近日，美国耶鲁大学Hongyu Zhao及其小组研制了来自不同来源的多个空间转录组学数据集的可解释、灵活和空间感知集成。2026年4月27日出版的《自然—遗传学》发表了这项成果。

研究团队提出了INSPIRE，这是一种深度学习方法，用于对多个ST数据集进行可解释的综合分析。INSPIRE采用基于图神经网络的对抗学习策略，实现空间知情和自适应的数据集成。通过结合非负矩阵分解，INSPIRE识别出可解释的空间因素和相关基因程序，这些因素表征了组织结构、细胞类型组织和生物过程。在广泛的应用中，INSPIRE在解析细粒度生物信号、整合跨技术互补优势、捕获条件特异性变异、揭示肿瘤微环境异质性、阐明发育动力学和促进三维组织重建方面表现出卓越的性能。INSPIRE还可以扩展到非常大的数据集，如Xenium-profiled人乳腺癌和Stereo-seq小鼠器官发生数据集的应用所证明的那样。

研究人员表示，空间转录组学（ST）的最新进展产生了越来越多的异质性数据集，为研究组织组织和功能提供了前所未有的机会。然而，有效地解释和整合来自不同环境和条件的数据仍然是一个重大挑战。

附：英文原文

Title: Interpretable, flexible and spatially aware integration of multiple spatial transcriptomics datasets from diverse sources

Author: Zhao, Jia, Zhang, Xiangyu, Wang, Gefei, Lin, Yingxin, Liu, Tianyu, Chang, Rui B., Zhao, Hongyu

Issue&Volume: 2026-04-27

Abstract: Recent advances in spatial transcriptomics (ST) have generated an expanding collection of heterogeneous datasets, offering unprecedented opportunities to investigate tissue organizations and functions. However, effective interpretation and integration of data originating from diverse sources and conditions remain a major challenge. We present INSPIRE, a deep-learning method for interpretable, integrative analysis of multiple ST datasets. INSPIRE adopts an adversarial learning strategy with graph neural networks to achieve spatially informed and adaptive data integration. By incorporating non-negative matrix factorization, INSPIRE identifies interpretable spatial factors and associated gene programs that characterize tissue architecture, cell-type organization and biological processes. Across a broad range of applications, INSPIRE demonstrates superior performance in resolving fine-grained biological signals, integrating complementary strengths across technologies, capturing condition-specific variation, uncovering tumor microenvironment heterogeneity, elucidating developmental dynamics and facilitating three-dimensional tissue reconstruction. INSPIRE also scales to extremely large datasets, as demonstrated by applications to Xenium-profiled human breast cancer and Stereo-seq mouse organogenesis datasets.

DOI: 10.1038/s41588-026-02579-x

Source: https://www.nature.com/articles/s41588-026-02579-x