[{"corr_author":"1","ddc":["570"],"month":"06","publication":"Biophysical Reports","date_created":"2025-06-08T22:01:22Z","oa":1,"intvolume":"         5","date_updated":"2026-04-07T11:48:07Z","publication_status":"published","scopus_import":"1","oa_version":"Published Version","language":[{"iso":"eng"}],"doi":"10.1016/j.bpr.2025.100211","OA_place":"publisher","DOAJ_listed":"1","abstract":[{"lang":"eng","text":"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."}],"acknowledged_ssus":[{"_id":"M-Shop"},{"_id":"EM-Fac"},{"_id":"LifeSc"}],"title":"Image-based 3D active sample stabilization on the nanometer scale for optical microscopy","related_material":{"record":[{"id":"20206","relation":"dissertation_contains","status":"public"}]},"issue":"2","article_number":"100211","publication_identifier":{"eissn":["2667-0747"]},"has_accepted_license":"1","type":"journal_article","project":[{"grant_number":"CZI01","_id":"62909c6f-2b32-11ec-9570-e1476aab5308","name":"CryoMinflux-guided in-situ molecular census and structure determination"},{"name":"Studying Organelle Structure and Function at Nanoscale Resolution with Expansion Microscopy","_id":"6285a163-2b32-11ec-9570-8e204ca2dba5","grant_number":"26137"},{"grant_number":"665385","name":"International IST Doctoral Program","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"},{"call_identifier":"FWF","_id":"26AA4EF2-B435-11E9-9278-68D0E5697425","name":"Molecular Drug Targets","grant_number":"W1232-B24"},{"grant_number":"LT00057","name":"High-speed 3D-nanoscopy to study the role of adhesion during 3D cell migration","_id":"2668BFA0-B435-11E9-9278-68D0E5697425"}],"quality_controlled":"1","article_type":"original","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"OA_type":"gold","citation":{"ama":"Vorlaufer J, Semenov N, Kreuzinger C, et al. Image-based 3D active sample stabilization on the nanometer scale for optical microscopy. <i>Biophysical Reports</i>. 2025;5(2). doi:<a href=\"https://doi.org/10.1016/j.bpr.2025.100211\">10.1016/j.bpr.2025.100211</a>","ista":"Vorlaufer J, Semenov N, Kreuzinger C, Javoor M, Zens B, Agudelo Duenas N, Tavakoli M, Suplata M, Jahr W, Lyudchik J, Wartak A, Schur FK, Danzl JG. 2025. Image-based 3D active sample stabilization on the nanometer scale for optical microscopy. Biophysical Reports. 5(2), 100211.","short":"J. Vorlaufer, N. Semenov, C. Kreuzinger, M. Javoor, B. Zens, N. Agudelo Duenas, M. Tavakoli, M. Suplata, W. Jahr, J. Lyudchik, A. Wartak, F.K. Schur, J.G. Danzl, Biophysical Reports 5 (2025).","chicago":"Vorlaufer, Jakob, Nikolai Semenov, Caroline Kreuzinger, Manjunath Javoor, Bettina Zens, Nathalie Agudelo Duenas, Mojtaba Tavakoli, et al. “Image-Based 3D Active Sample Stabilization on the Nanometer Scale for Optical Microscopy.” <i>Biophysical Reports</i>. Elsevier, 2025. <a href=\"https://doi.org/10.1016/j.bpr.2025.100211\">https://doi.org/10.1016/j.bpr.2025.100211</a>.","apa":"Vorlaufer, J., Semenov, N., Kreuzinger, C., Javoor, M., Zens, B., Agudelo Duenas, N., … Danzl, J. G. (2025). Image-based 3D active sample stabilization on the nanometer scale for optical microscopy. <i>Biophysical Reports</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.bpr.2025.100211\">https://doi.org/10.1016/j.bpr.2025.100211</a>","ieee":"J. Vorlaufer <i>et al.</i>, “Image-based 3D active sample stabilization on the nanometer scale for optical microscopy,” <i>Biophysical Reports</i>, vol. 5, no. 2. Elsevier, 2025.","mla":"Vorlaufer, Jakob, et al. “Image-Based 3D Active Sample Stabilization on the Nanometer Scale for Optical Microscopy.” <i>Biophysical Reports</i>, vol. 5, no. 2, 100211, Elsevier, 2025, doi:<a href=\"https://doi.org/10.1016/j.bpr.2025.100211\">10.1016/j.bpr.2025.100211</a>."},"_id":"19795","author":[{"orcid":"0009-0000-7590-3501","id":"937696FA-C996-11E9-8C7C-CF13E6697425","first_name":"Jakob","last_name":"Vorlaufer","full_name":"Vorlaufer, Jakob"},{"first_name":"Nikolai","id":"e64d39c7-72ef-11ef-b75a-ee3046860d1b","last_name":"Semenov","full_name":"Semenov, Nikolai"},{"first_name":"Caroline","id":"382077BA-F248-11E8-B48F-1D18A9856A87","full_name":"Kreuzinger, Caroline","last_name":"Kreuzinger"},{"id":"305ab18b-dc7d-11ea-9b2f-b58195228ea2","first_name":"Manjunath","orcid":"0000-0003-2311-2112","full_name":"Javoor, Manjunath","last_name":"Javoor"},{"orcid":"0000-0002-9561-1239","first_name":"Bettina","id":"45FD126C-F248-11E8-B48F-1D18A9856A87","last_name":"Zens","full_name":"Zens, Bettina"},{"first_name":"Nathalie","id":"40E7F008-F248-11E8-B48F-1D18A9856A87","full_name":"Agudelo Duenas, Nathalie","last_name":"Agudelo Duenas"},{"orcid":"0000-0002-7667-6854","first_name":"Mojtaba","id":"3A0A06F4-F248-11E8-B48F-1D18A9856A87","last_name":"Tavakoli","full_name":"Tavakoli, Mojtaba"},{"full_name":"Suplata, Marek","last_name":"Suplata","id":"EE8452B8-C26A-11E9-B157-E80CE6697425","first_name":"Marek"},{"first_name":"Wiebke","id":"425C1CE8-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-0201-2315","full_name":"Jahr, Wiebke","last_name":"Jahr"},{"last_name":"Lyudchik","full_name":"Lyudchik, Julia","first_name":"Julia","id":"46E28B80-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Andreas","id":"60aaa06c-3de5-11eb-9e53-baa88e955dcb","last_name":"Wartak","full_name":"Wartak, Andreas"},{"full_name":"Schur, Florian Km","last_name":"Schur","id":"48AD8942-F248-11E8-B48F-1D18A9856A87","first_name":"Florian Km","orcid":"0000-0003-4790-8078"},{"id":"42EFD3B6-F248-11E8-B48F-1D18A9856A87","first_name":"Johann G","orcid":"0000-0001-8559-3973","full_name":"Danzl, Johann G","last_name":"Danzl"}],"article_processing_charge":"Yes","publisher":"Elsevier","year":"2025","day":"11","date_published":"2025-06-11T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"JoDa"},{"_id":"GradSch"},{"_id":"FlSc"},{"_id":"EM-Fac"}],"acknowledgement":"We acknowledge expert support by ISTA’s scientific service units, including the Miba Machine Shop, the Electron Microscopy Facility, and the Lab Support Facility. This work has been made possible in part by CZI grant DAF2021-234754 and grant DOI: https://doi.org/10.37921/812628ebpcwg from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation (funder DOI: https://doi.org/10.13039/100014989) (F.K.M.S. and J.G.D.). We further gratefully acknowledge funding by the following sources: Austrian Science Fund (FWF) grant DK W1232 (M.R.T. and J.G.D.); Austrian Academy of Sciences DOC fellowship 26137 (M.R.T.); Marie Skłodowska-Curie Actions Fellowship GA no. 665385 under the EU Horizon 2020 program (J.L.); ISTA postdoctoral fellowship IST fellow (A.W.); and Human Frontier Science Program postdoctoral fellowship LT000557/2018 (W.J.).","file_date_updated":"2025-06-10T07:24:46Z","volume":5,"file":[{"file_size":7238179,"date_created":"2025-06-10T07:24:46Z","date_updated":"2025-06-10T07:24:46Z","access_level":"open_access","relation":"main_file","file_id":"19802","success":1,"creator":"dernst","content_type":"application/pdf","checksum":"4018c833f25a3ad3b57e3577fed70334","file_name":"2025_BiophysicalReports_Vorlaufer.pdf"}],"license":"https://creativecommons.org/licenses/by/4.0/","ec_funded":1,"status":"public"},{"oa_version":"Published Version","language":[{"iso":"eng"}],"publication_status":"published","date_updated":"2026-04-07T12:58:30Z","scopus_import":"1","external_id":{"isi":["001025621500001"],"pmid":["37429995"]},"title":"Dense 4D nanoscale reconstruction of living brain tissue","acknowledged_ssus":[{"_id":"ScienComp"},{"_id":"Bio"},{"_id":"PreCl"},{"_id":"E-Lib"},{"_id":"LifeSc"},{"_id":"M-Shop"}],"abstract":[{"lang":"eng","text":"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."}],"doi":"10.1038/s41592-023-01936-6","OA_place":"publisher","page":"1256-1265","publication":"Nature Methods","date_created":"2023-07-23T22:01:13Z","corr_author":"1","ddc":["570"],"isi":1,"month":"08","intvolume":"        20","oa":1,"year":"2023","day":"01","date_published":"2023-08-01T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"PeJo"},{"_id":"GaNo"},{"_id":"BeBi"},{"_id":"JoDa"},{"_id":"Bio"}],"_id":"13267","article_processing_charge":"Yes (in subscription journal)","author":[{"first_name":"Philipp","id":"39BDC62C-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2340-7431","full_name":"Velicky, Philipp","last_name":"Velicky"},{"first_name":"Eder","id":"3FB91342-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-5665-0430","full_name":"Miguel Villalba, Eder","last_name":"Miguel Villalba"},{"id":"443DB6DE-F248-11E8-B48F-1D18A9856A87","first_name":"Julia M","orcid":"0000-0003-3862-1235","full_name":"Michalska, Julia M","last_name":"Michalska"},{"id":"46E28B80-F248-11E8-B48F-1D18A9856A87","first_name":"Julia","last_name":"Lyudchik","full_name":"Lyudchik, Julia"},{"first_name":"Donglai","last_name":"Wei","full_name":"Wei, Donglai"},{"last_name":"Lin","full_name":"Lin, Zudi","first_name":"Zudi"},{"last_name":"Watson","full_name":"Watson, Jake","orcid":"0000-0002-8698-3823","id":"63836096-4690-11EA-BD4E-32803DDC885E","first_name":"Jake"},{"last_name":"Troidl","full_name":"Troidl, Jakob","first_name":"Jakob"},{"last_name":"Beyer","full_name":"Beyer, Johanna","first_name":"Johanna"},{"id":"43DF3136-F248-11E8-B48F-1D18A9856A87","first_name":"Yoav","full_name":"Ben Simon, Yoav","last_name":"Ben Simon"},{"last_name":"Sommer","full_name":"Sommer, Christoph M","orcid":"0000-0003-1216-9105","first_name":"Christoph M","id":"4DF26D8C-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Jahr","full_name":"Jahr, Wiebke","orcid":"0000-0003-0201-2315","first_name":"Wiebke","id":"425C1CE8-F248-11E8-B48F-1D18A9856A87"},{"id":"9ac8f577-2357-11eb-997a-e566c5550886","first_name":"Alban","full_name":"Cenameri, Alban","last_name":"Cenameri"},{"full_name":"Broichhagen, Johannes","last_name":"Broichhagen","first_name":"Johannes"},{"full_name":"Grant, Seth G.N.","last_name":"Grant","first_name":"Seth G.N."},{"orcid":"0000-0001-5001-4804","first_name":"Peter M","id":"353C1B58-F248-11E8-B48F-1D18A9856A87","last_name":"Jonas","full_name":"Jonas, Peter M"},{"orcid":"0000-0002-7673-7178","first_name":"Gaia","id":"3E57A680-F248-11E8-B48F-1D18A9856A87","last_name":"Novarino","full_name":"Novarino, Gaia"},{"full_name":"Pfister, Hanspeter","last_name":"Pfister","first_name":"Hanspeter"},{"first_name":"Bernd","id":"49876194-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6511-9385","full_name":"Bickel, Bernd","last_name":"Bickel"},{"last_name":"Danzl","full_name":"Danzl, Johann G","orcid":"0000-0001-8559-3973","id":"42EFD3B6-F248-11E8-B48F-1D18A9856A87","first_name":"Johann G"}],"publisher":"Springer Nature","file_date_updated":"2025-02-26T08:01:57Z","volume":20,"file":[{"date_updated":"2025-02-26T08:01:57Z","access_level":"open_access","file_size":14103039,"date_created":"2025-02-26T08:01:57Z","success":1,"creator":"dernst","file_name":"2023_NatureMethods_Velicky.pdf","content_type":"application/pdf","checksum":"a68e845780a82ea36d0d4d3212a87c10","relation":"main_file","file_id":"19088"}],"status":"public","ec_funded":1,"acknowledgement":"We thank J. Vorlaufer, N. Agudelo and A. Wartak for microscope maintenance and troubleshooting, C. Kreuzinger and A. Freeman for technical assistance, M. Šuplata for hardware control support and M. Cunha dos Santos for initial exploration of software. We\r\nthank P. Henderson for advice on deep-learning training and M. Sixt, S. Boyd and T. Weiss for discussions and critical reading of the manuscript. L. Lavis (Janelia Research Campus) generously provided the JF585-HaloTag ligand. We acknowledge expert support by IST\r\nAustria’s scientific computing, imaging and optics, preclinical, library and laboratory support facilities and by the Miba machine shop. We gratefully acknowledge funding by the following sources: Austrian Science Fund (F.W.F.) grant no. I3600-B27 (J.G.D.), grant no. DK W1232\r\n(J.G.D. and J.M.M.) and grant no. Z 312-B27, Wittgenstein award (P.J.); the Gesellschaft für Forschungsförderung NÖ grant no. LSC18-022 (J.G.D.); an ISTA Interdisciplinary project grant (J.G.D. and B.B.); the European Union’s Horizon 2020 research and innovation programme,\r\nMarie-Skłodowska Curie grant 665385 (J.M.M. and J.L.); the European Union’s Horizon 2020 research and innovation programme, European Research Council grant no. 715767, MATERIALIZABLE (B.B.); grant no. 715508, REVERSEAUTISM (G.N.); grant no. 695568, SYNNOVATE (S.G.N.G.); and grant no. 692692, GIANTSYN (P.J.); the Simons\r\nFoundation Autism Research Initiative grant no. 529085 (S.G.N.G.); the Wellcome Trust Technology Development grant no. 202932 (S.G.N.G.); the Marie Skłodowska-Curie Actions Individual Fellowship no. 101026635 under the EU Horizon 2020 program (J.F.W.);\r\nthe Human Frontier Science Program postdoctoral fellowship LT000557/2018 (W.J.); and the National Science Foundation grant no. IIS-1835231 (H.P.) and NCS-FO-2124179 (H.P.).","publication_identifier":{"eissn":["1548-7105"],"issn":["1548-7091"]},"has_accepted_license":"1","related_material":{"link":[{"relation":"software","url":"https://github.com/danzllab/LIONESS"}],"record":[{"id":"12817","status":"public","relation":"research_data"},{"id":"14770","relation":"shorter_version","status":"public"},{"relation":"earlier_version","status":"public","id":"11943"},{"id":"18674","status":"public","relation":"dissertation_contains"}]},"article_type":"original","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"OA_type":"hybrid","citation":{"mla":"Velicky, Philipp, et al. “Dense 4D Nanoscale Reconstruction of Living Brain Tissue.” <i>Nature Methods</i>, vol. 20, Springer Nature, 2023, pp. 1256–65, doi:<a href=\"https://doi.org/10.1038/s41592-023-01936-6\">10.1038/s41592-023-01936-6</a>.","short":"P. Velicky, E. Miguel Villalba, J.M. Michalska, J. Lyudchik, D. Wei, Z. Lin, J. Watson, J. Troidl, J. Beyer, Y. Ben Simon, C.M. Sommer, W. Jahr, A. Cenameri, J. Broichhagen, S.G.N. Grant, P.M. Jonas, G. Novarino, H. Pfister, B. Bickel, J.G. Danzl, Nature Methods 20 (2023) 1256–1265.","chicago":"Velicky, Philipp, Eder Miguel Villalba, Julia M Michalska, Julia Lyudchik, Donglai Wei, Zudi Lin, Jake Watson, et al. “Dense 4D Nanoscale Reconstruction of Living Brain Tissue.” <i>Nature Methods</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1038/s41592-023-01936-6\">https://doi.org/10.1038/s41592-023-01936-6</a>.","apa":"Velicky, P., Miguel Villalba, E., Michalska, J. M., Lyudchik, J., Wei, D., Lin, Z., … Danzl, J. G. (2023). Dense 4D nanoscale reconstruction of living brain tissue. <i>Nature Methods</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41592-023-01936-6\">https://doi.org/10.1038/s41592-023-01936-6</a>","ieee":"P. Velicky <i>et al.</i>, “Dense 4D nanoscale reconstruction of living brain tissue,” <i>Nature Methods</i>, vol. 20. Springer Nature, pp. 1256–1265, 2023.","ama":"Velicky P, Miguel Villalba E, Michalska JM, et al. Dense 4D nanoscale reconstruction of living brain tissue. <i>Nature Methods</i>. 2023;20:1256-1265. doi:<a href=\"https://doi.org/10.1038/s41592-023-01936-6\">10.1038/s41592-023-01936-6</a>","ista":"Velicky P, Miguel Villalba E, Michalska JM, Lyudchik J, Wei D, Lin Z, Watson J, Troidl J, Beyer J, Ben Simon Y, Sommer CM, Jahr W, Cenameri A, Broichhagen J, Grant SGN, Jonas PM, Novarino G, Pfister H, Bickel B, Danzl JG. 2023. Dense 4D nanoscale reconstruction of living brain tissue. Nature Methods. 20, 1256–1265."},"pmid":1,"type":"journal_article","project":[{"call_identifier":"FWF","_id":"265CB4D0-B435-11E9-9278-68D0E5697425","name":"Optical control of synaptic function via adhesion molecules","grant_number":"I03600"},{"call_identifier":"FWF","name":"Molecular Drug Targets","_id":"2548AE96-B435-11E9-9278-68D0E5697425","grant_number":"W1232"},{"name":"Synaptic communication in neuronal microcircuits","_id":"25C5A090-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"Z00312"},{"name":"High content imaging to decode human immune cell interactions in health and allergic disease","_id":"23889792-32DE-11EA-91FC-C7463DDC885E","grant_number":"LS18-022"},{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","name":"International IST Doctoral Program","call_identifier":"H2020","grant_number":"665385"},{"grant_number":"715767","_id":"24F9549A-B435-11E9-9278-68D0E5697425","name":"MATERIALIZABLE: Intelligent fabrication-oriented Computational Design and Modeling","call_identifier":"H2020"},{"grant_number":"715508","name":"Probing the Reversibility of Autism Spectrum Disorders by Employing in vivo and in vitro Models","_id":"25444568-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"},{"grant_number":"692692","_id":"25B7EB9E-B435-11E9-9278-68D0E5697425","name":"Biophysics and circuit function of a giant cortical glutamatergic synapse","call_identifier":"H2020"},{"call_identifier":"H2020","name":"Synaptic computations of the hippocampal CA3 circuitry","_id":"fc2be41b-9c52-11eb-aca3-faa90aa144e9","grant_number":"101026635"},{"_id":"2668BFA0-B435-11E9-9278-68D0E5697425","name":"High-speed 3D-nanoscopy to study the role of adhesion during 3D cell migration","grant_number":"LT00057"}],"quality_controlled":"1"},{"year":"2020","day":"01","date_published":"2020-03-01T00:00:00Z","department":[{"_id":"JoDa"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","_id":"6808","author":[{"id":"425C1CE8-F248-11E8-B48F-1D18A9856A87","first_name":"Wiebke","full_name":"Jahr, Wiebke","last_name":"Jahr"},{"first_name":"Philipp","id":"39BDC62C-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2340-7431","full_name":"Velicky, Philipp","last_name":"Velicky"},{"full_name":"Danzl, Johann G","last_name":"Danzl","id":"42EFD3B6-F248-11E8-B48F-1D18A9856A87","first_name":"Johann G","orcid":"0000-0001-8559-3973"}],"article_processing_charge":"No","publisher":"Elsevier","volume":174,"status":"public","publication_identifier":{"issn":["1046-2023"]},"issue":"3","main_file_link":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7100895/","open_access":"1"}],"article_type":"original","citation":{"ista":"Jahr W, Velicky P, Danzl JG. 2020. Strategies to maximize performance in STimulated Emission Depletion (STED) nanoscopy of biological specimens. Methods. 174(3), 27–41.","ama":"Jahr W, Velicky P, Danzl JG. Strategies to maximize performance in STimulated Emission Depletion (STED) nanoscopy of biological specimens. <i>Methods</i>. 2020;174(3):27-41. doi:<a href=\"https://doi.org/10.1016/j.ymeth.2019.07.019\">10.1016/j.ymeth.2019.07.019</a>","apa":"Jahr, W., Velicky, P., &#38; Danzl, J. G. (2020). Strategies to maximize performance in STimulated Emission Depletion (STED) nanoscopy of biological specimens. <i>Methods</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.ymeth.2019.07.019\">https://doi.org/10.1016/j.ymeth.2019.07.019</a>","ieee":"W. Jahr, P. Velicky, and J. G. Danzl, “Strategies to maximize performance in STimulated Emission Depletion (STED) nanoscopy of biological specimens,” <i>Methods</i>, vol. 174, no. 3. Elsevier, pp. 27–41, 2020.","short":"W. Jahr, P. Velicky, J.G. Danzl, Methods 174 (2020) 27–41.","chicago":"Jahr, Wiebke, Philipp Velicky, and Johann G Danzl. “Strategies to Maximize Performance in STimulated Emission Depletion (STED) Nanoscopy of Biological Specimens.” <i>Methods</i>. Elsevier, 2020. <a href=\"https://doi.org/10.1016/j.ymeth.2019.07.019\">https://doi.org/10.1016/j.ymeth.2019.07.019</a>.","mla":"Jahr, Wiebke, et al. “Strategies to Maximize Performance in STimulated Emission Depletion (STED) Nanoscopy of Biological Specimens.” <i>Methods</i>, vol. 174, no. 3, Elsevier, 2020, pp. 27–41, doi:<a href=\"https://doi.org/10.1016/j.ymeth.2019.07.019\">10.1016/j.ymeth.2019.07.019</a>."},"pmid":1,"type":"journal_article","quality_controlled":"1","project":[{"call_identifier":"FWF","name":"Optical control of synaptic function via adhesion molecules","_id":"265CB4D0-B435-11E9-9278-68D0E5697425","grant_number":"I03600"},{"grant_number":"LT00057","_id":"2668BFA0-B435-11E9-9278-68D0E5697425","name":"High-speed 3D-nanoscopy to study the role of adhesion during 3D cell migration"}],"oa_version":"Submitted Version","language":[{"iso":"eng"}],"date_updated":"2025-04-14T09:39:25Z","publication_status":"published","scopus_import":"1","external_id":{"isi":["000525860400005"],"pmid":["31344404"]},"title":"Strategies to maximize performance in STimulated Emission Depletion (STED) nanoscopy of biological specimens","abstract":[{"text":"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.","lang":"eng"}],"doi":"10.1016/j.ymeth.2019.07.019","page":"27-41","publication":"Methods","date_created":"2019-08-12T16:36:32Z","isi":1,"month":"03","intvolume":"       174","oa":1}]