用空间多组学解析生物系统复杂性，助力精准医学发展
Unleashing the power of computational insights in revealing the complexity of biological systems in the new era of spatial multi-omics
- 结合机器学习与多组学整合建模，揭示细胞空间组织规律
- 可识别器官发育中的细胞拓扑关系及肿瘤发生的关键分子特征
- 适合生物医学研究者、精准医疗开发者参考
近年来，空间组学技术的进展极大提升了对生物系统的分辨率。通过保留分子测量的空间位置信息，这些方法实现了对细胞异质性、组织结构及发育生物学、神经科学、肿瘤学和进化研究中动态过程的全面解析。本文系统回顾了技术与计算算法的持续进步，推动对哺乳动物组织和器官结构与机制的深入理解。先进机器学习算法与多组学整合模型能解码复杂生物过程，包括器官发育中细胞的空间组织与拓扑关系，以及肿瘤发生与转移的关键分子标志物和调控网络。最后，展望了空间组学在精准医学中的技术革新与建模方向。
原文摘要 · Abstract (English)
Recent advances in spatial omics technologies have revolutionized our ability to study biological systems with unprecedented resolution. By preserving the spatial context of molecular measurements, these methods enable comprehensive mapping of cellular heterogeneity, tissue architecture, and dynamic biological processes in developmental biology, neuroscience, oncology, and evolutionary studies. This review highlights a systematic overview of the continuous advancements in both technology and computational algorithms that are paving the way for a deeper, more systematic comprehension of the structure and mechanisms of mammalian tissues and organs by using spatial multi-omics. Our viewpoint demonstrates how advanced machine learning algorithms and multi-omics integrative modeling can decode complex biological processes, including the spatial organization and topological relationships of cells during organ development, as well as key molecular signatures and regulatory networks underlying tumorigenesis and metastasis. Finally, we outline future directions for technological innovation and modeling insights of spatial omics in precision medicine.
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