biostudies-literatureUnknownWu ENCI NIH HHSHematoxylin 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 1000 paired and aligned H&E and multiplex immunofluorescence (mIF) samples from 20 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 suchbioRxiv : the preprint server for biology2024.11.10.622859https://www.ebi.ac.uk/biostudies/studies/S-EPMC11601356biostudies-literatureROSIE: AI generation of multiplex immunofluorescence staining from histopathology images.PMC11601356P20 CA252733P50 CA285275R01 CA280639Bieniosek MZou JWu ECharville GWLi LWu ZGiba HSchurch CHuyghe JRMayer ATPeters ULi CIMakky ABehera VRaman AThakkar NTrevino AEfalseROSIE: AI generation of multiplex immunofluorescence staining from histopathology images.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 1000 paired and aligned H&E and multiplex immunofluorescence (mIF) samples from 20 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 such2024-01-01T00:00:00Z2024 Nov2026-04-07T20:19:10.377Z2025-04-03T23:57:13.862ZS-EPMC116013563960571110.1101/2024.11.10.622859