美国斯坦福大学Garry P. Nolan团队在研究中取得进展。他们开发出了利用多组学和生态空间分析定量表征组织状态。这一研究成果发表在2025年4月1日出版的国际学术期刊《自然—遗传学》上。

课题组研究人员提出MESA（多组学和生态空间分析），这是一个从生态学概念中汲取灵感的框架，用于描述组织状态的功能和空间变化。MESA引入了系统量化空间多样性和识别热点的指标，将空间模式与表型结果（包括疾病进展）联系起来。

此外，MESA整合了空间和单细胞多组学数据，以促进对细胞邻域及其在组织微环境中的空间相互作用的深入分子理解。将MESA应用于不同的数据集展示了它比以前的方法带来的额外见解，包括新确定的空间结构和与疾病状态相关的关键细胞群。MESA作为Python包提供了一个通用的框架，用于在健康和疾病的空间组学中对组织结构进行定量解码。

据悉，组织中细胞的空间组织是生物功能的基础，空间分析技术的最新进展增强了分析这种排列以研究生物过程和疾病进展的能力。

附：英文原文

Title: Quantitative characterization of tissue states using multiomics and ecological spatial analysis

Author: Ding, Daisy Yi, Tang, Zeyu, Zhu, Bokai, Ren, Hongyu, Shalek, Alex K., Tibshirani, Robert, Nolan, Garry P.

Issue&Volume: 2025-04-01

Abstract: The spatial organization of cells in tissues underlies biological function, and recent advances in spatial profiling technologies have enhanced our ability to analyze such arrangements to study biological processes and disease progression. We propose MESA (multiomics and ecological spatial analysis), a framework drawing inspiration from ecological concepts to delineate functional and spatial shifts across tissue states. MESA introduces metrics to systematically quantify spatial diversity and identify hot spots, linking spatial patterns to phenotypic outcomes, including disease progression. Furthermore, MESA integrates spatial and single-cell multiomics data to facilitate an in-depth, molecular understanding of cellular neighborhoods and their spatial interactions within tissue microenvironments. Applying MESA to diverse datasets demonstrates additional insights it brings over prior methods, including newly identified spatial structures and key cell populations linked to disease states. Available as a Python package, MESA offers a versatile framework for quantitative decoding of tissue architectures in spatial omics across health and disease.

DOI: 10.1038/s41588-025-02119-z

Source: https://www.nature.com/articles/s41588-025-02119-z