Wang Qing, Chen Wen-Jie, Su Jing, Wang Guangyu, Song Qianqian
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32611, United States.
School of Biological and Behavioural Sciences, Queen Mary University of London, London, E1 4NS, United Kingdom.
Bioinformatics. 2025 Jul 1;41(7). doi: 10.1093/bioinformatics/btaf363.
Histopathology, particularly hematoxylin and eosin (H&E) staining, is pivotal for diagnosing and characterizing pathological conditions by visualizing tissue morphology. However, H&E-stained images inherently lack molecular resolution, necessitating costly and labor-intensive technologies like spatial transcriptomics (ST) to uncover spatial gene expression patterns. There is a critical need for scalable computational methods that can bridge this imaging-transcriptomics gap.
We present histology-enhanced contrastive learning for imputation of profiles (HECLIP), an innovative deep learning framework designed to infer spatial gene expression profiles directly from H&E-stained histology images. HECLIP employs an image-centric contrastive learning strategy to capture morphological features relevant to molecular expression. By minimizing dependence on ST data, HECLIP enables accurate and biologically meaningful predictions of gene expression. Extensive benchmarking on publicly available datasets demonstrates that HECLIP outperforms existing methods. Ablation studies confirm the contribution of each model component to its overall performance.
The source code for HECLIP is freely available at: https://github.com/QSong-github/HECLIP.
组织病理学，尤其是苏木精和伊红（H&E）染色，通过可视化组织形态对于诊断和表征病理状况至关重要。然而，H&E染色图像本质上缺乏分子分辨率，需要像空间转录组学（ST）这样昂贵且 labor-intensive technologies（此处有误，推测应为labor-intensive techniques，意为劳动密集型技术）来揭示空间基因表达模式。迫切需要能够弥合这种成像 - 转录组学差距的可扩展计算方法。
我们提出了用于轮廓插补的组织学增强对比学习（HECLIP），这是一种创新的深度学习框架，旨在直接从H&E染色的组织学图像推断空间基因表达谱。HECLIP采用以图像为中心的对比学习策略来捕获与分子表达相关的形态特征。通过最小化对ST数据的依赖，HECLIP能够对基因表达进行准确且具有生物学意义的预测。在公开可用数据集上的广泛基准测试表明，HECLIP优于现有方法。消融研究证实了每个模型组件对其整体性能的贡献。
HECLIP的源代码可在以下网址免费获取：https://github.com/QSong-github/HECLIP。
