Published March 7, 2025 | Version v3
Dataset Open

SpatialMETA: A Novel Framework for Integrating Spatial Transcriptomics and Metabolomics Data

Authors/Creators

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Description

Multimodal analysis of spatial transcriptomics (ST) and spatial metabolomics (SM) has rapidly advanced for characterizing tissue microenvironments. However, integrating ST and SM data remains challenging due to differing morphologies, resolutions, and batch effects. We developed SpatialMETA (Spatial Metabolomics and Transcriptomics Analysis), a novel method for integrating spatial multi-omics data, which aligns ST and SM to a unified resolution, enables both cross-modal and cross-sample integration to identify ST-SM associated spatial patterns, and provides extensive visualization and analysis capabilities. The datasets for SpatialMETA  is avaiable. 

Files

mouse3_brain_msi.csv

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Additional details

Dates

Submitted
2024-06-25

Software

Repository URL
https://github.com/WanluLiuLab/SpatialMETA

References

  • Sun, C. et al. Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer. Nat. Commun. 14, 2692 (2023).
  • Hu, J. et al. Multi-omic profiling of clear cell renal cell carcinoma identifies metabolic reprogramming associated with disease progression. Nat. Genet. 56, 442–457 (2024).
  • Ravi, V. M. et al. Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma. Cancer Cell 40, 639-655.e13 (2022).
  • Zheng, P. et al. Integrated spatial transcriptome and metabolism study reveals metabolic heterogeneity in human injured brain. Cell Rep. Med. 4, 101057 (2023).
  • Vicari, M. et al. Spatial multimodal analysis of transcriptomes and metabolomes in tissues. Nat. Biotechnol. (2023) doi:10.1038/s41587-023-01937-y.