多模态预训练框架CarHE从常规病理图像预测肿瘤的空间转录组
The Multimodal Pretraining Framework CarHE Predicts Spatial Transcriptomics in Tumors from Routine Pathology Images.
作者
作者单位
- Center for Excellence in Molecular Cell Science Shanghai, Shanghai China.
- Center for Excellence in Molecular Cell Science China.
- Center for Excellence in Molecular Cell Science Shanghai, China.
- Center for Excellence in Molecular Cell Science Shanghai China.
- Shanghai Pulmonary Hospital Shanghai China.
- Shanghai Pulmonary Hospital Shanghai, Shanghai China.
- Shanghai Pulmonary Hospital Shanghai, China China.
- Fudan University China.
- Shanghai Jiao Tong University Shanghai China.
摘要
中文
空间转录组分析提供空间分辨的基因表达数据，可深入了解复杂生物过程。然而，当前的空间转录组方法仍然费用高昂，且在分辨率、可扩展性和基因覆盖方面受限，限制了其在大规模研究中的广泛采用。在这里，我们开发了CarHE（针对苏木精-伊红图像的基因表达对比对齐），一种多模态预训练框架，可从常规H&E染色切片推断高维空间转录组谱。通过使用对比学习将细胞类型特异性转录组信息与组织学特征对齐，CarHE在评估的数据集和空间转录组平台上实现了高预测准确性。CarHE近似了与乳腺癌、肺癌、黑色素瘤和透明细胞肾细胞癌中三级淋巴结构（TLS）相关区域一致的空间组织病理微环境特征。此外，CarHE从2D图像推断近似的3D空间转录组背景，提供比2D可视化更具信息量的邻域背景。在880例肺癌患者队列中，CarHE衍生特征与无病生存期相关，并优于当前方法。总体而言，CarHE为基于H&E的空间推断提供了一种经济高效且可扩展的框架，支持向转化研究应用的进一步验证。
English
Spatial transcriptomic analyses provide spatially resolved gene expression data that can provide insights into complex biological processes. However, current spatial transcriptomics approaches remain financially prohibitive and restricted in resolution, scalability, and gene coverage, limiting broader adoption for large-scale studies. Here, we developed CarHE (contrastive alignment of gene expression for hematoxylin and eosin images), a multimodal pretraining framework that infers high-dimensional spatial transcriptomic profiles from routine H&E-stained slides. By using contrastive learning to align cell type-specific transcriptomic information with histological features, CarHE achieved high prediction accuracy across evaluated datasets and spatial transcriptomics platforms. CarHE approximated spatially organized pathological microenvironment features consistent with tertiary lymphoid structure (TLS)-associated regions in breast cancer, lung cancer, melanoma, and clear cell renal cell carcinoma. Additionally, CarHE inferred approximated 3D spatial transcriptomic context from 2D images, providing more informative neighborhood context than 2D visualization. In a cohort of 880 lung cancer patients, CarHE-derived features were associated with disease-free survival and outperformed current approaches. Overall, CarHE provides a cost-effective and scalable framework for H&E-based spatial inference, supporting further validation toward translational research applications.
分类与指标
- 研究类型
- AI/ML
- 病种
- 肺癌
- JCR 分区
- Q1
- 影响因子
- 22.6
- 新锐分区
- 1区