用空间转录组知识指导病理切片分析，提升无分子数据下的组织结构解析能力
Cross-Modal Knowledge Distillation from Spatial Transcriptomics to Histology
- 通过跨模态知识蒸馏，将转录组的组织微区结构迁移到仅依赖染色图像的模型中
- 在多种组织和疾病中，模型对微区结构的识别准确率显著高于传统方法
- 适合病理分析、生物研究等需高精度组织结构解析的场景
空间转录组提供丰富的分子层面组织结构信息，可无监督发现具有特定细胞组成与功能的空间微区，对生物学研究和临床诊断均有意义。然而其成本高、数据稀缺，而H&E染色切片虽丰富但信息粒度较粗。本文提出利用成对的空间转录组与H&E数据，通过跨模态知识蒸馏，将转录组推导的微区结构迁移至仅依赖染色图像的模型。在多个组织类型和疾病背景下，该蒸馏模型与转录组推导的微区结构一致性显著优于仅使用图像特征的无监督基线方法，并经细胞类型分析验证了其生物学合理性。训练阶段需配对数据，推理时仅需历史切片图像，无需转录组输入。
原文摘要 · Abstract (English)
Spatial transcriptomics provides a molecularly rich description of tissue organization, enabling unsupervised discovery of tissue niches -- spatially coherent regions of distinct cell-type composition and function that are relevant to both biological research and clinical interpretation. However, spatial transcriptomics remains costly and scarce, while H&E histology is abundant but carries a less granular signal. We propose to leverage paired spatial transcriptomics and H&E data to transfer transcriptomics-derived niche structure to a histology-only model via cross-modal distillation. Across multiple tissue types and disease contexts, the distilled model achieves substantially higher agreement with transcriptomics-derived niche structure than unsupervised morphology-based baselines trained on identical image features, and recovers biologically meaningful neighborhood composition as confirmed by cell-type analysis. The resulting framework leverages paired spatial transcriptomic and H&E data during training, and can then be applied to held-out tissue regions using histology alone, without any transcriptomic input at inference time.
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