Integrating cross-sample and cross-modal data for spatial transcriptomics and metabolomics with SpatialMETA

Ruonan Tian, Ziwei Xue, Yiru Chen, Yicheng Qi, Jian Zhang, Jie Yuan, Dengfeng Ruan, Junxin Lin, Jia Liu, Di Wang, Youqiong Ye and Wanlu Liu ()
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Ruonan Tian: Zhejiang University School of Medicine
Ziwei Xue: Zhejiang University School of Medicine
Yiru Chen: Zhejiang University—University of Edinburgh Institute, Zhejiang University School of Medicine
Yicheng Qi: Zhejiang University—University of Edinburgh Institute, Zhejiang University School of Medicine
Jian Zhang: Zhejiang University School of Medicine
Jie Yuan: Chinese Academy of Sciences
Dengfeng Ruan: Zhejiang University School of Medicine
Junxin Lin: Taizhou University
Jia Liu: Chinese Academy of Sciences
Di Wang: Zhejiang University School of Medicine
Youqiong Ye: Shanghai Jiao Tong University School of Medicine
Wanlu Liu: Zhejiang University School of Medicine

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

Abstract: Abstract Simultaneous profiling of spatial transcriptomics (ST) and spatial metabolomics (SM) on the same or adjacent tissue sections offers a revolutionary approach to decode tissue microenvironment and identify potential therapeutic targets for cancer immunotherapy. Unlike other spatial omics, cross-modal integration of ST and SM data is challenging due to differences in feature distributions of transcript counts and metabolite intensities, and inherent disparities in spatial morphology and resolution. Furthermore, cross-sample integration is essential for capturing spatial consensus and heterogeneous patterns but is often complicated by batch effects. Here, we introduce SpatialMETA, a conditional variational autoencoder (CVAE)-based framework for cross-modal and cross-sample integration of ST and SM data. SpatialMETA employs tailored decoders and loss functions to enhance modality fusion, batch effect correction and biological conservation, enabling interpretable integration of spatially correlated ST-SM patterns and downstream analysis. SpatialMETA identifies immune spatial clusters with distinct metabolic features in cancer, revealing insights that extend beyond the original study. Compared to existing tools, SpatialMETA demonstrates superior reconstruction capability and fused modality representation, accurately capturing ST and SM feature distributions. In summary, SpatialMETA offers a powerful platform for advancing spatial multi-omics research and refining the understanding of metabolic heterogeneity within the tissue microenvironment.

Date: 2025
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DOI: 10.1038/s41467-025-63915-z

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