标题： HaDM-ST：组织学辅助差异建模用于空间转录组生成 显示英文标题

标题： HaDM-ST: Histology-Assisted Differential Modeling for Spatial Transcriptomics Generation

摘要： 空间转录组学（ST）揭示了基因表达的空间异质性，但其分辨率受限于当前平台。 最近的方法通过苏木精和伊红（H&E）染色的组织学来提高分辨率，但仍有三个主要挑战存在：（1）从视觉复杂的H&E图像中分离出与表达相关的特征；（2）在基于扩散的框架中实现空间精确的多模态对齐；（3）建模不同表达通道中的基因特异性变化。 我们提出了HaDM-ST（基于组织学的ST生成差分建模），这是一种以H&E图像和低分辨率ST为条件的高分辨率ST生成框架。HaDM-ST包括：（i）一个语义蒸馏网络，用于从H&E中提取预测线索；（ii）一个空间对齐模块，强制与低分辨率ST进行像素级对应；（iii）一个通道感知的对抗学习者，用于细粒度的基因水平建模。 在多种组织和物种中的200个基因实验表明，HaDM-ST始终优于先前方法，在高分辨率ST预测中提高了空间保真度和基因水平的一致性。 显示英文摘要

摘要： Spatial transcriptomics (ST) reveals spatial heterogeneity of gene expression, yet its resolution is limited by current platforms. Recent methods enhance resolution via H&E-stained histology, but three major challenges persist: (1) isolating expression-relevant features from visually complex H&E images; (2) achieving spatially precise multimodal alignment in diffusion-based frameworks; and (3) modeling gene-specific variation across expression channels. We propose HaDM-ST (Histology-assisted Differential Modeling for ST Generation), a high-resolution ST generation framework conditioned on H&E images and low-resolution ST. HaDM-ST includes: (i) a semantic distillation network to extract predictive cues from H&E; (ii) a spatial alignment module enforcing pixel-wise correspondence with low-resolution ST; and (iii) a channel-aware adversarial learner for fine-grained gene-level modeling. Experiments on 200 genes across diverse tissues and species show HaDM-ST consistently outperforms prior methods, enhancing spatial fidelity and gene-level coherence in high-resolution ST predictions.