Unlocking single-cell level and whole-slide insights in spatial transcriptomics with PanoSpace

Spatial transcriptomics has significantly advanced our ability to map gene expression within native tissue contexts. However, current low-resolution technologies are constrained by limited spatial resolution and tissue coverage. We present PanoSpace, a novel computational framework that integrates low-resolution spatial transcriptomics data with high-resolution histological images and matched single-cell RNA sequencing references. PanoSpace achieves comprehensive single-cell level and whole-tissue analysis by accurately inferring spatial localization, cell type, and gene expression for all cells across entire tissue slides. It also facilitates exploration of intra-cell-type heterogeneity and cell-cell interactions within spatial contexts. Application of PanoSpace to breast, prostate, and cervical cancer tissues reveals detailed cell-type distributions and gene expression patterns with unprecedented resolution and coverage. Furthermore, through analysis of interactions with cancer-associated fibroblasts, PanoSpace uncovers intra-cell-type heterogeneity and provides novel insights into tumor microenvironment dynamics. These findings highlight PanoSpace as a powerful tool for offering insights beyond the reach of existing technologies and computational methods.