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

Ihuan Gunawan, Felix V. Kohane, Moumitha Dey, Kathy Nguyen, Ye Zheng, Daniel P. Neumann, Fatemeh Vafaee, Erik Meijering and John G. Lock ()
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Ihuan Gunawan: University of New South Wales
Felix V. Kohane: University of New South Wales
Moumitha Dey: University of New South Wales
Kathy Nguyen: University of New South Wales
Ye Zheng: University of New South Wales
Daniel P. Neumann: University of New South Wales
Fatemeh Vafaee: University of New South Wales
Erik Meijering: University of New South Wales
John G. Lock: University of New South Wales

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Standard immunofluorescence imaging captures just ~4 molecular markers (4-plex) per cell, limiting dissection of complex biology. Inspired by multimodal omics-based data integration approaches, we propose an Extensible Immunofluorescence (ExIF) framework that transforms carefully designed but easily produced panels of 4-plex immunofluorescence into a unified dataset with theoretically unlimited marker plexity, using generative deep learning-based virtual labelling. ExIF enables integrated analyses of complex cell biology, exemplified here through interrogation of the epithelial-mesenchymal transition (EMT), driving significant improvements in downstream quantitative analyses usually reserved for omics data, including: classification of cell phenotypes; manifold learning of cell phenotype heterogeneity; and pseudotemporal inference of molecular marker dynamics. Introducing data integration concepts from omics to microscopy, ExIF empowers life scientists to use routine 4-plex fluorescence microscopy to quantitatively interrogate complex, multimolecular single-cell processes in a manner that approaches the performance of multiplexed labelling methods whose uptake remains limited.

Date: 2025
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DOI: 10.1038/s41467-025-59592-7

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