sCellST: a Multiple Instance Learning approach to predict single-cell gene expression from H&E images using spatial transcriptomics

Advancing our understanding of tissue organization and its disruptions in disease remains a key focus in biomedical research. Histological slides stained with Hematoxylin and Eosin (H&E) provide an abundant source of morphological information, while Spatial Transcriptomics (ST) enables detailed, spatially-resolved gene expression (GE) analysis, though at a high cost and with limited clinical accessibility. Predicting GE directly from H&E images using ST as a reference has thus become an attractive objective; however, current patch-based approaches lack single-cell resolution. Here, we present sCellST, a multiple-instance learning model that predicts GE by leveraging cell morphology alone, achieving remarkable predictive accuracy. When tested on a pancreatic ductal adenocarcinoma dataset, sCellST outperformed traditional methods, underscoring the value of basing predictions on single-cell images rather than tissue patches. Additionally, we demonstrate that sCellST can detect subtle morphological differences among cell types by utilizing marker genes in ovarian cancer samples. Our findings suggest that this approach could enable single-cell level GE predictions across large cohorts of H&E-stained slides, providing an innovative means to valorize this abundant resource in biomedical research.