@inproceedings{21135, abstract = {Three-dimensional (3D) microscopy data is often anisotropic with significantly lower resolution (up to 8x) along the z axis than along the xy axes. Computationally generating plausible isotropic resolution from anisotropic imaging data would benefit the visual analysis of large-scale volumes. This paper proposes niiv, a self-supervised method for isotropic reconstruction of 3D microscopy data that can quickly produce images at arbitrary output resolutions. The representation embeds a learned latent code within a neural field that describes the implicit higher-resolution isotropic image region. We use an attention-guided latent interpolation approach, which allows flexible information exchange over a local latent neighborhood. Under isotropic volume assumptions, we self-supervise this representation on low-/high-resolution lateral image pairs to reconstruct an isotropic volume from low-resolution axial images. We evaluate our method on simulated and real anisotropic electron (EM) and light microscopy (LM) data. Compared to diffusion-based baselines, niiv shows improved reconstruction quality (+1 dB PSNR) and is over three orders of magnitude faster (1,000x) to infer. Specifically, niiv reconstructs a 128^3 voxel volume in 2/10th of a second, renderable at varying (continuous) high resolutions for display. Our code is available at https://github.com/jakobtroidl/niiv-miccai.}, author = {Troidl, Jakob and Liang, Yiqing and Beyer, Johanna and Tavakoli, Mojtaba and Danzl, Johann G and Hadwiger, Markus and Pfister, Hanspeter and Tompkin, James}, booktitle = {1st International Workshop on Efficient Medical Artificial Intelligence}, isbn = {9783032139603}, issn = {1611-3349}, location = {Daejeon, South Korea}, pages = {257--267}, publisher = {Springer Nature}, title = {{niiv: Interactive Self-supervised Neural Implicit Isotropic Volume Reconstruction}}, doi = {10.1007/978-3-032-13961-0_26}, volume = {16318}, year = {2026}, } @article{19795, abstract = {Super-resolution microscopy often entails long acquisition times of minutes to hours. Since drifts during the acquisition adversely affect data quality, active sample stabilization is commonly used for some of these techniques to reach their full potential. Although drifts in the lateral plane can often be corrected after acquisition, this is not always possible or may come with drawbacks. Therefore, it is appealing to stabilize sample position in three dimensions (3D) during acquisition. Various schemes for active sample stabilization have been demonstrated previously, with some reaching sub-nanometer stability in 3D. Here, we present a scheme for active drift correction that delivers the nanometer-scale 3D stability demanded by state-of-the-art super-resolution techniques and is straightforward to implement compared to previous schemes capable of reaching this level of stabilization precision. Using a refined algorithm that can handle various types of reference structure, without sparse signal peaks being mandatory, we stabilized sample position to ∼1 nm in 3D using objective lenses both with high and low numerical aperture. Our implementation requires only the addition of a simple widefield imaging path and we provide an open-source control software with graphical user interface to facilitate easy adoption of the module. Finally, we demonstrate how this has the potential to enhance data collection for diffraction-limited and super-resolution imaging techniques using single-molecule localization microscopy and cryo-confocal imaging as showcases.}, author = {Vorlaufer, Jakob and Semenov, Nikolai and Kreuzinger, Caroline and Javoor, Manjunath and Zens, Bettina and Agudelo Duenas, Nathalie and Tavakoli, Mojtaba and Suplata, Marek and Jahr, Wiebke and Lyudchik, Julia and Wartak, Andreas and Schur, Florian Km and Danzl, Johann G}, issn = {2667-0747}, journal = {Biophysical Reports}, number = {2}, publisher = {Elsevier}, title = {{Image-based 3D active sample stabilization on the nanometer scale for optical microscopy}}, doi = {10.1016/j.bpr.2025.100211}, volume = {5}, year = {2025}, } @phdthesis{20206, abstract = {The internal structure of biomolecules and their organization in higher-order arrangements are key factors governing the working principles of biological systems. Bioimaging has successfully revealed arrangements across relevant spatial scales. For example, cryo-electron tomography has become widely used for analyzing biomolecular structures in situ due to its comprehensive structural visualization of near-natively preserved samples, and its capability of sub-nm resolution via averaging. However, the identification of molecules within crowded cellular environments is often hindered by low contrast. Fluorescence microscopy, on the other hand, routinely visualizes specifically labeled targets at single-molecule contrast against essentially zero background. Moreover, it provides comparatively high throughput and is amenable to multiplexing. Due to this complementarity, combining datasets from both modalities acquired on the same region via correlative light and electron microscopy can reveal novel types of information. The spatial scale at which information can be extracted depends on imaging resolution and correlation accuracy. Since diffraction of light limits the resolution of conventional fluorescence microscopy to few hundreds of nanometers, reaching the full potential of correlative imaging requires super-resolution approaches. Performing imaging at cryogenic temperature preserves structures in a near-native state and minimizes distortions between the fluorescence and the electron microscopy datasets. Implementations of this concept have achieved correlation on the scale of cellular organelles or bacterial domains. We have worked towards pushing correlative imaging to the single-molecule scale by improving cryo-super-resolution microscopy, and devising a refined image correlation workflow. As part of this project, I constructed a microscopy setup and adopted it for super-resolution fluorescence microscopy at room temperature and cryogenic conditions. I explored different cryo-stages and acquisition strategies. Specifically, I developed a new scheme for correcting sample drift, thus increasing mechanical stability during microscopy acquisitions. }, author = {Vorlaufer, Jakob}, issn = {2663-337X}, pages = {107}, publisher = {Institute of Science and Technology Austria}, title = {{Construction of a cryo-super-resolution microscope to guide in situ structure analysis}}, doi = {10.15479/AT-ISTA-20206}, year = {2025}, } @article{18879, abstract = {Our brain has remarkable computational power, generating sophisticated behaviors, storing memories over an individual’s lifetime, and producing higher cognitive functions. However, little of our neuroscience knowledge covers the human brain. Is this organ truly unique, or is it a scaled version of the extensively studied rodent brain? Combining multicellular patch-clamp recording with expansion-based superresolution microscopy and full-scale modeling, we determined the cellular and microcircuit properties of the human hippocampal CA3 region, a fundamental circuit for memory storage. In contrast to neocortical networks, human hippocampal CA3 displayed sparse connectivity, providing a circuit architecture that maximizes associational power. Human synapses showed unique reliability, high precision, and long integration times, exhibiting both species- and circuit-specific properties. Together with expanded neuronal numbers, these circuit characteristics greatly enhanced the memory storage capacity of CA3. Our results reveal distinct microcircuit properties of the human hippocampus and begin to unravel the inner workings of our most complex organ. }, author = {Watson, Jake and Vargas Barroso, Victor M and Morse, Rebecca and Navas Olivé, Andrea C and Tavakoli, Mojtaba and Danzl, Johann G and Tomschik, Matthias and Rössler, Karl and Jonas, Peter M}, issn = {1097-4172}, journal = {Cell}, number = {2}, pages = {501--514.e18}, publisher = {Elsevier}, title = {{Human hippocampal CA3 uses specific functional connectivity rules for efficient associative memory}}, doi = {10.1016/j.cell.2024.11.022}, volume = {188}, year = {2025}, } @misc{18697, abstract = {The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution with dense cellular labelling. Light microscopy is uniquely positioned to visualize specific molecules but dense, synapse-level circuit reconstruction by light microscopy has been out of reach due to limitations in resolution, contrast, and volumetric imaging capability. Here we developed light-microscopy based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning based segmentation and analysis of connectivity, thus directly incorporating molecular information in synapse-level brain tissue reconstructions. LICONN will allow synapse-level brain tissue phenotyping in biological experiments in a readily adoptable manner.}, author = {Danzl, Johann G and Lyudchik, Julia and Kreuzinger, Caroline}, publisher = {Institute of Science and Technology Austria}, title = {{Light-microscopy based connectomic reconstruction of mammalian brain tissue}}, doi = {10.15479/AT:ISTA:18697}, year = {2025}, } @article{19704, abstract = {The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution1,2 with dense cellular labelling. Light microscopy is uniquely positioned to visualize specific molecules, but dense, synapse-level circuit reconstruction by light microscopy has been out of reach, owing to limitations in resolution, contrast and volumetric imaging capability. Here we describe light-microscopy-based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning-based segmentation and analysis of connectivity, thereby directly incorporating molecular information into synapse-level reconstructions of brain tissue. LICONN will allow synapse-level phenotyping of brain tissue in biological experiments in a readily adoptable manner.}, author = {Tavakoli, Mojtaba and Lyudchik, Julia and Januszewski, Michał and Vistunou, Vitali and Agudelo Duenas, Nathalie and Vorlaufer, Jakob and Sommer, Christoph M and Kreuzinger, Caroline and Oliveira, Bárbara and Cenameri, Alban and Novarino, Gaia and Jain, Viren and Danzl, Johann G}, issn = {1476-4687}, journal = {Nature}, pages = {398--410}, publisher = {Springer Nature}, title = {{Light-microscopy-based connectomic reconstruction of mammalian brain tissue}}, doi = {10.1038/s41586-025-08985-1}, volume = {642}, year = {2025}, } @article{19003, abstract = {Super-resolution methods provide far better spatial resolution than the optical diffraction limit of about half the wavelength of light (∼200-300 nm). Nevertheless, they have yet to attain widespread use in plants, largely due to plants’ challenging optical properties. Expansion microscopy improves effective resolution by isotropically increasing the physical distances between sample structures while preserving relative spatial arrangements and clearing the sample. However, its application to plants has been hindered by the rigid, mechanically cohesive structure of plant tissues. Here, we report on whole-mount expansion microscopy of thale cress (Arabidopsis thaliana) root tissues (PlantEx), achieving a four-fold resolution increase over conventional microscopy. Our results highlight the microtubule cytoskeleton organization and interaction between molecularly defined cellular constituents. Combining PlantEx with stimulated emission depletion (STED) microscopy, we increase nanoscale resolution and visualize the complex organization of subcellular organelles from intact tissues by example of the densely packed COPI-coated vesicles associated with the Golgi apparatus and put these into a cellular structural context. Our results show that expansion microscopy can be applied to increase effective imaging resolution in Arabidopsis root specimens. }, author = {Gallei, Michelle C and Truckenbrodt, Sven M and Kreuzinger, Caroline and Inumella, Syamala and Vistunou, Vitali and Sommer, Christoph M and Tavakoli, Mojtaba and Agudelo Duenas, Nathalie and Vorlaufer, Jakob and Jahr, Wiebke and Randuch, Marek and Johnson, Alexander J and Benková, Eva and Friml, Jiří and Danzl, Johann G}, issn = {1532-298X}, journal = {The Plant Cell}, number = {4}, publisher = {Oxford University Press}, title = {{Super-resolution expansion microscopy in plant roots}}, doi = {10.1093/plcell/koaf006}, volume = {37}, year = {2025}, } @misc{18837, abstract = {Super-resolution methods provide far better spatial resolution than the optical diffraction limit of about half the wavelength of light (∼200-300 nm). Nevertheless, they have yet to attain widespread use in plants, largely due to plants’ challenging optical properties. Expansion microscopy improves effective resolution by isotropically increasing the physical distances between sample structures while preserving relative spatial arrangements and clearing the sample. However, its application to plants has been hindered by the rigid, mechanically cohesive structure of plant tissues. Here, we report on whole-mount expansion microscopy of thale cress (Arabidopsis thaliana) root tissues (PlantEx), achieving a four-fold resolution increase over conventional microscopy. Our results highlight the microtubule cytoskeleton organization and interaction between molecularly defined cellular constituents. Combining PlantEx with stimulated emission depletion (STED) microscopy, we increase nanoscale resolution and visualize the complex organization of subcellular organelles from intact tissues by example of the densely packed COPI-coated vesicles associated with the Golgi apparatus and put these into a cellular structural context. Our results show that expansion microscopy can be applied to increase effective imaging resolution in Arabidopsis root specimens.}, author = {Danzl, Johann G and Kreuzinger, Caroline}, publisher = {Institute of Science and Technology Austria}, title = {{Research Data for the publication "Super-resolution expansion microscopy in plant roots"}}, doi = {10.15479/AT:ISTA:18837}, year = {2025}, } @unpublished{18689, abstract = {Multiplexed fluorescence microscopy imaging is widely used in biomedical applications. However, simultaneous imaging of multiple fluorophores can result in spectral leaks and overlapping, which greatly degrades image quality and subsequent analysis. Existing popular spectral unmixing methods are mainly based on computational intensive linear models and the performance is heavily dependent on the reference spectra, which may greatly preclude its further applications. In this paper, we propose a deep learning-based blindly spectral unmixing method, termed AutoUnmix, to imitate the physical spectral mixing process. A tranfer learning framework is further devised to allow our AutoUnmix adapting to a variety of imaging systems without retraining the network. Our proposed method has demonstrated real-time unmixing capabilities, surpassing existing methods by up to 100-fold in terms of unmixing speed. We further validate the reconstruction performance on both synthetic datasets and biological samples. The unmixing results of AutoUnmix achieve a highest SSIM of 0.99 in both three- and four-color imaging, with nearly up to 20% higher than other popular unmixing methods. Due to the desirable property of data independency and superior blind unmixing performance, we believe AutoUnmix is a powerful tool to study the interaction process of different organelles labeled by multiple fluorophores.}, author = {Gallei, Michelle C and Truckenbrodt, Sven M and Kreuzinger, Caroline and Inumella, Syamala and Vistunou, Vitali and Sommer, Christoph M and Tavakoli, Mojtaba and Agudelo Duenas, Nathalie and Vorlaufer, Jakob and Jahr, Wiebke and Randuch, Marek and Johnson, Alexander J and Benková, Eva and Friml, Jiří and Danzl, Johann G}, booktitle = {bioRxiv}, title = {{Super-resolution expansion microscopy in plant roots}}, doi = {10.1101/2024.02.21.581330}, year = {2024}, } @phdthesis{18681, author = {Tavakoli, Mojtaba}, isbn = {978-3-99078-048-0}, issn = {2663-337X}, pages = {230}, publisher = {Institute of Science and Technology Austria}, title = {{Developing molecular and structural tools for studying brain architecture with super resolution expansion microscopy. LICONN: Molecularly-informed connectomics reconstruction with light microscopy}}, doi = {10.15479/at:ista:18681}, year = {2024}, } @unpublished{18688, abstract = {The human brain has remarkable computational power. It generates sophisticated behavioral sequences, stores engrams over an individual’s lifetime, and produces higher cognitive functions up to the level of consciousness. However, so little of our neuroscience knowledge covers the human brain, and it remains unknown whether this organ is truly unique, or is a scaled version of the extensively studied rodent brain. To address this fundamental question, we determined the cellular, synaptic, and connectivity rules of the hippocampal CA3 recurrent circuit using multicellular patch clamp-recording. This circuit is the largest autoassociative network in the brain, and plays a key role in memory and higher-order computations such as pattern separation and pattern completion. We demonstrate that human hippocampal CA3 employs sparse connectivity, in stark contrast to neocortical recurrent networks. Connectivity sparsifies from rodents to humans, providing a circuit architecture that maximizes associational power. Unitary synaptic events at human CA3–CA3 synapses showed both distinct species-specific and circuit-dependent properties, with high reliability, unique amplitude precision, and long integration times. We also identify differential scaling rules between hippocampal pathways from rodents to humans, with a moderate increase in the convergence of CA3 inputs per cell, but a marked increase in human mossy fiber innervation. Anatomically guided full-scale modeling suggests that the human brain’s sparse connectivity, expanded neuronal number, and reliable synaptic signaling combine to enhance the associative memory storage capacity of CA3. Together, our results reveal unique rules of connectivity and synaptic signaling in the human hippocampus, demonstrating the absolute necessity of human brain research and beginning to unravel the remarkable performance of our autoassociative memory circuits.}, author = {Watson, Jake F. and Vargas-Barroso, Victor and Morse-Mora, Rebecca J. and Navas-Olive, Andrea and Tavakoli, Mojtaba and Danzl, Johann G and Tomschik, Matthias and Rössler, Karl and Jonas, Peter M}, booktitle = {bioRxiv}, title = {{Human hippocampal CA3 uses specific functional connectivity rules for efficient associative memory}}, doi = {10.1101/2024.05.02.592169}, year = {2024}, } @article{14257, abstract = {Mapping the complex and dense arrangement of cells and their connectivity in brain tissue demands nanoscale spatial resolution imaging. Super-resolution optical microscopy excels at visualizing specific molecules and individual cells but fails to provide tissue context. Here we developed Comprehensive Analysis of Tissues across Scales (CATS), a technology to densely map brain tissue architecture from millimeter regional to nanometer synaptic scales in diverse chemically fixed brain preparations, including rodent and human. CATS uses fixation-compatible extracellular labeling and optical imaging, including stimulated emission depletion or expansion microscopy, to comprehensively delineate cellular structures. It enables three-dimensional reconstruction of single synapses and mapping of synaptic connectivity by identification and analysis of putative synaptic cleft regions. Applying CATS to the mouse hippocampal mossy fiber circuitry, we reconstructed and quantified the synaptic input and output structure of identified neurons. We furthermore demonstrate applicability to clinically derived human tissue samples, including formalin-fixed paraffin-embedded routine diagnostic specimens, for visualizing the cellular architecture of brain tissue in health and disease.}, author = {Michalska, Julia M and Lyudchik, Julia and Velicky, Philipp and Korinkova, Hana and Watson, Jake and Cenameri, Alban and Sommer, Christoph M and Amberg, Nicole and Venturino, Alessandro and Roessler, Karl and Czech, Thomas and Höftberger, Romana and Siegert, Sandra and Novarino, Gaia and Jonas, Peter M and Danzl, Johann G}, issn = {1546-1696}, journal = {Nature Biotechnology}, pages = {1051--1064}, publisher = {Springer Nature}, title = {{Imaging brain tissue architecture across millimeter to nanometer scales}}, doi = {10.1038/s41587-023-01911-8}, volume = {42}, year = {2024}, } @phdthesis{18674, abstract = {Mapping the complex and dense arrangement of cells and their connectivity in brain tissue requires volumetric imaging at nanoscale spatial resolution. While light microscopy excels at visualizing specific molecules and individual cells, achieving dense, synapse-level circuit reconstruction has not been possible with any light microscopy technique. Thus, the goal of my work was to develop image and data analysis pipelines for brain tissue visualization and reconstruction with light microscopy. To achieve dense circuit reconstruction with single-synapse resolution, I developed both conventional and deep-learning-based synapse detection algorithms, as well as connectivity analysis pipelines that integrate synapse detection with volumetric segmentation of brain tissue.}, author = {Lyudchik, Julia}, isbn = { 978-3-99078-051-0}, issn = {2663-337X}, pages = {217}, publisher = {Institute of Science and Technology Austria}, title = {{Image analysis for brain tissue reconstruction with super-resolution light microscopy}}, doi = {10.15479/at:ista:18674}, year = {2024}, } @unpublished{18677, abstract = {The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution with dense cellular labeling. Light microscopy is uniquely positioned to visualize specific molecules but dense, synapse-level circuit reconstruction by light microscopy has been out of reach due to limitations in resolution, contrast, and volumetric imaging capability. Here we developed light-microscopy based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning based segmentation and analysis of connectivity, thus directly incorporating molecular information in synapse-level brain tissue reconstructions. LICONN will allow synapse-level brain tissue phenotyping in biological experiments in a readily adoptable manner.}, author = {Tavakoli, Mojtaba and Lyudchik, Julia and Januszewski, Michał and Vistunou, Vitali and Agudelo Duenas, Nathalie and Vorlaufer, Jakob and Sommer, Christoph M and Kreuzinger, Caroline and Oliveira, Bárbara and Cenameri, Alban and Novarino, Gaia and Jain, Viren and Danzl, Johann G}, booktitle = {bioRxiv}, title = {{Light-microscopy based dense connectomic reconstruction of mammalian brain tissue}}, doi = {10.1101/2024.03.01.582884}, year = {2024}, } @phdthesis{18471, abstract = {Spatial omics technologies are enriching our understanding of complex biological samples, by allowing us to study their molecular composition while preserving the spatial relationships between molecules in their native context. As the field continues to advance, there are technical challenges that need to be addressed in order to take full advantage of the spatial capabilities of these methods. In this work, I present two technical developments that I established for multiplexed error robust FISH (MERFISH) throughout my PhD: (1) pushing the spatial resolution limits to the nanoscale, and (2) adding rich tissue context to the mouse brain transcriptome. To achieve nanoscale resolution with MERFISH in cultured cells, I combined it with stimulated emission depletion (STED) and expansion microscopy (ExM) to achieve a spatial resolution as low as ~20 nm, and explored the compatibility of MERFISH with singlemolecule localization microscopy (SMLM) techniques. To visualize targeted mRNAs in mouse brain tissue, I applied the comprehensive analysis of tissues across scales (CATS) toolbox, which provides an unbiased morphological readout by labeling the extracellular domain. I successfully established this method, which we call CATS-MERFISH-ExM, to work with thick mouse brain slices, being able to extract transcriptomics information with 3D tissue context. CATS-MERFISH-ExM enabled us to identify cell types and further visualize the subcellular distribution of transcripts in mouse brain tissue, shedding light on the neuropil-specific transcriptome. This method provides integrated information on cellular structure and transcriptomes in situ, and could potentially be applied with other modalities, opening new avenues for scientific discovery. }, author = {Agudelo Duenas, Nathalie}, isbn = {978-3-99078-044-2}, issn = {2663-337X}, pages = {97}, publisher = {Institute of Science and Technology Austria}, title = {{Visualizing the neuronal transcriptional landscape with tissue context}}, doi = {10.15479/at:ista:18471}, year = {2024}, } @misc{13116, abstract = {The emergence of large-scale order in self-organized systems relies on local interactions between individual components. During bacterial cell division, FtsZ -- a prokaryotic homologue of the eukaryotic protein tubulin -- polymerizes into treadmilling filaments that further organize into a cytoskeletal ring. In vitro, FtsZ filaments can form dynamic chiral assemblies. However, how the active and passive properties of individual filaments relate to these large-scale self-organized structures remains poorly understood. Here, we connect single filament properties with the mesoscopic scale by combining minimal active matter simulations and biochemical reconstitution experiments. We show that density and flexibility of active chiral filaments define their global order. At intermediate densities, curved, flexible filaments organize into chiral rings and polar bands. An effectively nematic organization dominates for high densities and for straight, mutant filaments with increased rigidity. Our predicted phase diagram captures these features quantitatively, demonstrating how the flexibility, density and chirality of active filaments affect their collective behaviour. Our findings shed light on the fundamental properties of active chiral matter and explain how treadmilling FtsZ filaments organize during bacterial cell division. }, author = {Dunajova, Zuzana and Prats Mateu, Batirtze and Radler, Philipp and Lim, Keesiang and Brandis, Dörte and Velicky, Philipp and Danzl, Johann G and Wong, Richard W. and Elgeti, Jens and Hannezo, Edouard B and Loose, Martin}, publisher = {Institute of Science and Technology Austria}, title = {{Chiral and nematic phases of flexible active filaments}}, doi = {10.15479/AT:ISTA:13116}, year = {2023}, } @misc{13126, abstract = {Mapping the complex and dense arrangement of cells and their connectivity in brain tissue demands nanoscale spatial resolution imaging. Super-resolution optical microscopy excels at visualizing specific molecules and individual cells but fails to provide tissue context. Here, we developed Comprehensive Analysis of Tissues across Scales (CATS), a technology to densely map brain tissue architecture from millimeter regional to nanometer synaptic scales in diverse chemically fixed brain preparations, including rodent and human. CATS uses fixation-compatible extracellular labeling and optical imaging, including stimulated emission depletion or expansion microscopy, to comprehensively delineate cellular structures. It enables three-dimensional reconstruction of single synapses and mapping of synaptic connectivity by identification and analysis of putative synaptic cleft regions. Applying CATS to the mouse hippocampal mossy fiber circuitry, we reconstructed and quantified the synaptic input and output structure of identified neurons. We furthermore demonstrate applicability to clinically derived human tissue samples, including formalin-fixed paraffin-embedded routine diagnostic specimens, for visualizing the cellular architecture of brain tissue in health and disease.}, author = {Danzl, Johann G}, publisher = {Institute of Science and Technology Austria}, title = {{Research data for the publication "Imaging brain tissue architecture across millimeter to nanometer scales"}}, doi = {10.15479/AT:ISTA:13126}, year = {2023}, } @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}, } @misc{12817, abstract = {3D-reconstruction of living brain tissue down to 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, it 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). It leverages optical modifications to stimulated emission depletion (STED) microscopy in comprehensively, extracellularly labelled tissue and prior 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 synapse level incorporating molecular, activity, and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue.}, author = {Danzl, Johann G}, publisher = {Institute of Science and Technology Austria}, title = {{Research data for the publication "Dense 4D nanoscale reconstruction of living brain tissue"}}, doi = {10.15479/AT:ISTA:12817}, year = {2023}, } @article{14770, abstract = {We developed LIONESS, a technology that leverages improvements to optical super-resolution microscopy and prior information on sample structure via machine learning to overcome the limitations (in 3D-resolution, signal-to-noise ratio and light exposure) of optical microscopy of living biological specimens. LIONESS enables dense reconstruction of living brain tissue and morphodynamics visualization at the nanoscale.}, author = {Danzl, Johann G and Velicky, Philipp}, issn = {1548-7105}, journal = {Nature Methods}, keywords = {Cell Biology, Molecular Biology, Biochemistry, Biotechnology}, number = {8}, pages = {1141--1142}, publisher = {Springer Nature}, title = {{LIONESS enables 4D nanoscale reconstruction of living brain tissue}}, doi = {10.1038/s41592-023-01937-5}, volume = {20}, year = {2023}, }