  The recent advancement of spatial transcriptomics (ST) allows to characterize
spatial gene expression within tissue for discovery research. However, current
ST platforms suffer from low resolution, hindering in-depth understanding of
spatial gene expression. Super-resolution approaches promise to enhance ST maps
by integrating histology images with gene expressions of profiled tissue spots.
However, current super-resolution methods are limited by restoration
uncertainty and mode collapse. Although diffusion models have shown promise in
capturing complex interactions between multi-modal conditions, it remains a
challenge to integrate histology images and gene expression for super-resolved
ST maps. This paper proposes a cross-modal conditional diffusion model for
super-resolving ST maps with the guidance of histology images. Specifically, we
design a multi-modal disentangling network with cross-modal adaptive modulation
to utilize complementary information from histology images and spatial gene
expression. Moreover, we propose a dynamic cross-attention modelling strategy
to extract hierarchical cell-to-tissue information from histology images.
Lastly, we propose a co-expression-based gene-correlation graph network to
model the co-expression relationship of multiple genes. Experiments show that
our method outperforms other state-of-the-art methods in ST super-resolution on
three public datasets.
