Yu Zhuohan, Yang Yuning, Chen Xingjian, Wong Ka-Chun, Zhang Zhaolei, Zhao Yuming, Li Xiangtao
School of Artificial Intelligence, Jilin University, Jilin, 130012, China.
Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada.
Adv Sci (Weinh). 2025 Jan;12(3):e2410081. doi: 10.1002/advs.202410081. Epub 2024 Nov 28.
Recent advances in spatial transcriptomics have enabled simultaneous preservation of high-throughput gene expression profiles and the spatial context, enabling high-resolution exploration of distinct regional characterization in tissue. To effectively understand the underlying biological mechanisms within tissue microenvironments, there is a requisite for methods that can accurately capture external spatial heterogeneity and interpret internal gene regulation from spatial transcriptomics data. However, current methods for region identification often lack the simultaneous characterizing of spatial structure and gene regulation, thereby limiting the ability of spatial dissection and gene interpretation. Here, stDCL is developed, a dual graph contrastive learning method to identify spatial domains and interpret gene regulation in spatial transcriptomics data. stDCL adaptively incorporates gene expression data and spatial information via a graph embedding autoencoder, thereby preserving critical information within the latent embedding representations. In addition, dual graph contrastive learning is proposed to train the model, ensuring that the latent embedding representation closely resembles the actual spatial distribution and exhibits cluster similarity. Benchmarking stDCL against other state-of-the-art clustering methods using complex cortex datasets demonstrates its superior accuracy and effectiveness in identifying spatial domains. Our analysis of the imputation matrices generated by stDCL reveals its capability to reconstruct spatial hierarchical structures and refine differential expression assessment. Furthermore, it is demonstrated that the versatility of stDCL in interpretability of gene regulation, spatial heterogeneity at high resolution, and embryonic developmental patterns. In addition, it is also showed that stDCL can successfully annotate disease-associated astrocyte subtypes in Alzheimer's disease and unravel multiple relevant pathways and regulatory mechanisms.
空间转录组学的最新进展使得高通量基因表达谱和空间背景能够同时得以保留，从而能够对组织中不同区域特征进行高分辨率探索。为了有效理解组织微环境中的潜在生物学机制，需要有能够准确捕捉外部空间异质性并从空间转录组学数据中解读内部基因调控的方法。然而，当前用于区域识别的方法往往缺乏对空间结构和基因调控的同时表征，从而限制了空间剖析和基因解读的能力。在此，开发了stDCL，一种用于识别空间转录组学数据中的空间域并解读基因调控的双图对比学习方法。stDCL通过图嵌入自动编码器自适应地整合基因表达数据和空间信息，从而在潜在嵌入表示中保留关键信息。此外，还提出了双图对比学习来训练模型，确保潜在嵌入表示与实际空间分布紧密相似并表现出聚类相似性。使用复杂皮质数据集将stDCL与其他现有最先进的聚类方法进行基准测试，证明了其在识别空间域方面具有卓越的准确性和有效性。我们对由stDCL生成的插补矩阵的分析揭示了其重建空间层次结构和完善差异表达评估的能力。此外，还证明了stDCL在基因调控解释、高分辨率空间异质性和胚胎发育模式方面的通用性。此外，还表明stDCL能够成功注释阿尔茨海默病中与疾病相关的星形胶质细胞亚型，并揭示多种相关途径和调控机制。
