Spatial gene expression at single-cell resolution from histology using deep learning with GHIST.

ABSTRACT: The increased use of spatially resolved transcriptomics provides new biological insights into disease mechanisms. However, the high cost and complexity of these methods are barriers to broader application. Consequently, methods have been created to predict spot-based gene expression from routinely collected histology images. Recent benchmarking showed that current methodologies have limited accuracy and spatial resolution, constraining translational capacity. Here, we introduce GHIST, a deep learning-based framework that predicts spatial gene expression at single-cell resolution by leveraging subcellular spatial transcriptomics and synergistic relationships between multiple layers of biological information. We validated GHIST using public datasets and The Cancer Genome Atlas data, demonstr

SUBMITTER: Fu X 

PROVIDER: S-EPMC12446070 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature