TY - JOUR A1 - Berberich, Andreas A1 - Kurz, Andreas A1 - Reinhard, Sebastian A1 - Paul, Torsten Johann A1 - Burd, Paul Ray A1 - Sauer, Markus A1 - Kollmannsberger, Philip T1 - Fourier Ring Correlation and anisotropic kernel density estimation improve deep learning based SMLM reconstruction of microtubules JF - Frontiers in Bioinformatics N2 - 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. KW - dSTORM KW - deep learning–artificial neural network (DL-ANN) KW - single molecule localization microscopy KW - microtubule cytoskeleton KW - super-resolution Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-261686 VL - 1 ER - TY - JOUR A1 - Deng, Chunchu A1 - Reinhard, Sebastian A1 - Hennlein, Luisa A1 - Eilts, Janna A1 - Sachs, Stefan A1 - Doose, Sören A1 - Jablonka, Sibylle A1 - Sauer, Markus A1 - Moradi, Mehri A1 - Sendtner, Michael T1 - Impaired dynamic interaction of axonal endoplasmic reticulum and ribosomes contributes to defective stimulus-response in spinal muscular atrophy JF - Translational Neurodegeneration N2 - 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. KW - spinal muscular atrophy KW - BDNF stimulation KW - dynamics of ribosomal assembly KW - presynaptic ER dynamics Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-300649 SN - 2047-9158 VL - 11 IS - 1 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 - Trinks, Nora A1 - Reinhard, Sebastian A1 - Drobny, Matthias A1 - Heilig, Linda A1 - Löffler, Jürgen A1 - Sauer, Markus A1 - Terpitz, Ulrich T1 - Subdiffraction-resolution fluorescence imaging of immunological synapse formation between NK cells and A. fumigatus by expansion microscopy JF - Communications Biology N2 - 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. KW - biological fluorescence KW - fluorescence imaging KW - imaging the immune system KW - infectious diseases KW - super-resolution microscopy Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-264996 VL - 4 IS - 1 ER - TY - JOUR A1 - Geiger, Nina A1 - Kersting, Louise A1 - Schlegel, Jan A1 - Stelz, Linda A1 - Fähr, Sofie A1 - Diesendorf, Viktoria A1 - Roll, Valeria A1 - Sostmann, Marie A1 - König, Eva-Maria A1 - Reinhard, Sebastian A1 - Brenner, Daniela A1 - Schneider-Schaulies, Sibylle A1 - Sauer, Markus A1 - Seibel, Jürgen A1 - Bodem, Jochen T1 - The acid ceramidase is a SARS-CoV-2 host factor JF - Cells N2 - 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. KW - SARS-CoV-2 KW - ceramides KW - ceramidase KW - fluoxetine KW - acid sphingomyelinase Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-286105 SN - 2073-4409 VL - 11 IS - 16 ER -