Shi Zhiceng, Zhu Fangfang, Min Wenwen
School of Information Science and Engineering, Yunnan University, Kunming, 650500, Yunnan, China.
School of Health and Nursing, Yunnan Open University, Kunming, 650599, Yunnan, China.
Methods. 2025 Feb;234:187-195. doi: 10.1016/j.ymeth.2024.11.016. Epub 2025 Jan 2.
Spatial transcriptomics has significantly advanced the measurement of spatial gene expression in the field of biology. However, the high cost of ST limits its application in large-scale studies. Using deep learning to predict spatial gene expression from H&E-stained histology images offers a more cost-effective alternative, but existing methods fail to fully leverage the multimodal information provided by Spatial transcriptomics and pathology images. In response, this paper proposes STMCL, a novel multimodal contrastive learning framework. STMCL integrates multimodal information, including histology images, gene expression features of spots, and their locations, to accurately infer spatial gene expression profiles. We tested four different types of multi-slice spatial transcriptomics datasets generated by the 10X Genomics platform. The results indicate that STMCL has advantages over baseline methods in predicting spatial gene expression profiles. Furthermore, STMCL is capable of capturing cancer-specific highly expressed genes and preserving gene expression patterns while maintaining the original spatial structure of gene expression. Our code is available at https://github.com/wenwenmin/STMCL.
空间转录组学在生物学领域极大地推动了空间基因表达的测量。然而，空间转录组学的高成本限制了其在大规模研究中的应用。利用深度学习从苏木精-伊红（H&E）染色的组织学图像预测空间基因表达提供了一种更具成本效益的替代方法，但现有方法未能充分利用空间转录组学和病理图像提供的多模态信息。作为回应，本文提出了STMCL，一种新颖的多模态对比学习框架。STMCL整合了多模态信息，包括组织学图像、斑点的基因表达特征及其位置，以准确推断空间基因表达谱。我们测试了由10X基因组学平台生成的四种不同类型的多层空间转录组学数据集。结果表明，STMCL在预测空间基因表达谱方面优于基线方法。此外，STMCL能够捕获癌症特异性高表达基因并保留基因表达模式，同时保持基因表达的原始空间结构。我们的代码可在https://github.com/wenwenmin/STMCL获取。
