Journal Article10.1038/s41467-025-63915-z
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,Wanlu Liu,Ruonan Tian,Ziwei Xue,Yiru Chen,Yicheng Qi,Jian Zhang,Jie Yuan,Dengfeng Ruan,Junxin Lin,Jia Liu,Di Wang,Youqiong Ye,Wanlu Liu +23 more
TL;DR: Researchers introduce SpatialMETA, a CVAE-based framework for integrating spatial transcriptomics and metabolomics data, enabling interpretable analysis of tissue microenvironment heterogeneity and identifying immune spatial clusters with distinct metabolic features in cancer.
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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. Simultaneous profiling of spatial transcriptomics (ST) and metabolomics (SM) offers a novel way to decode tissue microenvironment heterogeneity. Here, the authors present SpatialMETA, a conditional variational autoencoder-based framework designed for the integration of ST and SM data.
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