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; et al. (Nature Communications)
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.