Spatial integration of multi-omics single-cell data with SIMO

Penghui Yang, Kaiyu Jin, Yue Yao, Lijun Jin, Xin Shao, Chengyu Li, Xiaoyan Lu () and Xiaohui Fan ()
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Penghui Yang: Zhejiang University
Kaiyu Jin: Zhejiang University
Yue Yao: Zhejiang University
Lijun Jin: Zhejiang University
Xin Shao: Zhejiang University
Chengyu Li: Zhejiang University
Xiaoyan Lu: Zhejiang University
Xiaohui Fan: Zhejiang University

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Technical limitations in spatial and single-cell omics sequencing pose challenges for capturing and describing multimodal information at the spatial scale. To address this, we develop SIMO, a computational method designed for the Spatial Integration of Multi-Omics datasets through probabilistic alignment. Unlike previous tools, SIMO not only integrates spatial transcriptomics with single-cell RNA-seq but expands beyond, enabling integration across multiple single-cell modalities, such as chromatin accessibility and DNA methylation, which have not been co-profiled spatially before. We benchmark SIMO on simulated datasets, demonstrating its high accuracy and robustness. Further application on biological datasets reveals SIMO’s ability to detect topological patterns of cells and their regulatory modes across multiple omics layers. Through comprehensive analysis of real-world data, SIMO uncovers multimodal spatial heterogeneity, offering deeper insights into the spatial organization and regulation of biological molecules. These findings position SIMO as a powerful tool for advancing spatial biology by revealing previously inaccessible multimodal insights.

Date: 2025
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DOI: 10.1038/s41467-025-56523-4

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