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）的最新进展使得在组织内表征空间基因表达以进行发现研究成为可能。然而，当前的ST平台分辨率较低，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切片的组织学图像与已分析组织斑点的基因表达来增强ST图谱。然而，当前超分辨率方法受限于恢复不确定性和模式崩溃问题。尽管扩散模型在捕获多模态条件间的复杂交互方面已显示出潜力，但整合组织学图像与基因表达以生成超分辨率ST图谱仍是一个挑战。本文提出了一种跨模态条件扩散模型，用于在组织学图像的指导下对ST图谱进行超分辨率重建。具体而言，我们设计了一个具有跨模态自适应调制功能的多模态解缠网络，以利用来自组织学图像和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，以从组织学图像中提取层次化的细胞到组织信息。最后，我们提出了一种基于共表达的基因相关性图网络，以建模多个基因间的共表达关系。实验表明，在三个公共数据集上的ST超分辨率任务中，我们的方法优于其他最先进的方法。