利用组织切片图像生成高分辨率基因表达图谱，提升空间精度与基因特异性。
HaDM-ST: Histology-Assisted Differential Modeling for Spatial Transcriptomics Generation
- 从H&E染色图像中提取关键语义特征，指导基因表达预测。
- 在扩散框架中实现像素级对齐，提升空间结构保真度。
- 针对不同基因设计自适应建模，增强表达模式的准确性。
空间转录组学（ST）可揭示基因表达的空间异质性，但受限于现有平台的分辨率。近期方法通过结合H&E染色组织切片提升分辨率，但仍存在三大挑战：(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) 建模跨表达通道的基因特异性变化。本文提出HaDM-ST（Histology-assisted Differential Modeling for ST Generation），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包含：(i) 语义蒸馏网络，用于从H&E图像中提取预测性特征；(ii) 空间对齐模块，强制与低分辨率ST实现像素级对应；(iii) 通道感知对抗学习器，实现细粒度基因级建模。在200个基因、多种组织和物种上的实验表明，HaDM-ST显著优于现有方法，在高分辨率预测中提升了空间保真度与基因层面的一致性。
原文摘要 · Abstract (English)
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.
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