Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics

Shuangsang Fang, Mengyang Xu, Lei Cao, Xiaobin Liu, Marija Bezulj, Liwei Tan, Zhiyuan Yuan, Yao Li, Tianyi Xia, Longyu Guo, Vladimir Kovacevic, Junhou Hui, Lidong Guo, Chao Liu, Mengnan Cheng, Li’ang Lin, Zhenbin Wen, Bojana Josic, Nikola Milicevic, Ping Qiu, Qin Lu, Yumei Li, Leying Wang, Luni Hu, Chao Zhang, Qiang Kang, Fengzhen Chen, Ziqing Deng, Junhua Li, Mei Li, Shengkang Li, Yi Zhao (), Guangyi Fan (), Yong Zhang (), Ao Chen (), Yuxiang Li () and Xun Xu ()
Additional contact information
Shuangsang Fang: BGI Research
Mengyang Xu: BGI Research
Lei Cao: BGI Research
Xiaobin Liu: BGI Research
Marija Bezulj: BGI Research
Liwei Tan: BGI Research
Zhiyuan Yuan: Fudan University
Yao Li: BGI Research
Tianyi Xia: BGI Research
Longyu Guo: BGI Research
Vladimir Kovacevic: BGI Research
Junhou Hui: BGI Research
Lidong Guo: BGI Research
Chao Liu: BGI Research
Mengnan Cheng: BGI Research
Li’ang Lin: BGI Research
Zhenbin Wen: BGI Research
Bojana Josic: BGI Research
Nikola Milicevic: BGI Research
Ping Qiu: BGI Research
Qin Lu: BGI Research
Yumei Li: BGI Research
Leying Wang: BGI Research
Luni Hu: BGI Research
Chao Zhang: BGI Research
Qiang Kang: BGI Research
Fengzhen Chen: BGI Research
Ziqing Deng: BGI Research
Junhua Li: BGI Research
Mei Li: BGI Research
Shengkang Li: BGI Research
Yi Zhao: Chinese Academy of Sciences
Guangyi Fan: BGI Research
Yong Zhang: BGI Research
Ao Chen: BGI Research
Yuxiang Li: BGI Research
Xun Xu: BGI Research

Nature Communications, 2025, vol. 16, issue 1, 1-19

Abstract: Abstract Understanding complex biological systems requires tracing cellular dynamic changes across conditions, time, and space. However, integrating multi-sample data in a unified way to explore cellular heterogeneity remains challenging. Here, we present Stereopy, a flexible framework for modeling and dissecting comparative and spatiotemporal patterns in multi-sample spatial transcriptomics with interactive data visualization. To optimize this framework, we devise a universal container, a scope controller, and an integrative transformer tailored for multi-sample multimodal data storage, management, and processing. Stereopy showcases three representative applications: investigating specific cell communities and genes responsible for pathological changes, detecting spatiotemporal gene patterns by considering spatial and temporal features, and inferring three-dimensional niche-based cell-gene interaction network that bridges intercellular communications and intracellular regulations. Stereopy serves as both a comprehensive bioinformatics toolbox and an extensible framework that empowers researchers with enhanced data interpretation abilities and new perspectives for mining multi-sample spatial transcriptomics data.

Date: 2025
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DOI: 10.1038/s41467-025-58079-9

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