TY - JOUR AB - 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. AU - Velicky, Philipp AU - Miguel Villalba, Eder AU - Michalska, Julia M AU - Lyudchik, Julia AU - Wei, Donglai AU - Lin, Zudi AU - Watson, Jake AU - Troidl, Jakob AU - Beyer, Johanna AU - Ben Simon, Yoav AU - Sommer, Christoph M AU - Jahr, Wiebke AU - Cenameri, Alban AU - Broichhagen, Johannes AU - Grant, Seth G.N. AU - Jonas, Peter M AU - Novarino, Gaia AU - Pfister, Hanspeter AU - Bickel, Bernd AU - Danzl, Johann G ID - 13267 JF - Nature Methods SN - 1548-7091 TI - Dense 4D nanoscale reconstruction of living brain tissue VL - 20 ER - TY - JOUR AB - 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. AU - Danzl, Johann G AU - Velicky, Philipp ID - 14770 IS - 8 JF - Nature Methods KW - Cell Biology KW - Molecular Biology KW - Biochemistry KW - Biotechnology SN - 1548-7091 TI - LIONESS enables 4D nanoscale reconstruction of living brain tissue VL - 20 ER - TY - JOUR AB - 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. AU - Knyazev, Sergey AU - Chhugani, Karishma AU - Sarwal, Varuni AU - Ayyala, Ram AU - Singh, Harman AU - Karthikeyan, Smruthi AU - Deshpande, Dhrithi AU - Baykal, Pelin Icer AU - Comarova, Zoia AU - Lu, Angela AU - Porozov, Yuri AU - Vasylyeva, Tetyana I. AU - Wertheim, Joel O. AU - Tierney, Braden T. AU - Chiu, Charles Y. AU - Sun, Ren AU - Wu, Aiping AU - Abedalthagafi, Malak S. AU - Pak, Victoria M. AU - Nagaraj, Shivashankar H. AU - Smith, Adam L. AU - Skums, Pavel AU - Pasaniuc, Bogdan AU - Komissarov, Andrey AU - Mason, Christopher E. AU - Bortz, Eric AU - Lemey, Philippe AU - Kondrashov, Fyodor AU - Beerenwinkel, Niko AU - Lam, Tommy Tsan Yuk AU - Wu, Nicholas C. AU - Zelikovsky, Alex AU - Knight, Rob AU - Crandall, Keith A. AU - Mangul, Serghei ID - 11187 IS - 4 JF - Nature Methods SN - 1548-7091 TI - Unlocking capacities of genomics for the COVID-19 response and future pandemics VL - 19 ER - TY - JOUR AU - Pinkard, Henry AU - Stuurman, Nico AU - Ivanov, Ivan E. AU - Anthony, Nicholas M. AU - Ouyang, Wei AU - Li, Bin AU - Yang, Bin AU - Tsuchida, Mark A. AU - Chhun, Bryant AU - Zhang, Grace AU - Mei, Ryan AU - Anderson, Michael AU - Shepherd, Douglas P. AU - Hunt-Isaak, Ian AU - Dunn, Raymond L. AU - Jahr, Wiebke AU - Kato, Saul AU - Royer, Loïc A. AU - Thiagarajah, Jay R. AU - Eliceiri, Kevin W. AU - Lundberg, Emma AU - Mehta, Shalin B. AU - Waller, Laura ID - 9258 IS - 3 JF - Nature Methods SN - 1548-7091 TI - Pycro-Manager: Open-source software for customized and reproducible microscope control VL - 18 ER -