Yang Yitao, Cui Yang, Zeng Xin, Zhang Yubo, Loza Martin, Park Sung-Joon, Nakai Kenta
Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, the University of Tokyo, Tokyo, Japan.
Human Genome Center, the Institute of Medical Science, the University of Tokyo, Tokyo, Japan.
Nat Commun. 2025 Jan 27;16(1):1067. doi: 10.1038/s41467-025-56276-0.
Spatial transcriptomics is an essential application for investigating cellular structures and interactions and requires multimodal information to precisely study spatial domains. Here, we propose STAIG, a deep-learning model that integrates gene expression, spatial coordinates, and histological images using graph-contrastive learning coupled with high-performance feature extraction. STAIG can integrate tissue slices without prealignment and remove batch effects. Moreover, it is designed to accept data acquired from various platforms, with or without histological images. By performing extensive benchmarks, we demonstrate the capability of STAIG to recognize spatial regions with high precision and uncover new insights into tumor microenvironments, highlighting its promising potential in deciphering spatial biological intricates.
空间转录组学是研究细胞结构和相互作用的重要应用，需要多模态信息来精确研究空间区域。在这里，我们提出了STAIG，一种深度学习模型，它使用图对比学习结合高性能特征提取来整合基因表达、空间坐标和组织学图像。STAIG可以在无需预对齐的情况下整合组织切片并消除批次效应。此外，它设计用于接受从各种平台获取的数据，无论有无组织学图像。通过进行广泛的基准测试，我们证明了STAIG能够高精度识别空间区域并揭示肿瘤微环境的新见解，突出了其在解读空间生物学复杂性方面的潜在应用前景。
