Zhang Tianjiao, Li Shenghe, Zhang Ruolan, Zhang Hongfei, Zhao Zhongqian, Sun Hao, Wu Zhenao, Wang Guohua
College of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Xiangfang District, Harbin, 150040, China.
Faculty of Computing, Harbin Institute of Technology, No. 26 Hexing Road, Xiangfang District, Harbin, 150001, China.
Adv Sci (Weinh). 2025 Jun 20:e06176. doi: 10.1002/advs.202506176.
The rapid advancement of spatial transcriptomics has provided a critical data foundation for the high-resolution characterization of tissue spatial domains. Traditional methods for spatial domain identification primarily rely on gene expression data from sampled spots in low-resolution spatial transcriptomic data, often overlooking valuable information between spots that can be crucial for domain identification. Furthermore, these methods are limited by their focus on gene expression data from neighboring spots, without fully integrating prior knowledge of cell types within the tissue's spatial structure. To address these challenges, SGCD, a novel method for tissue spatial domain identification based on data interpolation and cell type deconvolution is proposed. SGCD utilizes interpolation techniques to estimate gene expression data for cells in the gaps between spots and applies deconvolution to extract cell type information from both spots and interstitial regions. By integrating gene expression, cell type, and spatial location data, SGCD achieves accurate delineation of complex spatial domains through graph contrastive learning. Evaluations on various publicly available datasets, including the human dorsolateral prefrontal cortex, mouse brain, pancreatic ductal adenocarcinoma, and breast cancer, demonstrate that SGCD significantly outperforms existing methods in both accuracy and detail, offering strong support for advancing the understanding of tissue functions and disease mechanisms.
空间转录组学的快速发展为组织空间域的高分辨率表征提供了关键的数据基础。传统的空间域识别方法主要依赖于低分辨率空间转录组数据中采样点的基因表达数据，常常忽略了采样点之间对域识别至关重要的有价值信息。此外，这些方法局限于关注相邻采样点的基因表达数据，没有充分整合组织空间结构内细胞类型的先验知识。为应对这些挑战，提出了SGCD，一种基于数据插值和细胞类型反卷积的组织空间域识别新方法。SGCD利用插值技术估计采样点之间间隙中细胞的基因表达数据，并应用反卷积从采样点和间隙区域提取细胞类型信息。通过整合基因表达、细胞类型和空间位置数据，SGCD通过图对比学习实现了对复杂空间域的精确描绘。对包括人类背外侧前额叶皮层、小鼠大脑、胰腺导管腺癌和乳腺癌在内的各种公开可用数据集的评估表明，SGCD在准确性和细节方面均显著优于现有方法，为推进对组织功能和疾病机制的理解提供了有力支持。
