Eldad David Shulman

Abstract Spatial transcriptomics (ST) is transforming our understanding of tumor heterogeneity by enabling high-resolution, location-specific mapping of gene expression across tumors and their microenvironment. However, the associated high cost of the assay has limited cohort size and hence large-scale biomarker discovery. Here we present Path2Space, a deep learning approach that predicts spatial gene expression directly from histopathology slides.

Abstract The rise of spatial transcriptomics (ST) is transforming our understanding of tumor heterogeneity by enabling high-resolution, location-specific mapping of gene expression across the tumor microenvironment. However, the translational potential of spatial transcriptomics is still limited by its high cost, hindering the assembly of large patient cohorts needed for robust biomarker discovery.