Volumetric imaging of fast cellular dynamics with deep learning enhanced bioluminescence microscopy

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• dc.contributor.author Morales-Curiel, Luis Felipe
• dc.contributor.author Gonzalez, Adriana Carolina
• dc.contributor.author Castro-Olvera, Gustavo
• dc.contributor.author Lin, Li-Chun Lynn
• dc.contributor.author El-Quessny, Malak
• dc.contributor.author Porta-de-la-Riva, Montserrat
• dc.contributor.author Severino, Jacqueline, 1990-
• dc.contributor.author Batlle Masó, Laura, 1993-
• dc.contributor.author Venturini, Valeria
• dc.contributor.author Ruprecht, Verena
• dc.contributor.author Ramallo, Diego
• dc.contributor.author Loza-Alvarez, Pablo
• dc.contributor.author Krieg, Michael
• dc.date.accessioned 2023-02-23T07:03:44Z
• dc.date.available 2023-02-23T07:03:44Z
• dc.date.issued 2022
• dc.description.abstract Bioluminescence microscopy is an appealing alternative to fluorescence microscopy, because it does not depend on external illumination, and consequently does neither produce spurious background autofluorescence, nor perturb intrinsically photosensitive processes in living cells and animals. The low photon emission of known luciferases, however, demands long exposure times that are prohibitive for imaging fast biological dynamics. To increase the versatility of bioluminescence microscopy, we present an improved low-light microscope in combination with deep learning methods to image extremely photon-starved samples enabling subsecond exposures for timelapse and volumetric imaging. We apply our method to image subcellular dynamics in mouse embryonic stem cells, epithelial morphology during zebrafish development, and DAF-16 FoxO transcription factor shuttling from the cytoplasm to the nucleus under external stress. Finally, we concatenate neural networks for denoising and light-field deconvolution to resolve intracellular calcium dynamics in three dimensions of freely moving Caenorhabditis elegans.
• dc.description.sponsorship We would like to thank the NMSB and SLN lab, especially Jordi Andilla for discussions and suggestion on optical design and imaging procedures. We thank Senda Jiménez-Delgado, Hanna-Maria Häkkinen, Queralt Tolosa and Neus Sanfeliu-Cerdan for help with molecular biology, worm and zebrafish maintenance and imaging. V.R. acknowledges financial support from the Ministerio de Ciencia y Innovacion through the Plan Nacional (PID2020-117011GB-I00) and funding from the European Union’s Horizon EIC-ESMEA Pathfinder program under grant agreement No 101046620. M.K. acknowledges financial support from the ERC (MechanoSystems, 715243), HFSP (CDA00023/2018), Ministerio de Ciencia y Innovacion (PID2021-123812OB-I00 project funded by MCIN/AEI/10.13039/501100011033/FEDER, UE), FEDER (EQC2018-005048-P), “Severo Ochoa” program for Centres of Excellence in R&D (CEX2019-000910-S; RYC-2016-21062), from Fundació Privada Cellex, Fundació Mir-Puig, and from Generalitat de Catalunya through the CERCA and Research program (2017 SGR 1012), the Laserlab-Europe (H2020 GA no. 871124) in addition to funding through H2020 Marie Skłodowska-Curie Actions (754510 to A.G., and 847517 to L.F.M.C.).
• dc.format.mimetype application/pdf
• dc.identifier.citation Morales-Curiel LF, Gonzalez AC, Castro-Olvera G, Lin LL, El-Quessny M, Porta-de-la-Riva M, Severino J, Morera LB, Venturini V, Ruprecht V, Ramallo D, Loza-Alvarez P, Krieg M. Volumetric imaging of fast cellular dynamics with deep learning enhanced bioluminescence microscopy. Commun Biol. 2022 Dec 3;5(1):1330. DOI: 10.1038/s42003-022-04292-x
• dc.identifier.doi http://dx.doi.org/10.1038/s42003-022-04292-x
• dc.identifier.issn 2399-3642
• dc.identifier.uri http://hdl.handle.net/10230/55869
• dc.language.iso eng
• dc.publisher Nature Research
• dc.relation.ispartof Commun Biol. 2022 Dec 3;5(1):1330
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/715243
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-117011GB-I00
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2021-123812OB-I00
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/871124
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/754510
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/847517
• dc.rights © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Bioluminescence imaging
• dc.subject.keyword Image processing
• dc.subject.keyword Machine learning
• dc.subject.keyword Optogenetics
• dc.title Volumetric imaging of fast cellular dynamics with deep learning enhanced bioluminescence microscopy
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion