Extensible Immunofluorescence (ExIF) accessibly generates high-plexity datasets by integrating standard 4-plex imaging data.

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Title: Extensible Immunofluorescence (ExIF) accessibly generates high-plexity datasets by integrating standard 4-plex imaging data.
Authors: Gunawan I; School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia.; School of Computer Science and Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, Australia., Kohane FV; School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia., Dey M; School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia., Nguyen K; School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia., Zheng Y; School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia.; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia., Neumann DP; School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia., Vafaee F; School of Biotechnology and Biomolecular Sciences, Faculty of Science, University of New South Wales, Sydney, NSW, Australia.; UNSW Data Science Hub, University of New South Wales, Sydney, NSW, Australia.; UNSW Artificial Intelligence Institute, University of New South Wales, Sydney, NSW, Australia., Meijering E; School of Computer Science and Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, Australia.; UNSW Data Science Hub, University of New South Wales, Sydney, NSW, Australia.; UNSW Artificial Intelligence Institute, University of New South Wales, Sydney, NSW, Australia., Lock JG; School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia. john.lock@unsw.edu.au.; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia. john.lock@unsw.edu.au.; UNSW Data Science Hub, University of New South Wales, Sydney, NSW, Australia. john.lock@unsw.edu.au.; UNSW Artificial Intelligence Institute, University of New South Wales, Sydney, NSW, Australia. john.lock@unsw.edu.au.
Source: Nature communications [Nat Commun] 2025 May 17; Vol. 16 (1), pp. 4606. Date of Electronic Publication: 2025 May 17.
Publication Type: Journal Article
Journal Info: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE
Database: MEDLINE Ultimate
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ISSN:2041-1723
DOI:10.1038/s41467-025-59592-7