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Protocol to computationally average confocal images of zebrafish lymphatic vessels to identify phenotypes
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.ORCID iD: 0000-0002-8717-5190
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.ORCID iD: 0000-0003-4028-8443
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.ORCID iD: 0000-0002-6841-8900
2025 (English)In: STAR Protocols, E-ISSN 2666-1667, Vol. 6, no 1, article id 103632Article in journal (Refereed) Published
Abstract [en]

Characterization of zebrafish mutants is routinely time consuming as morphological phenotypes can be subtle or variable, thus hampering robust quantification. Here, we present a protocol to identify the overall phenotype by overlaying images of lymphatic vessels acquired from multiple zebrafish embryos of a genetic background. We describe steps for preparing samples, obtaining confocal images, and their computational alignment. In addition, we supply sample images for testing. For complete details on the use and execution of this protocol, please refer to Arnold et al.1

Place, publisher, year, edition, pages
Cell Press, 2025. Vol. 6, no 1, article id 103632
Keywords [en]
image, samples, obtaining, alignment, computational, images
National Category
Radiology and Medical Imaging Computer graphics and computer vision
Identifiers
URN: urn:nbn:se:uu:diva-551740DOI: 10.1016/j.xpro.2025.103632ISI: 001425140300001PubMedID: 39923241Scopus ID: 2-s2.0-85217081494OAI: oai:DiVA.org:uu-551740DiVA, id: diva2:1947694
Funder
Knut and Alice Wallenberg Foundation, 2017.0144Knut and Alice Wallenberg Foundation, 2024.0181Ragnar Söderbergs stiftelse, M13/17Swedish Research Council, 2016-01437Swedish Research Council, 2023-02459Kjell and Marta Beijer FoundationAvailable from: 2025-03-26 Created: 2025-03-26 Last updated: 2025-03-26Bibliographically approved

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Arnold, HannahPanara, VirginiaAllalou, AminKoltowska, Katarzyna
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Vascular BiologyScience for Life Laboratory, SciLifeLabComputerized Image Analysis and Human-Computer InteractionDepartment of Immunology, Genetics and PathologyDivision Vi3
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STAR Protocols
Radiology and Medical ImagingComputer graphics and computer vision

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CiteExportLink to record
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