SciSt: single-cell reference-informed spatial gene expression prediction from pathological images.

ABSTRACT: The widespread application of spatial transcriptomics in uncovering disease mechanisms remains limited by the scarcity of samples and the high experimental costs, which have not declined substantially in recent years. Unlocking the vast resources of clinical H&E-stained images could provide an efficient and cost-effective alternative for large-scale spatial analysis. However, predicting spatial gene expression from histopathological images remains challenging, as existing end-to-end frameworks often fail to capture the intrinsic transcriptomic structures observed in real transcriptomics data. To address this, we developed SciSt, a deep learning framework that predicts spatial gene expression by integrating pathological features with biologically informed initial gene expressions. These ini

SUBMITTER: Li Y 

PROVIDER: S-EPMC12632197 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature