<p>Spatial transcriptomics has transformed the mapping of gene expression within intact tissues, yet current sequencing-based platforms are limited by coarse spot-level resolution and sparse sampling that leaves large interspot regions unmeasured. Here we introduce PanoSpace, a computational framework that integrates low-resolution spatial transcriptomics with high-resolution histology and matched single-cell RNA sequencing to reconstruct a continuous, single-cell-level map across entire tissue sections. Originally developed for tumors, PanoSpace accurately reconstructs cellular locations, cell identities and gene expression profiles, enabling detailed characterization of intracell-type heterogeneity and spatially organized cell–cell interactions. Application to breast and prostate cancers reveals complex cellular architectures and tumor microenvironment dynamics mediated by cancer-associated fibroblasts. Thanks to its modular design, PanoSpace can be seamlessly adapted to noncancerous tissues, as demonstrated by precise spatial reconstruction in mouse brain. Together, these results demonstrate that PanoSpace enables comprehensive spatial transcriptomic analysis and facilitates biological discovery.</p>