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 - Eiring, Patrick A1 - McLaughlin, Ryan A1 - Matikonda, Siddharth S. A1 - Han, Zhongying A1 - Grabenhorst, Lennart A1 - Helmerich, Dominic A. A1 - Meub, Mara A1 - Beliu, Gerti A1 - Luciano, Michael A1 - Bandi, Venu A1 - Zijlstra, Niels A1 - Shi, Zhen-Dan A1 - Tarasov, Sergey G. A1 - Swenson, Rolf A1 - Tinnefeld, Philip A1 - Glembockyte, Viktorija A1 - Cordes, Thorben A1 - Sauer, Markus A1 - Schnermann, Martin J. T1 - Targetable conformationally restricted cyanines enable photon-count-limited applications JF - Angewandte Chemie Internationale Edition N2 - Cyanine dyes are exceptionally useful probes for a range of fluorescence-based applications, but their photon output can be limited by trans-to-cis photoisomerization. We recently demonstrated that appending a ring system to the pentamethine cyanine ring system improves the quantum yield and extends the fluorescence lifetime. Here, we report an optimized synthesis of persulfonated variants that enable efficient labeling of nucleic acids and proteins. We demonstrate that a bifunctional sulfonated tertiary amide significantly improves the optical properties of the resulting bioconjugates. These new conformationally restricted cyanines are compared to the parent cyanine derivatives in a range of contexts. These include their use in the plasmonic hotspot of a DNA-nanoantenna, in single-molecule Förster-resonance energy transfer (FRET) applications, far-red fluorescence-lifetime imaging microscopy (FLIM), and single-molecule localization microscopy (SMLM). These efforts define contexts in which eliminating cyanine isomerization provides meaningful benefits to imaging performance. KW - biology KW - super-resolution microscopy KW - conformational restriction KW - cyanine dyes KW - DNA nanotechnology KW - fluorescent dyes KW - single-molecule fluorescence spectroscopy Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-256559 VL - 60 IS - 51 ER - TY - JOUR A1 - Kuhlemann, Alexander A1 - Beliu, Gerti A1 - Janzen, Dieter A1 - Petrini, Enrica Maria A1 - Taban, Danush A1 - Helmerich, Dominic A. A1 - Doose, Sören A1 - Bruno, Martina A1 - Barberis, Andrea A1 - Villmann, Carmen A1 - Sauer, Markus A1 - Werner, Christian T1 - Genetic Code Expansion and Click-Chemistry Labeling to Visualize GABA-A Receptors by Super-Resolution Microscopy JF - Frontiers in Synaptic Neuroscience N2 - 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. KW - super-resolution microscopy (SRM) KW - click-chemistry KW - dSTORM KW - GABA-A receptor KW - genetic code expansion Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-251035 SN - 1663-3563 VL - 13 ER -