Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images

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• dc.contributor.author Wernersson, Erik
• dc.contributor.author Gelali, Eleni
• dc.contributor.author Girelli, Gabriele
• dc.contributor.author Wang, Su
• dc.contributor.author Castillo, David
• dc.contributor.author Mattsson Langseth, Christoffer
• dc.contributor.author Verron, Quentin
• dc.contributor.author Nguyen, Huy Q.
• dc.contributor.author Chattoraj, Shyamtanu
• dc.contributor.author Martinez Casals, Anna
• dc.contributor.author Blom, Hans
• dc.contributor.author Lundberg, Emma
• dc.contributor.author Nilsson, Mats
• dc.contributor.author Martí Renom, Marc A.
• dc.contributor.author Wu, Chao-Ting
• dc.contributor.author Crosetto, Nicola
• dc.contributor.author Bienko, Magda
• dc.date.accessioned 2024-09-13T07:21:45Z
• dc.date.available 2024-09-13T07:21:45Z
• dc.date.issued 2024
• dc.description.abstract Microscopy-based spatially resolved omic methods are transforming the life sciences. However, these methods rely on high numerical aperture objectives and cannot resolve crowded molecular targets, limiting the amount of extractable biological information. To overcome these limitations, here we develop Deconwolf, an open-source, user-friendly software for high-performance deconvolution of widefield fluorescence microscopy images, which efficiently runs on laptop computers. Deconwolf enables accurate quantification of crowded diffraction limited fluorescence dots in DNA and RNA fluorescence in situ hybridization images and allows robust detection of individual transcripts in tissue sections imaged with ×20 air objectives. Deconvolution of in situ spatial transcriptomics images with Deconwolf increased the number of transcripts identified more than threefold, while the application of Deconwolf to images obtained by fluorescence in situ sequencing of barcoded Oligopaint probes drastically improved chromosome tracing. Deconwolf greatly facilitates the use of deconvolution in many bioimaging applications.
• dc.description.sponsorship The authors acknowledge the Spatial Proteomics National Facility at SciLifeLab, funded by SciLifeLab and the Swedish Research Council (grant no. VR-RFI 2019-00217) as a member of the National Microscopy Infrastructure (NMI). The authors also acknowledge the Advanced Light Microscopy facility at KTH-SciLifeLab, also part of the National Microscopy Infrastructure NMI (grant no. VR-RFI 2019-00217), for support with STED and confocal imaging. The authors thank Atlas Antibodies for providing the tissue sample used to compare images acquired using a widefield and a confocal microscope. This work was supported by funding from the Swedish Cancer Research Foundation (Cancerfonden, grant no. CAN 2018/604) and the Swedish Research Council (grant no. 2019-01238) to M.N.; by funding from the Knut and Alice Wallenberg Foundation (grant no. KAW 2018.0172), the Erling Persson Foundation, and the European Union’s Horizon 2020 research and innovation programme (grant no. EPIC-XS_823839) to E.L.; by funding from the National Human Genome Research Institute of the National Institutes of Health (grant no. RM1HG011016) to C.-t.W. and M.A.M.-R.; by funding from the National Institutes of Health (grants no. RM1HG011016; R01HD091797; R01GM123289) and Bruker Nano Inc. to C.-t.W.; by funding from the Swedish Cancer Research Foundation (Cancerfonden, grant no. 21 1785 Pj), the Swedish Foundation for Strategic Research (SSF, grant no. BD15_0095), the Swedish Research Council (grant. no. 2022-00721), and the Strategic Research Programme in Cancer (StratCan, now Cancer Research KI) at Karolinska Institutet (grant. no. 2201) to N.C.; and by funding from the Ragnar Söderberg Foundation (Fellows in Medicine 2016), the Human Frontier Science Program (HFSP Career Development Award), the Swedish Cancer Research Foundation (Cancerfonden, grant no. 22 2240 Pj 01 H), the Swedish Research Council (grant. no. 2020-02657_3), and the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant no. StG-2016_GENOMIS_715727) to M.B.
• dc.format.mimetype application/pdf
• dc.identifier.citation Wernersson E, Gelali E, Girelli G, Wang S, Castillo D, Mattsson Langseth C, et al. Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images. Nat Methods. 2024 Jul;21(7):1245-56. DOI: 10.1038/s41592-024-02294-7
• dc.identifier.doi http://dx.doi.org/10.1038/s41592-024-02294-7
• dc.identifier.issn 1548-7091
• dc.identifier.uri http://hdl.handle.net/10230/61081
• dc.language.iso eng
• dc.publisher Nature Research
• dc.relation.ispartof Nat Methods. 2024 Jul;21(7):1245-56
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/823839
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/715727
• dc.rights © The Author(s) 2024. 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, 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 Software
• dc.subject.keyword Wide-field fluorescence microscopy
• dc.title Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion