct shapes and sizes.\\ \\ Finally, a novel approach to three-dimensional SMLM is presented, employing the precise quantification of single molecule emitter intensities. This method, named TRABI, relies on the principles of aperture photometry which were improved for SMLM.\\ With TRABI it was shown, that widely used Gaussian fitting based localization software underestimates photon counts significantly. This mismatch was utilized as a \$z\$-dependent parameter, enabling the conversion of 2D SMLM data to a virtual 3D space. Furthermore it was demonstrated, that TRABI can be combined beneficially with a multi-plane detection scheme, resulting in superior performance regarding axial localization precision and resolution.\\ Additionally, TRABI has been subsequently employed to photometrically characterize a novel dye for SMLM, revealing superior photo-physical properties at the single-molecule level.\\ Following the conclusion of this thesis, the TRABI method and its applications remains subject of diverse ongoing research.},
  subject      = {Einzelmolek{\"u}lmikroskopie},
  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}
}
@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}
}