<i>ROSIE</i>: AI generation of multiplex immunofluorescence staining from histopathology images.

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 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 such

SUBMITTER: Wu E 

PROVIDER: S-EPMC11601356 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature