2025年11月26日出版的《自然—细胞生物学》杂志发表了美国科学家的一项最新研究成果。来自宾夕法尼亚大学的李明耀研究小组揭示了智能空间组学（S2组学）优化感兴趣区域选择，以捕获不同组织中的分子异质性。

在这里，该课题组人员提出了智能空间组学（S2组学），这是一种端到端工作流，可以自动从组织学图像中选择ROI，目标是最大化ROI中的分子信息内容。通过对多个空间组学平台和组织类型的综合评估，课题组人员证明了S2组学能够实现系统性和可重复性的ROI选择，并增强下游生物发现的灵活性和影响力。目前，S2组学是一个端到端工作流程，可自动识别组织学图像中感兴趣的区域，以最大限度地在空间组学实验中捕获分子信息。

研究人员表示，空间组学技术在保留原生组织结构的同时实现了高分辨率分子分析，从而改变了生物医学研究。这些进展为研究组织结构和功能提供了前所未有的见解。然而，空间组学实验的高成本和时间密集性质需要仔细的实验设计，特别是在从大组织切片中选择感兴趣区域（ROIs）时。目前，ROI的选择是手工进行的，这引入了主观性、不一致性和缺乏可重复性。先前的研究表明空间分子模式与组织学特征之间存在很强的相关性，这表明可以利用容易获得且成本低廉的组织学图像来指导空间组学实验。

附：英文原文

Title: Smart spatial omics (S2-omics) optimizes region of interest selection to capture molecular heterogeneity in diverse tissues

Author: Yuan, Musu, Jin, Kaitian, Yan, Hanying, Schroeder, Amelia, Luo, Chunyu, Yao, Sicong, Dumoulin, Bernhard, Levinsohn, Jonathan, Luo, Tianhao, Clemenceau, Jean R., Jang, Inyeop, Kim, Minji, Liu, Yunhe, Deng, Minghua, Furth, Emma E., Wilson, Parker, Nayak, Anupma, Lubo, Idania, Solis Soto, Luisa Maren, Wang, Linghua, Park, Jeong Hwan, Susztak, Katalin, Hwang, Tae Hyun, Li, Mingyao

Issue&Volume: 2025-11-26

Abstract: Spatial omics technologies have transformed biomedical research by enabling high-resolution molecular profiling while preserving the native tissue architecture. These advances provide unprecedented insights into tissue structure and function. However, the high cost and time-intensive nature of spatial omics experiments necessitate careful experimental design, particularly in selecting regions of interest (ROIs) from large tissue sections. Currently, ROI selection is performed manually, which introduces subjectivity, inconsistency and a lack of reproducibility. Previous studies have shown strong correlations between spatial molecular patterns and histological features, suggesting that readily available and cost-effective histology images can be leveraged to guide spatial omics experiments. Here we present Smart Spatial omics (S2-omics), an end-to-end workflow that automatically selects ROIs from histology images with the goal of maximizing molecular information content in the ROIs. Through comprehensive evaluations across multiple spatial omics platforms and tissue types, we demonstrate that S2-omics enables systematic and reproducible ROI selection and enhances the robustness and impact of downstream biological discovery. Yuan et al. present S2-omics, an end-to-end workflow that automatically identifies regions of interest in histology images to maximize molecular information capture in spatial omics experiments.

DOI: 10.1038/s41556-025-01811-w

Source: https://www.nature.com/articles/s41556-025-01811-w