Using spatial proteomics to enhance cell type assignments in histology images

Hematoxylin and eosin (H&E) staining has been a standard in clinical histopathology for many decades but lacks molecular detail. Advances in multiplexed spatial proteomics imaging allow cell types and tissues to be annotated by their expression patterns as well as their morphological features. However, these technologies are at present unavailable in most clinical settings. In this work, we present a machine learning framework that leverages histopathology foundation models and paired H&E and spatial proteomic imaging data to enable enhanced cell type annotation on H&E-only datasets. We trained and evaluated our method on kidney datasets with paired H&E and spatial proteomic imaging data and found that models trained using our methods outperform models trained directly on the imaging data. We also show how our framework can be used to study biological differences between two major kidney diseases.