2026-04-17T01:39:43Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/558692025-12-12T02:58:06Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Morales-Curiel, Luis Felipe author Gonzalez, Adriana Carolina author Castro-Olvera, Gustavo author Lin, Li-Chun Lynn author El-Quessny, Malak author Porta-de-la-Riva, Montserrat author Severino, Jacqueline, 1990- author Batlle Masó, Laura, 1993- author Venturini, Valeria author Ruprecht, Verena author Ramallo, Diego author Loza-Alvarez, Pablo author Krieg, Michael author 2023-02-23T07:03:44Z 2023-02-23T07:03:44Z 2022 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. 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.). Bioluminescence imaging Image processing Machine learning Optogenetics Volumetric imaging of fast cellular dynamics with deep learning enhanced bioluminescence microscopy