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.

翻译：空间转录组学（ST）的最新进展使得能够在组织内表征空间基因表达，从而支持发现性研究。然而，当前的ST平台存在分辨率低的问题，阻碍了对空间基因表达的深入理解。超分辨率方法有望通过整合组织学图像与已分析组织点的基因表达来提升ST图谱。然而，当前超分辨率方法受限于重建不确定性及模式崩溃问题。尽管扩散模型在捕捉多模态条件之间的复杂交互方面展现出潜力，但如何整合组织学图像与基因表达以生成超分辨率ST图谱仍具挑战性。本文提出了一种跨模态条件扩散模型，在组织学图像引导下实现ST图谱的超分辨率。具体而言，我们设计了一个多模态解耦网络，通过跨模态自适应调制来利用组织学图像与空间基因表达的互补信息。此外，我们提出了一种动态交叉注意力建模策略，从组织学图像中提取层级化的细胞到组织信息。最后，我们构建了一个基于共表达的基因关联图网络，以建模多个基因的共表达关系。实验表明，在三个公开数据集上的ST超分辨率任务中，我们的方法优于其他最先进方法。