@article {69994, title = {A Multimodal Adaptive Optical Microscope For In Vivo Imaging from Molecules to Organisms}, journal = {bioRxiv}, year = {2025}, month = {2025 Jun 16}, abstract = {

Understanding biological systems requires observing features and processes across vast spatial and temporal scales, spanning nanometers to centimeters and milliseconds to days, often using multiple imaging modalities within complex native microenvironments. Yet, achieving this comprehensive view is challenging because microscopes optimized for specific tasks typically lack versatility due to inherent optical and sample handling trade-offs, and frequently suffer performance degradation from sample-induced optical aberrations in multicellular contexts. Here, we present MOSAIC, a reconfigurable microscope that integrates multiple advanced imaging techniques including light-sheet, label-free, super-resolution, and multi-photon, all equipped with adaptive optics. MOSAIC enables non-invasive imaging of subcellular dynamics in both cultured cells and live multicellular organisms, nanoscale mapping of molecular architectures across millimeter-scale expanded tissues, and structural/functional neural imaging within live mice. MOSAIC facilitates correlative studies across biological scales within the same specimen, providing an integrated platform for broad biological investigation.

Preprint:\ https://www.biorxiv.org/content/early/2025/06/13/2025.06.02.657494

}, doi = {10.1101/2025.06.02.657494}, url = {https://www.biorxiv.org/content/early/2025/06/13/2025.06.02.657494}, author = {Fu, Tian-Ming and Liu, Gaoxiang and Milkie, Daniel E. and Ruan, Xiongtao and G{\"o}rlitz, Frederik and Shi, Yu and Ferro, Valentina and Divekar, Nikita S. and Wang, Wei and York, Harrison M. and Kilic, Velat and Mueller, Matthew and Liang, Yajie and Daugird, Timothy A. and Gacha-Garay, Maria J. and Larkin, Kathryn A. and Adikes, Rebecca C. and Harrison, Nathaniel and Shirazinejad, Cyna and Williams, Samara and Nourse, Jamison L. and Sheu, Shu-Hsien and Gao, Liang and Li, Tongchao and Mondal, Chandrani and Achour, Kemal and Hercule, Wilmene and Stabley, Daniel and Emmerich, Kevin and Dong, Peng and Drubin, David and Liu, Zhe J. and Clapham, David and Mumm, Jeff S. and Koyama, Minoru and Killilea, Alison and Bravo-Cordero, Jose Javier and Keene, C. Dirk and Luo, Liqun and Kirchhausen, Tom and Pathak, Medha M. and Arumugam, Senthil and Nunez, James K. and Gao, Ruixuan and Matus, David Q. and Martin, Benjamin L. and Swinburne, Ian A. and Betzig, Eric and Legant, Wesley R. and Upadhyayula, Srigokul} } @article {67226, title = {Deep learning enables fast and dense single-molecule localization with high accuracy}, journal = {Nature Methods}, volume = {18}, year = {2021}, month = {2021 Sep 03}, abstract = {

Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but the need for activating only single isolated emitters limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE, a computational tool that can localize single emitters at high density in 3D with highest accuracy for a large range of imaging modalities and conditions. In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 data-sets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to take live-cell SMLM data with reduced light exposure in just 3 seconds and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many labs to reduce imaging times and increase localization density in SMLM.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {10.1101/2020.10.26.355164}, url = {https://www.biorxiv.org/content/early/2020/10/26/2020.10.26.355164}, author = {Speiser, Artur and M{\"u}ller, Lucas-Raphael and Matti, Ulf and Obara, Christopher J. and Legant, Wesley R. and Kreshuk, Anna and Macke, Jakob H. and Ries, Jonas and Turaga, Srinivas C.} } @article {65368, title = {3D ATAC-PALM: super-resolution imaging of the accessible genome.}, journal = {Nature Methods}, volume = {17}, year = {2020}, month = {2020 Apr 01}, pages = {430-6}, abstract = {

To image the accessible genome at nanometer scale in situ, we developed three-dimensional assay for transposase-accessible chromatin-photoactivated localization microscopy (3D ATAC-PALM) that integrates an assay for transposase-accessible chromatin with visualization, PALM super-resolution imaging and lattice light-sheet microscopy. Multiplexed with oligopaint DNA{\textendash}fluorescence in situ hybridization (FISH), RNA{\textendash}FISH and protein fluorescence, 3D ATAC-PALM connected microscopy and genomic data, revealing spatially segregated accessible chromatin domains (ACDs) that enclose active chromatin and transcribed genes. Using these methods to analyze genetically perturbed cells, we demonstrated that genome architectural protein CTCF prevents excessive clustering of accessible chromatin and decompacts ACDs. These results highlight 3D ATAC-PALM as a useful tool to probe the structure and organizing mechanism of the genome.

}, issn = {1548-7091}, doi = {10.1038/s41592-020-0775-2}, url = {http://www.nature.com/articles/s41592-020-0775-2}, author = {Xie, Liangqi and Dong, Peng and Chen, Xingqi and Hsieh, Tsung-Han S. and Banala, Sambashiva and De Marzio, Margherita and English, Brian P. and Qi, Yifeng and Jung, Seol Kyoung and Kieffer-Kwon, Kyong-Rim and Legant, Wesley R. and Hansen, Anders S. and Schulmann, Anton and Casellas, Rafael and Zhang, Bin and Betzig, Eric and Lavis, Luke D. and Chang, Howard Y. and Tjian, Robert and Liu, Zhe} } @article {65041, title = {High-resolution imaging reveals how the spindle midzone impacts chromosome movement.}, journal = {The Journal of Cell Biology}, volume = {218}, year = {2019}, month = {27 Jun 2019}, pages = {2529-44}, abstract = {

In the spindle midzone, microtubules from opposite half-spindles form bundles between segregating chromosomes. Microtubule bundles can either push or restrict chromosome movement during anaphase in different cellular contexts, but how these activities are achieved remains poorly understood. Here, we use high-resolution live-cell imaging to analyze individual microtubule bundles, growing filaments, and chromosome movement in dividing human cells. Within bundles, filament overlap length marked by the cross-linking protein PRC1 decreases during anaphase as chromosome segregation slows. Filament ends within microtubule bundles appear capped despite dynamic PRC1 turnover and submicrometer proximity to growing microtubules. Chromosome segregation distance and rate are increased in two human cell lines when microtubule bundle assembly is prevented via PRC1 knockdown. Upon expressing a mutant PRC1 with reduced microtubule affinity, bundles assemble but chromosome hypersegregation is still observed. We propose that microtubule overlap length reduction, typically linked to pushing forces generated within filament bundles, is needed to properly restrict spindle elongation and position chromosomes within daughter cells.

}, issn = {0021-9525}, doi = {10.1083/jcb.201904169}, url = {http://www.jcb.org/lookup/doi/10.1083/jcb.201904169https://syndication.highwire.org/content/doi/10.1083/jcb.201904169}, author = {Pamula, Melissa C. and Carlini, Lina and Forth, Scott and Verma, Priyanka and Suresh, Subbulakshmi and Legant, Wesley R. and Khodjakov, Alexey and Betzig, Eric and Kapoor, Tarun M.} } @article {65113, title = {Software for lattice light-sheet imaging of FRET biosensors, illustrated with a new Rap1 biosensor.}, journal = {The Journal of Cell Biology}, volume = {218}, year = {2019}, month = {2019 Sep 2}, pages = {3153-3160}, abstract = {

Lattice light-sheet microscopy (LLSM) is valuable for its combination of reduced photobleaching and outstanding spatiotemporal resolution in 3D. Using LLSM to image biosensors in living cells could provide unprecedented visualization of rapid, localized changes in protein conformation or posttranslational modification. However, computational manipulations required for biosensor imaging with LLSM are challenging for many software packages. The calculations require processing large amounts of data even for simple changes such as reorientation of cell renderings or testing the effects of user-selectable settings, and lattice imaging poses unique challenges in thresholding and ratio imaging. We describe here a new software package, named ImageTank, that is specifically designed for practical imaging of biosensors using LLSM. To demonstrate its capabilities, we use a new biosensor to study the rapid 3D dynamics of the small GTPase Rap1 in vesicles and cell protrusions.

}, issn = {0021-9525}, doi = {10.1083/jcb.201903019}, url = {http://www.jcb.org/lookup/doi/10.1083/jcb.201903019}, author = {O{\textquoteright}Shaughnessy, Ellen C. and Stone, Orrin J. and LaFosse, Paul K. and Azoitei, Mihai L. and Tsygankov, Denis and Heddleston, John M. and Legant, Wesley R. and Wittchen, Erika S. and Burridge, Keith and Elston, Timothy C. and Betzig, Eric and Chew, Teng-Leong and Adalsteinsson, David and Hahn, Klaus M.} } @article {67222, title = {Teaching deep neural networks to localize single molecules for super-resolution microscopy}, journal = {arXiv e-prints}, year = {2019}, month = {06/2019}, pages = {arXiv:1907.00770}, abstract = {

Single-molecule localization fluorescence microscopy constructs super-resolution images by sequential imaging and computational localization of sparsely activated fluorophores. Accurate and efficient fluorophore localization algorithms are key to the success of this computational microscopy method. We present a novel localization algorithm based on deep learning which significantly improves upon the state of the art. Our contributions are a novel network architecture for simultaneous detection and localization, and new loss function which phrases detection and localization as a Bayesian inference problem, and thus allows the network to provide uncertainty-estimates. In contrast to standard methods which independently process imaging frames, our network architecture uses temporal context from multiple sequentially imaged frames to detect and localize molecules. We demonstrate the power of our method across a variety of datasets, imaging modalities, signal to noise ratios, and fluorophore densities. While existing localization algorithms can achieve optimal localization accuracy at low fluorophore densities, they are confounded by high densities. Our method is the first deep-learning based approach which achieves state-of-the-art on the SMLM2016 challenge. It achieves the best scores on 12 out of 12 data-sets when comparing both detection accuracy and precision, and excels at high densities. Finally, we investigate how unsupervised learning can be used to make the network robust against mismatch between simulated and real data. The lessons learned here are more generally relevant for the training of deep networks to solve challenging Bayesian inverse problems on spatially extended domains in biology and physics.

}, keywords = {Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Machine Learning}, url = {https://arxiv.org/abs/1907.00770}, author = {Speiser, Artur and M{\"u}ller, Lucas-Raphael and Matti, Ulf and Obara, Christopher J. and Legant, Wesley R. and Ries, Jonas and Macke, Jakob H. and Turaga, Srinivas C.} }