Yang Penghui, Jin Kaiyu, Yao Yue, Jin Lijun, Shao Xin, Li Chengyu, Lu Xiaoyan, Fan Xiaohui
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
Nat Commun. 2025 Feb 1;16(1):1265. doi: 10.1038/s41467-025-56523-4.
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.
空间和单细胞组学测序中的技术限制给在空间尺度上捕获和描述多模态信息带来了挑战。为了解决这一问题，我们开发了SIMO，这是一种通过概率比对进行多组学数据集空间整合的计算方法。与以前的工具不同，SIMO不仅将空间转录组学与单细胞RNA测序整合在一起，而且还扩展到了其他领域，能够跨多种单细胞模态进行整合，如染色质可及性和DNA甲基化，而这些在以前尚未在空间上进行共分析。我们在模拟数据集上对SIMO进行了基准测试，证明了其高准确性和稳健性。在生物数据集上的进一步应用揭示了SIMO在多个组学层面检测细胞拓扑模式及其调控模式的能力。通过对真实世界数据的全面分析，SIMO揭示了多模态空间异质性，为生物分子的空间组织和调控提供了更深入的见解。这些发现使SIMO成为揭示以前无法获得的多模态见解、推动空间生物学发展的强大工具。
