@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{DengReinhardHennleinetal.2022, author = {Deng, Chunchu and Reinhard, Sebastian and Hennlein, Luisa and Eilts, Janna and Sachs, Stefan and Doose, S{\"o}ren and Jablonka, Sibylle and Sauer, Markus and Moradi, Mehri and Sendtner, Michael}, title = {Impaired dynamic interaction of axonal endoplasmic reticulum and ribosomes contributes to defective stimulus-response in spinal muscular atrophy}, series = {Translational Neurodegeneration}, volume = {11}, journal = {Translational Neurodegeneration}, number = {1}, issn = {2047-9158}, doi = {10.1186/s40035-022-00304-2}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-300649}, year = {2022}, abstract = {Background: Axonal degeneration and defects in neuromuscular neurotransmission represent a pathological hallmark in spinal muscular atrophy (SMA) and other forms of motoneuron disease. These pathological changes do not only base on altered axonal and presynaptic architecture, but also on alterations in dynamic movements of organelles and subcellular structures that are not necessarily reflected by static histopathological changes. The dynamic interplay between the axonal endoplasmic reticulum (ER) and ribosomes is essential for stimulus-induced local translation in motor axons and presynaptic terminals. However, it remains enigmatic whether the ER and ribosome crosstalk is impaired in the presynaptic compartment of motoneurons with Smn (survival of motor neuron) deficiency that could contribute to axonopathy and presynaptic dysfunction in SMA. Methods: Using super-resolution microscopy, proximity ligation assay (PLA) and live imaging of cultured motoneurons from a mouse model of SMA, we investigated the dynamics of the axonal ER and ribosome distribution and activation. Results: We observed that the dynamic remodeling of ER was impaired in axon terminals of Smn-deficient motoneurons. In addition, in axon terminals of Smn-deficient motoneurons, ribosomes failed to respond to the brain-derived neurotrophic factor stimulation, and did not undergo rapid association with the axonal ER in response to extracellular stimuli. Conclusions: These findings implicate impaired dynamic interplay between the ribosomes and ER in axon terminals of motoneurons as a contributor to the pathophysiology of SMA and possibly also other motoneuron diseases.}, language = {en} } @article{GeigerKerstingSchlegeletal.2022, author = {Geiger, Nina and Kersting, Louise and Schlegel, Jan and Stelz, Linda and F{\"a}hr, Sofie and Diesendorf, Viktoria and Roll, Valeria and Sostmann, Marie and K{\"o}nig, Eva-Maria and Reinhard, Sebastian and Brenner, Daniela and Schneider-Schaulies, Sibylle and Sauer, Markus and Seibel, J{\"u}rgen and Bodem, Jochen}, title = {The acid ceramidase is a SARS-CoV-2 host factor}, series = {Cells}, volume = {11}, journal = {Cells}, number = {16}, issn = {2073-4409}, doi = {10.3390/cells11162532}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-286105}, year = {2022}, abstract = {SARS-CoV-2 variants such as the delta or omicron variants, with higher transmission rates, accelerated the global COVID-19 pandemic. Thus, novel therapeutic strategies need to be deployed. The inhibition of acid sphingomyelinase (ASM), interfering with viral entry by fluoxetine was reported. Here, we described the acid ceramidase as an additional target of fluoxetine. To discover these effects, we synthesized an ASM-independent fluoxetine derivative, AKS466. High-resolution SARS-CoV-2-RNA FISH and RTqPCR analyses demonstrate that AKS466 down-regulates viral gene expression. It is shown that SARS-CoV-2 deacidifies the lysosomal pH using the ORF3 protein. However, treatment with AKS488 or fluoxetine lowers the lysosomal pH. Our biochemical results show that AKS466 localizes to the endo-lysosomal replication compartments of infected cells, and demonstrate the enrichment of the viral genomic, minus-stranded RNA and mRNAs there. Both fluoxetine and AKS466 inhibit the acid ceramidase activity, cause endo-lysosomal ceramide elevation, and interfere with viral replication. Furthermore, Ceranib-2, a specific acid ceramidase inhibitor, reduces SARS-CoV-2 replication and, most importantly, the exogenous supplementation of C6-ceramide interferes with viral replication. These results support the hypotheses that the acid ceramidase is a SARS-CoV-2 host factor.}, 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} } @article{TrinksReinhardDrobnyetal.2021, author = {Trinks, Nora and Reinhard, Sebastian and Drobny, Matthias and Heilig, Linda and L{\"o}ffler, J{\"u}rgen and Sauer, Markus and Terpitz, Ulrich}, title = {Subdiffraction-resolution fluorescence imaging of immunological synapse formation between NK cells and A. fumigatus by expansion microscopy}, series = {Communications Biology}, volume = {4}, journal = {Communications Biology}, number = {1}, doi = {10.1038/s42003-021-02669-y}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-264996}, year = {2021}, abstract = {Expansion microscopy (ExM) enables super-resolution fluorescence imaging on standard microscopes by physical expansion of the sample. However, the investigation of interactions between different organisms such as mammalian and fungal cells by ExM remains challenging because different cell types require different expansion protocols to ensure identical, ideally isotropic expansion of both partners. Here, we introduce an ExM method that enables super-resolved visualization of the interaction between NK cells and Aspergillus fumigatus hyphae. 4-fold expansion in combination with confocal fluorescence imaging allows us to resolve details of cytoskeleton rearrangement as well as NK cells' lytic granules triggered by contact with an RFP-expressing A. fumigatus strain. In particular, subdiffraction-resolution images show polarized degranulation upon contact formation and the presence of LAMP1 surrounding perforin at the NK cell-surface post degranulation. Our data demonstrate that optimized ExM protocols enable the investigation of immunological synapse formation between two different species with so far unmatched spatial resolution.}, language = {en} }