Deciphering spatial domains from spatial multi-omics with SpatialGlue

Yahui Long; Kok Siong Ang; Raman Sethi; Sha Liao; Yang Heng; Lynn van Olst; Shuchen Ye; Chengwei Zhong; Hang Xu; Di Zhang; Immanuel Kwok; Nazihah Husna; Min Jian; Lai Guan Ng; Ao Chen; Nicholas R.J. Gascoigne; David Gate; Rong Fan; Xun Xu; Jinmiao Chen

doi:10.1038/s41592-024-02316-4

Deciphering spatial domains from spatial multi-omics with SpatialGlue

Yahui Long, Kok Siong Ang, Raman Sethi, Sha Liao, Yang Heng, Lynn van Olst, Shuchen Ye, Chengwei Zhong, Hang Xu, Di Zhang, Immanuel Kwok, Nazihah Husna, Min Jian, Lai Guan Ng, Ao Chen, Nicholas R.J. Gascoigne, David Gate, Rong Fan, Xun Xu, Jinmiao Chen^*

^*Corresponding author for this work

Neurology

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

Advances in spatial omics technologies now allow multiple types of data to be acquired from the same tissue slice. To realize the full potential of such data, we need spatially informed methods for data integration. Here, we introduce SpatialGlue, a graph neural network model with a dual-attention mechanism that deciphers spatial domains by intra-omics integration of spatial location and omics measurement followed by cross-omics integration. We demonstrated SpatialGlue on data acquired from different tissue types using different technologies, including spatial epigenome–transcriptome and transcriptome–proteome modalities. Compared to other methods, SpatialGlue captured more anatomical details and more accurately resolved spatial domains such as the cortex layers of the brain. Our method also identified cell types like spleen macrophage subsets located at three different zones that were not available in the original data annotations. SpatialGlue scales well with data size and can be used to integrate three modalities. Our spatial multi-omics analysis tool combines the information from complementary omics modalities to obtain a holistic view of cellular and tissue properties.

Original language	English (US)
Pages (from-to)	1658-1667
Number of pages	10
Journal	Nature Methods
Volume	21
Issue number	9
DOIs	https://doi.org/10.1038/s41592-024-02316-4
State	Published - Sep 2024

Funding

We thank Y. Tan for assistance in interpreting mouse thymus data, M. Wu for comments on the model and T. Watson for assistance in submitting in-house data to the Gene Expression Omnibus (GEO) database. The research was supported by: A*STAR under its BMRC Central Research Fund (CRF, UIBR) Award; AI, Analytics and Informatics (AI3) Horizontal Technology Programme Office (HTPO) seed grant (Spatial transcriptomics ST in conjunction with graph neural networks for cell\u2013cell interaction C211118015) from A*STAR, Singapore; Open Fund Individual Research Grant (mapping hematopoietic lineages of healthy patients and high-risk patients with acute myeloid leukemia with FLT3-ITD mutations using single-cell omics no. OFIRG18nov-0103) from the Ministry of Health, Singapore; National Research Foundation (NRF), award no. NRF-CRP26-2021-0001; the National Research Foundation, Singapore, and Singapore Ministry of Health\u2019s National Medical Research Council under its Open Fund-Large Collaborative Grant (\u2018OF-LCG\u2019) (MOH-OFLCG18May-0003); and Singapore National Medical Research Council (NMRC/OFLCG/003/2018). L.G.N. was supported by the National Natural Science Foundation of China (grant 32270956) and Shanghai Science and Technology Commission (grant 20JC1410100).

ASJC Scopus subject areas

Biotechnology
Biochemistry
Molecular Biology
Cell Biology

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Long, Y., Ang, K. S., Sethi, R., Liao, S., Heng, Y., van Olst, L., Ye, S., Zhong, C., Xu, H., Zhang, D., Kwok, I., Husna, N., Jian, M., Ng, L. G., Chen, A., Gascoigne, N. R. J., Gate, D., Fan, R., Xu, X., & Chen, J. (2024). Deciphering spatial domains from spatial multi-omics with SpatialGlue. Nature Methods, 21(9), 1658-1667. https://doi.org/10.1038/s41592-024-02316-4

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Deciphering spatial domains from spatial multi-omics with SpatialGlue

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