@article{13267, abstract = {Three-dimensional (3D) reconstruction of living brain tissue down to an individual synapse level would create opportunities for decoding the dynamics and structure–function relationships of the brain’s complex and dense information processing network; however, this has been hindered by insufficient 3D resolution, inadequate signal-to-noise ratio and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine-learning technology, LIONESS (live information-optimized nanoscopy enabling saturated segmentation). This leverages optical modifications to stimulated emission depletion microscopy in comprehensively, extracellularly labeled tissue and previous information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise ratio and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D reconstruction at a synapse level, incorporating molecular, activity and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue.}, author = {Velicky, Philipp and Miguel Villalba, Eder and Michalska, Julia M and Lyudchik, Julia and Wei, Donglai and Lin, Zudi and Watson, Jake and Troidl, Jakob and Beyer, Johanna and Ben Simon, Yoav and Sommer, Christoph M and Jahr, Wiebke and Cenameri, Alban and Broichhagen, Johannes and Grant, Seth G.N. and Jonas, Peter M and Novarino, Gaia and Pfister, Hanspeter and Bickel, Bernd and Danzl, Johann G}, issn = {1548-7105}, journal = {Nature Methods}, pages = {1256--1265}, publisher = {Springer Nature}, title = {{Dense 4D nanoscale reconstruction of living brain tissue}}, doi = {10.1038/s41592-023-01936-6}, volume = {20}, year = {2023}, } @unpublished{11943, abstract = {Complex wiring between neurons underlies the information-processing network enabling all brain functions, including cognition and memory. For understanding how the network is structured, processes information, and changes over time, comprehensive visualization of the architecture of living brain tissue with its cellular and molecular components would open up major opportunities. However, electron microscopy (EM) provides nanometre-scale resolution required for full in-silico reconstruction1–5, yet is limited to fixed specimens and static representations. Light microscopy allows live observation, with super-resolution approaches6–12 facilitating nanoscale visualization, but comprehensive 3D-reconstruction of living brain tissue has been hindered by tissue photo-burden, photobleaching, insufficient 3D-resolution, and inadequate signal-to-noise ratio (SNR). Here we demonstrate saturated reconstruction of living brain tissue. We developed an integrated imaging and analysis technology, adapting stimulated emission depletion (STED) microscopy6,13 in extracellularly labelled tissue14 for high SNR and near-isotropic resolution. Centrally, a two-stage deep-learning approach leveraged previously obtained information on sample structure to drastically reduce photo-burden and enable automated volumetric reconstruction down to single synapse level. Live reconstruction provides unbiased analysis of tissue architecture across time in relation to functional activity and targeted activation, and contextual understanding of molecular labelling. This adoptable technology will facilitate novel insights into the dynamic functional architecture of living brain tissue.}, author = {Velicky, Philipp and Miguel Villalba, Eder and Michalska, Julia M and Wei, Donglai and Lin, Zudi and Watson, Jake and Troidl, Jakob and Beyer, Johanna and Ben Simon, Yoav and Sommer, Christoph M and Jahr, Wiebke and Cenameri, Alban and Broichhagen, Johannes and Grant, Seth G. N. and Jonas, Peter M and Novarino, Gaia and Pfister, Hanspeter and Bickel, Bernd and Danzl, Johann G}, booktitle = {bioRxiv}, publisher = {Cold Spring Harbor Laboratory}, title = {{Saturated reconstruction of living brain tissue}}, doi = {10.1101/2022.03.16.484431}, year = {2022}, } @article{9258, author = {Pinkard, Henry and Stuurman, Nico and Ivanov, Ivan E. and Anthony, Nicholas M. and Ouyang, Wei and Li, Bin and Yang, Bin and Tsuchida, Mark A. and Chhun, Bryant and Zhang, Grace and Mei, Ryan and Anderson, Michael and Shepherd, Douglas P. and Hunt-Isaak, Ian and Dunn, Raymond L. and Jahr, Wiebke and Kato, Saul and Royer, Loïc A. and Thiagarajah, Jay R. and Eliceiri, Kevin W. and Lundberg, Emma and Mehta, Shalin B. and Waller, Laura}, issn = {1548-7105}, journal = {Nature Methods}, number = {3}, pages = {226--228}, publisher = {Springer Nature}, title = {{Pycro-Manager: Open-source software for customized and reproducible microscope control}}, doi = {10.1038/s41592-021-01087-6}, volume = {18}, year = {2021}, } @article{6808, abstract = {Super-resolution fluorescence microscopy has become an important catalyst for discovery in the life sciences. In STimulated Emission Depletion (STED) microscopy, a pattern of light drives fluorophores from a signal-emitting on-state to a non-signalling off-state. Only emitters residing in a sub-diffraction volume around an intensity minimum are allowed to fluoresce, rendering them distinguishable from the nearby, but dark fluorophores. STED routinely achieves resolution in the few tens of nanometers range in biological samples and is suitable for live imaging. Here, we review the working principle of STED and provide general guidelines for successful STED imaging. The strive for ever higher resolution comes at the cost of increased light burden. We discuss techniques to reduce light exposure and mitigate its detrimental effects on the specimen. These include specialized illumination strategies as well as protecting fluorophores from photobleaching mediated by high-intensity STED light. This opens up the prospect of volumetric imaging in living cells and tissues with diffraction-unlimited resolution in all three spatial dimensions.}, author = {Jahr, Wiebke and Velicky, Philipp and Danzl, Johann G}, issn = {1046-2023}, journal = {Methods}, number = {3}, pages = {27--41}, publisher = {Elsevier}, title = {{Strategies to maximize performance in STimulated Emission Depletion (STED) nanoscopy of biological specimens}}, doi = {10.1016/j.ymeth.2019.07.019}, volume = {174}, year = {2020}, } @article{6029, abstract = {Protein micropatterning has become an important tool for many biomedical applications as well as in academic research. Current techniques that allow to reduce the feature size of patterns below 1 μm are, however, often costly and require sophisticated equipment. We present here a straightforward and convenient method to generate highly condensed nanopatterns of proteins without the need for clean room facilities or expensive equipment. Our approach is based on nanocontact printing and allows for the fabrication of protein patterns with feature sizes of 80 nm and periodicities down to 140 nm. This was made possible by the use of the material X-poly(dimethylsiloxane) (X-PDMS) in a two-layer stamp layout for protein printing. In a proof of principle, different proteins at various scales were printed and the pattern quality was evaluated by atomic force microscopy (AFM) and super-resolution fluorescence microscopy.}, author = {Lindner, Marco and Tresztenyak, Aliz and Fülöp, Gergö and Jahr, Wiebke and Prinz, Adrian and Prinz, Iris and Danzl, Johann G and Schütz, Gerhard J. and Sevcsik, Eva}, issn = {22962646}, journal = {Frontiers in Chemistry}, publisher = {Frontiers Media S.A.}, title = {{A fast and simple contact printing approach to generate 2D protein nanopatterns}}, doi = {10.3389/fchem.2018.00655}, volume = {6}, year = {2019}, }