@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}, } @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}, } @article{11187, abstract = {During the COVID-19 pandemic, genomics and bioinformatics have emerged as essential public health tools. The genomic data acquired using these methods have supported the global health response, facilitated the development of testing methods and allowed the timely tracking of novel SARS-CoV-2 variants. Yet the virtually unlimited potential for rapid generation and analysis of genomic data is also coupled with unique technical, scientific and organizational challenges. Here, we discuss the application of genomic and computational methods for efficient data-driven COVID-19 response, the advantages of the democratization of viral sequencing around the world and the challenges associated with viral genome data collection and processing.}, author = {Knyazev, Sergey and Chhugani, Karishma and Sarwal, Varuni and Ayyala, Ram and Singh, Harman and Karthikeyan, Smruthi and Deshpande, Dhrithi and Baykal, Pelin Icer and Comarova, Zoia and Lu, Angela and Porozov, Yuri and Vasylyeva, Tetyana I. and Wertheim, Joel O. and Tierney, Braden T. and Chiu, Charles Y. and Sun, Ren and Wu, Aiping and Abedalthagafi, Malak S. and Pak, Victoria M. and Nagaraj, Shivashankar H. and Smith, Adam L. and Skums, Pavel and Pasaniuc, Bogdan and Komissarov, Andrey and Mason, Christopher E. and Bortz, Eric and Lemey, Philippe and Kondrashov, Fyodor and Beerenwinkel, Niko and Lam, Tommy Tsan Yuk and Wu, Nicholas C. and Zelikovsky, Alex and Knight, Rob and Crandall, Keith A. and Mangul, Serghei}, issn = {1548-7105}, journal = {Nature Methods}, number = {4}, pages = {374--380}, publisher = {Springer Nature}, title = {{Unlocking capacities of genomics for the COVID-19 response and future pandemics}}, doi = {10.1038/s41592-022-01444-z}, volume = {19}, 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}, }