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arXiv · 2508.07225

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

Xuepeng Liu·Zheng Jiang·Pinan Zhu·Hanyu Liu·Chao Li
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Abstract

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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Xuepeng Liu, Zheng Jiang, Pinan Zhu, Hanyu Liu, Chao Li. 2025-08-10. HaDM-ST: Histology-Assisted Differential Modeling for Spatial Transcriptomics Generation. https://arxiv.org/abs/2508.07225

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