TY - JOUR A1 - Lichter, Katharina A1 - Paul, Mila Marie A1 - Pauli, Martin A1 - Schoch, Susanne A1 - Kollmannsberger, Philip A1 - Stigloher, Christian A1 - Heckmann, Manfred A1 - Sirén, Anna-Leena T1 - Ultrastructural analysis of wild-type and RIM1α knockout active zones in a large cortical synapse JF - Cell Reports N2 - Rab3A-interacting molecule (RIM) is crucial for fast Ca\(^{2+}\)-triggered synaptic vesicle (SV) release in presynaptic active zones (AZs). We investigated hippocampal giant mossy fiber bouton (MFB) AZ architecture in 3D using electron tomography of rapid cryo-immobilized acute brain slices in RIM1α\(^{−/−}\) and wild-type mice. In RIM1α\(^{−/−}\), AZs are larger with increased synaptic cleft widths and a 3-fold reduced number of tightly docked SVs (0–2 nm). The distance of tightly docked SVs to the AZ center is increased from 110 to 195 nm, and the width of their electron-dense material between outer SV membrane and AZ membrane is reduced. Furthermore, the SV pool in RIM1α\(^{−/−}\) is more heterogeneous. Thus, RIM1α, besides its role in tight SV docking, is crucial for synaptic architecture and vesicle pool organization in MFBs. KW - active zone KW - acute brain slices KW - CA3 KW - electron tomography KW - high-pressure freezing KW - hippocampal mossy fiber bouton KW - RIM1α KW - SV pool KW - synaptic ultrastructure KW - presynaptic Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-300913 VL - 40 IS - 12 ER - TY - JOUR A1 - Reinhard, Sebastian A1 - Helmerich, Dominic A. A1 - Boras, Dominik A1 - Sauer, Markus A1 - Kollmannsberger, Philip T1 - ReCSAI: recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy JF - BMC Bioinformatics N2 - 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. KW - compressed sensing KW - AI KW - SMLM KW - FLIMbee KW - dSTORM Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-299768 VL - 23 IS - 1 ER - TY - JOUR A1 - Dannhäuser, Sven A1 - Mrestani, Achmed A1 - Gundelach, Florian A1 - Pauli, Martin A1 - Komma, Fabian A1 - Kollmannsberger, Philip A1 - Sauer, Markus A1 - Heckmann, Manfred A1 - Paul, Mila M. T1 - Endogenous tagging of Unc-13 reveals nanoscale reorganization at active zones during presynaptic homeostatic potentiation JF - Frontiers in Cellular Neuroscience N2 - Introduction Neurotransmitter release at presynaptic active zones (AZs) requires concerted protein interactions within a dense 3D nano-hemisphere. Among the complex protein meshwork the (M)unc-13 family member Unc-13 of Drosophila melanogaster is essential for docking of synaptic vesicles and transmitter release. Methods We employ minos-mediated integration cassette (MiMIC)-based gene editing using GFSTF (EGFP-FlAsH-StrepII-TEV-3xFlag) to endogenously tag all annotated Drosophila Unc-13 isoforms enabling visualization of endogenous Unc-13 expression within the central and peripheral nervous system. Results and discussion Electrophysiological characterization using two-electrode voltage clamp (TEVC) reveals that evoked and spontaneous synaptic transmission remain unaffected in unc-13\(^{GFSTF}\) 3rd instar larvae and acute presynaptic homeostatic potentiation (PHP) can be induced at control levels. Furthermore, multi-color structured-illumination shows precise co-localization of Unc-13\(^{GFSTF}\), Bruchpilot, and GluRIIA-receptor subunits within the synaptic mesoscale. Localization microscopy in combination with HDBSCAN algorithms detect Unc-13\(^{GFSTF}\) subclusters that move toward the AZ center during PHP with unaltered Unc-13\(^{GFSTF}\) protein levels. KW - active zone KW - Unc-13 KW - MiMIC KW - presynaptic homeostasis KW - nanoarchitecture KW - localization microscopy KW - STORM KW - HDBSCAN Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-299440 SN - 1662-5102 VL - 16 ER - TY - JOUR A1 - Marquardt, André A1 - Kollmannsberger, Philip A1 - Krebs, Markus A1 - Argentiero, Antonella A1 - Knott, Markus A1 - Solimando, Antonio Giovanni A1 - Kerscher, Alexander Georg T1 - Visual clustering of transcriptomic data from primary and metastatic tumors — dependencies and novel pitfalls JF - Genes N2 - Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP)) on three datasets of metastases (n = 682 samples) with three different data transformations (unprocessed, log10 as well as log10 + 1 transformed values), we visualized potential underlying clusters. Additionally, we analyzed two datasets (n = 616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters. KW - visual clustering KW - t-SNE KW - UMAP KW - transcriptomic analysis KW - cancer KW - metastasis Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-281872 SN - 2073-4425 VL - 13 IS - 8 ER -