ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images

Eric Wu (), Matthew Bieniosek, Zhenqin Wu, Nitya Thakkar, Gregory W. Charville, Ahmad Makky, Christian M. Schürch, Jeroen R. Huyghe, Ulrike Peters, Christopher I. Li, Li Li, Hannah Giba, Vivek Behera, Arjun Raman, Alexandro E. Trevino (), Aaron T. Mayer () and James Zou ()
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Eric Wu: Enable Medicine
Matthew Bieniosek: Enable Medicine
Zhenqin Wu: Enable Medicine
Nitya Thakkar: Stanford University
Gregory W. Charville: Stanford University
Ahmad Makky: University Hospital and Comprehensive Cancer Center Tübingen
Christian M. Schürch: University Hospital and Comprehensive Cancer Center Tübingen
Jeroen R. Huyghe: Fred Hutchinson Cancer Center
Ulrike Peters: Fred Hutchinson Cancer Center
Christopher I. Li: Fred Hutchinson Cancer Center
Li Li: Ochsner Health
Hannah Giba: University of Chicago
Vivek Behera: University of Chicago
Arjun Raman: University of Chicago
Alexandro E. Trevino: Enable Medicine
Aaron T. Mayer: Enable Medicine
James Zou: Enable Medicine

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Hematoxylin and eosin (H&E) is a common and inexpensive histopathology assay. Though widely used and information-rich, it cannot directly inform about specific molecular markers, which require additional experiments to assess. To address this gap, we present ROSIE, a deep-learning framework that computationally imputes the expression and localization of dozens of proteins from H&E images. Our model is trained on a dataset of over 1300 paired and aligned H&E and multiplex immunofluorescence (mIF) samples from over a dozen tissues and disease conditions, spanning over 16 million cells. Validation of our in silico mIF staining method on held-out H&E samples demonstrates that the predicted biomarkers are effective in identifying cell phenotypes, particularly distinguishing lymphocytes such as B cells and T cells, which are not readily discernible with H&E staining alone. Additionally, ROSIE facilitates the robust identification of stromal and epithelial microenvironments and immune cell subtypes like tumor-infiltrating lymphocytes (TILs), which are important for understanding tumor-immune interactions and can help inform treatment strategies in cancer research.

Date: 2025
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DOI: 10.1038/s41467-025-62346-0

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