@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},
}

@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},
}

@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},
}

@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 <jats:italic>in-silico</jats:italic> reconstruction<jats:sup>1–5</jats:sup>, yet is limited to fixed specimens and static representations. Light microscopy allows live observation, with super-resolution approaches<jats:sup>6–12</jats:sup> 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) microscopy<jats:sup>6,13</jats:sup> in extracellularly labelled tissue<jats:sup>14</jats:sup> 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         = {2296-2646},
  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},
}