Deciphering spatial domains from spatial multi-omics with SpatialGlue
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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.
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SpatialGLUE applied a GNN to integrate spatial multi-omics data, capturing more anatomical details including undiscovered cell types, and provided more accurate information on spatial domains.
“Additionally, SpatialGLUE applied a GNN to integrate spatial multi-omics data [130], which captured more anatomical details including undiscovered cell types, and provided more accurate information on spatial domains.”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [130]
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SMART: spatial multi-omic aggregation using graph neural networks and metric learning
Du, Z., Chen, Q., Huang, W., Chen, J., & Zheng, X. (2026) · Nature Communications · doi:10.1038/s41467-026-70821-5 · Cited by 7 · see citing works
“In the UMAP (Uniform Mani-fold Approximation and Projection) visualization, SMART, PRESENT, MISO and SpatialGlue exhibit well-de fi ned cluster boundaries corre-spondingtothe ground truth labels, demonstrating their effectiveness in identifying distinct tissue regions (Fig.”
InfluentialSelf-citation3d-OT: a deep geometry-aware framework for heterogeneous slices alignment of spatial multi-omics
Dai, B., Yi, L., Wang, P., Li, H., Hu, P., Song, Y., Xing, J., Feng, Z., Yuan, Z., & Zuo, Y. (2026) · Nature Methods · doi:10.1038/s41592-026-03034-9 · Cited by 10 · see citing works
“The predicted distribution of Mbp and Sox2 was consistent with the original analysis, whereas Nexn (CP), Cldn11 (ccg), Fezf2 (ctx L6a) , Bcl11b (CP) and Tle4 (CP) were discovered to be potential markers by other domain segmentation methods 20,21 .”
Chen, Y., Yang, M., & Hua, Q. (2025) · Pharmacological Research · doi:10.1016/j.phrs.2025.107734 · Cited by 8 · see citing works
Modal-NexT: Towards unified heterogeneous cellular data integration
Tang, Z., Chen, G., Chen, S., He, H., Huang, J., Dong, T., Zhou, J., Zhao, L., You, L., & Chen, C. Y. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103479 · Cited by 5 · see citing works
Multi-omics to study chronic respiratory diseases and viral infections
Idrees, S., Chen, H., Sadaf, T., Rehman, S. F., Johansen, M., Paudel, K., Liu, G., Wang, Y., Luecken, M., Hortle, E., et al. (2026) · European Respiratory Review · doi:10.1183/16000617.0286-2024 · Cited by 8
“Recent methods, such as BayesTME [88] and SpatialGlue [183] perform deconvolution without reference to scRNA-seq data.”
Mapping biology in space: from spatial transcriptomics platforms to analytical tools and databases
Guo, Z., Wu, R., Li, W., Yang, K., Ying, X., Alinejad-Rokny, H., & Ye, Y. (2026) · 中国科学通报：英文版 · doi:10.1016/j.scib.2026.01.034 · Cited by 9 · see citing works
Li, Y., Cai, G., Chen, F.-Q., Wen, K.-P., & Ou-Yang, L. (2025) · Communications Biology · doi:10.1038/s42003-025-08372-6 · Cited by 8 · see citing works
“GroundtruthannotationsfromSpatialGlue 27 wereemployedtoassessspatial domain identi fi cation accuracy and compare SMODEL against seven representative multi-omics methods (Supplementary Note 1).”
SpaFusion: A multi-level fusion model for clustering spatial multi-omics data
Chen, F., Cai, G., Li, Y., & Ou-Yang, L. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103372 · Cited by 8 · see citing works
Maity, A., Maidantchik, V. D., Weidenfeld, K., Larisch, S., Barkan, D., & Haick, H. (2025) · Advanced Materials · doi:10.1002/adma.202413017 · Cited by 10 · see citing works
Chen, R., Yao, Y., Qian, J., Peng, X. X., Shao, X., & Fan, X. (2025) · iMeta · doi:10.1002/imt2.70084 · Cited by 9 · see citing works
“For example, SpatialGlue [58] combines spatial multi ‐ omics data to improve spatial domain identification, suggesting that incorporating complementary modalities could enhance clustering accuracy and biological interpretability.”
Joshi, R., Tang, S. Y., Das, U. S., Boehmler, D. J., Mrčela, A., Lordan, R., Petersson, E. J., Weljie, A. M., & FitzGerald, G. A. (2025) · Analytica Chimica Acta · doi:10.1016/j.aca.2025.344723 · Cited by 7 · see citing works
“These methods leverage a wide range of machine learning techniques, including optimal transport [151] and graph neural networks [152].”
Spatial profiling of the metabolism-immune axis in ovarian cancer
Wang, Z.-B., Long, M., Yu, P., Wang, Y., Yang, Z., & Huang, M.-S. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1672020 · Cited by 5 · see citing works
“Such hierarchical attention overcomes fixed-weight fusion limitations, enabling biologically adaptive integration (Long et al., 2024).”
Multimodal spatial omics: From data acquisition to computational integration
Işık, E. B., Usta, Y. H., Riazi, M., Liu, H., Roach, W., Zhou, H., Nicolaou, A., Rattray, M., & Georgaka, S. (2026) · Patterns · doi:10.1016/j.patter.2026.101592 · Cited by 5 · see citing works
“For spatial domain identification tasks involving the integration of multiple spatial modalities (e.g., ST with spatial proteomics or epigenomics), graph-based methods such as SpatialGlue and MISO are recommended, as they can naturally encode spatial neighborhood structure and can handle heterogeneous feature types.”
Spatially resolving cancer: from cell states to therapy
Pei, G., Liu, Y., & Wang, L. (2025) · Trends in cancer · doi:10.1016/j.trecan.2025.09.002 · Cited by 7 · see citing works
SpaOmicsVAE: A deep learning framework for integrative analysis of spatial multi-omics data
Zhang, Z., Wang, M., Zhang, X., Dai, R., Wang, Z., Lei, L., Li, Z., Han, K., Wang, Z., Shi, C., et al. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109032 · Cited by 6 · see citing works
Emerging AI Approaches for Cancer Spatial Omics
Noorbakhsh, J., pour, A. F., & Chuang, J. (2025) · arXiv.org · doi:10.48550/arxiv.2506.23857 · Cited by 6
“For example, SpatialGlue [116] integrates spatial transcriptomics, proteomics and epigenomics data for domain identification.”
Chen, Y., Zhen, C., Mo, Y., Liu, J., & Zhang, L. (2025) · Advanced Science · doi:10.1002/advs.202413124 · Cited by 4 · see citing works
Decoding the blueprints of embryo development with single-cell and spatial omics
Liu, C. X., Li, X., Hu, Q., Jia, Z., Ye, Q., Wang, X., Zhao, K., Liu, L., & Wang, M. (2025) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2025.01.002 · Cited by 6 · see citing works
Ngassam, B. T., Niu, H., Pang, S., Shydlouskaya, V., & Andrews, T. S. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1715821 · Cited by 4 · see citing works
Hao, N.-H., Yang, X., Yan, B.-S., Li, D., Huang, J.-Z., Wu, X.-T., Ruiz, E. S., de Luzuriaga, A. R. D., Zhao, C., & Wan, G.-H. (2026) · arXiv.org · doi:10.48550/arxiv.2601.07826 · Cited by 5 · see citing works
“Subsequently, more sophisticated architectures were adopted to handle complex integrations, including variational autoencoders (VAE) (spaVAE [31]), attention mechanisms (SpatialGlue [32]), contrastive discriminators (COSMOS [33]), and conditional diffusion models (CAN-DIES [34]).”
Kgabeng, T., Wang, L., Ngwangwa, H., & Pandelani, T. (2025) · Bioengineering · doi:10.3390/bioengineering12101037 · Cited by 5 · see citing works
soFusion: facilitating tissue structure identification via spatial multi-omics data fusion
Yu, N., Li, W., Sun, X., Hu, J., Zou, Q., Liu, Z., Zhang, D.-L., Zhang, W., & Gao, R. (2025) · Briefings Bioinform. · doi:10.1093/bib/bbaf513 · Cited by 5 · see citing works
“GCN can capturecellular expression patterns and the characteristics of neighboring microenvironments [23].”
InfluentialSelf-citationSTINR: Deciphering Spatial Transcriptomics via Implicit Neural Representation
Luo, Y.-S., Zhao, X.-L., Ye, K., & Meng, D.-Y. (2025) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52734.2025.02415 · Cited by 5
“A recent SOTA multi-omics method SpatialGlue [12] is included as baseline.”
Ghobashi, A. H., & Ma, Q. (2025) · Quantitative Biology · doi:10.1002/qub2.70004 · Cited by 6 · see citing works
“For instance, AI-based models enable robust multimodal integration, seamlessly combining data from scRNA-seq, scATAC-seq, and spatial transcriptomics to uncover intricate relationships between gene expression, chromatin states, and spatial localization [45, 46].”
Wang, B., Liu, W., Luo, J., Chen, X., & Kwoh, C. K. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822097 · Cited by 4 · see citing works
“We compared three representative methods: non-spatial multi-omics integration methods totalVI [19] and scMM [17], and spatial multi-omics integration method SpatialGlue [3].”
InfluentialSTMGraph: spatial-context-aware of transcriptomes via a dual-remasked dynamic graph attention model
Lin, L., Wang, H., Chen, Y., Wang, Y., Xu, Y., Chen, Z., Yang, Y., Liu, K., & Ma, X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae685 · Cited by 5 · see citing works
“Besides, STMGraph also achieved the best clustering results when benchmarking of simulated data [49] (Supplementary Materials and Fig.”
Fathollahian, H., Zhao, S., Nipu, N., Salahshour, M., Duan, L., Tang, W., Boorboor, S., & Marai, G. E. (2026) · Computers & Graphics · doi:10.1016/j.cag.2026.104728 · Cited by 1 · see citing works
“GAT-based models have been widely applied in spatial omics to identify spatial domains and cell niches, including STGATE, SpaGCN, and SPACE-GM [13,41–44].”
Interpretable spatial multi-omics data integration and dimensionality reduction with SpaMV
Liu, Y., Ma, K., Xu, H., Xu, K., Hu, Y., Lin, Z., Lin, J.-L., Han, B., Li, S.-C., Lin, Z.-X., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-74718-1 · Cited by 4 · see citing works
“Transcriptome private topics mainly included the hindbrain (transcriptome private topics 1, 2, and 3), thalamus (transcriptome private topic 4), ventral part of medulla oblongata (transcriptome private topics 7 and 8), and the trigeminal ganglion (transcriptome private topic 10).”
Self-citationLi, M., Sun, P., Luo, Y., Zhou, G., Yang, X., Meng, D., & Ye, K. (2026) · Nature Communications · doi:10.1038/s41467-026-71882-2 · Cited by 2 · see citing works
Yiu, S. P. T., Chang, Y., Yeo, Y. Y., Qiu, H., Wu, W., Michel, H. A., Jin, X., Huang, R., Kure, S., Parmelee, L. K., et al. (2026) · Cancer Discovery · doi:10.1158/2159-8290.cd-25-0775 · Cited by 17 · see citing works
“The same-slide design is critical for SGCC, as it avoids the need for cross-section cell or spot matching required by sequential slide integration meth-ods such as SpatialGlue (73), MaxFuse (74), and MARIO (75).”
Interpretable data integration for single-cell and spatial multi-omics
Yang, C., He, Z., Nie, Q. H., & Zhang, L. (2026) · Cell Systems · doi:10.1016/j.cels.2025.101479 · Cited by 4 · see citing works
Cai, G., Chen, F., Wen, K., Li, Y., & Ou-Yang, L. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103428 · Cited by 6 · see citing works
Computational methods for spatial multi-omics integration
Geng, A., Cui, C., Luo, Z., Xu, J., Meng, Y., Cui, F., Wei, L., Zou, Q., & Zhang, Z. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.108807 · Cited by 6 · see citing works
Ma, Y., Wang, Y., & Ma, X. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag052 · Cited by 4 · see citing works
“The GNN encoder is then employed to extract graph-specific cell embeddings, and an attention mechanism is adopted to fuse these embedding of cells from various networks [28, 29], where structural constraint is imposed to encourage spatially adjacent cells to share similar embedding.”
Zhang, L., Zhu, Y., & Zhang, S. (2025) · Communications Biology · doi:10.1038/s42003-025-09340-w · Cited by 4 · see citing works
“The clustering results reveal that SEPAR and spaMultiVAE both capture contiguous spatial domains,whereasSpatialGlue,MISO,andSeuratproducemorefragmented patternswithreducedspatialcontinuity.”
InfluentialSuperGLUE facilitates an explainable training framework for multi-modal data analysis
Liu, T., Zhao, J., & Zhao, H. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101167 · Cited by 2 · see citing works
“18,19 Most MDI approaches choose to project multi-modal data into a joint space, including TotalVI (RNA+Protein), 20 MultiVI (RNA +Protein+ATAC), 21 MultiMAP (arbitrary multi-modal data), 22 GLUE (arbitrary multi-modal data without spatial information), 18 UnitedNet 19 (arbitrary multi-modal data), and SpatialGLUE 23…”
Zhou, Q., Li, C., Chen, C., Li, M., Liu, J., Sun, W., Zhang, Z., Gu, S., Cai, Y., Huang, Y., et al. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf365 · Cited by 3 · see citing works
“SpatialGlue and Spatial-MGCN use GCNs with attention mechanism to decipher spatial domain by learning the relationship of gene expression and spatial information [8, 9].”
Fu, Y., Nan, M., Ren, Q., Chen, X., & Gao, J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf172 · Cited by 4 · see citing works
“SpatialGlue improved spatial domain identification by integrating multiple spatial histology data from the same tissue and slices (Long et al. 2024).”
Chang, W., Zhang, W., Li, K., & Huang, P. (2026) · iScience · doi:10.1016/j.isci.2026.116349 · Cited by 4 · see citing works
“113 Furthermore, to address biases in cell-type assignment, integration with single-cell reference data via tools such as SPOTlight and Cell2location (discussed in section computational integration and investigation into functional analysis) remains essential for refining spatial mapping accuracy.”
Zhao, J., Zhang, X., Wang, G., Lin, Y., Liu, T., Chang, R. B., & Zhao, H. (2026) · Nature Genetics · doi:10.1038/s41588-026-02579-x · Cited by 6 · see citing works
Kamaraj, U. S., Chen, Y., Lei, J., Gautam, P., Horcharoensuk, P., Clemente, C. K. M., Weinacht, K. G., Gascoigne, N. R. J., Chen, J., Chen, C. K., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68596-w · Cited by 3 · see citing works
“1.5 59 .”
InfluentialSelf-citationSpatial multi-omics integration by cross-modal graph contrastive learning
Gui, Y., Xu, Y., & Li, C. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag192 · Cited by 2 · see citing works
“However, such approaches neglect spatial information,resultingininconsistentdomainidentificationcompared with spatially informed multi-omics frameworks, such as SpatialGlue [2], COSMOS [3], and CANDIES [4].”
GATCL: graph attention network meets contrastive learning for spatial domain identification
Mu, J., Yao, Y., Chen, Q., Sun, J., & Zhao, T. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag043 · Cited by 3 · see citing works
“Recently, SpatialGlue [32] proposes a graph neural network architecture equipped with a dual-attention mechanism to synergistically integrate spatial multi-omics data.”
InfluentialAgrawal, A., & Thomann, S. (2026) · American Journal Of Pathology · doi:10.1016/j.ajpath.2026.01.007 · Cited by 4 · see citing works
Yuan, L., Meng, B., Wang, Q., Hu, C., Wang, C., & Huang, D.-S. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013667 · Cited by 5 · see citing works
Multi-omics integration and batch correction using a modality-agnostic deep learning frameworkPreprint
Larizgoitia, J. I. A., Partel, G., Venturelli, L., Zhang, W., Spotbeen, X., Vanuytven, S., Kint, S., Vandereyken, K., Wouters, D., Ismail, A., et al. (2025) · bioRxiv · doi:10.1101/2025.10.21.683449 · Cited by 3 · see citing works
“We also included PCA as a baseline, given its widespread use in multi-omics pipelines and as a preprocessing step for models like SpatialGlue 33 .”
InfluentialInterpretable spatial multi-omics data integration and dimension reduction with SpaMVPreprint
Liu, Y., Ma, K., Xu, H., Xu, K., Hu, Y., Lin, Z., Lin, J.-L., Han, B., Li, S.-C., Lin, Z.-X., et al. (2025) · bioRxiv · doi:10.1101/2025.08.02.668264 · Cited by 3 · see citing works
“SpatialGlue [5] employs graph convolutional networks (GCNs) to extract modality-specific representations, which are integrated via an attention mechanism to form a unified latent space for unsupervised clustering.”
Self-citationNext-generation kidney tissue analysis — spatial omics and digital pathology
Yoshikawa, T., Bülow, R. D., Boor, P., & Yanagita, M. (2026) · Nature Reviews Nephrology · doi:10.1038/s41581-026-01102-z · Cited by 3 · see citing works
“However, potential applications include investigating local molecular patterns in diseases such as focal segmental glomerulosclerosis as well as solving complex tasks such as patient classification, biomarker identification 174 , or spatial domain identification across multi-omics data 175,176 .”
Shi, Y., Xu, W., & Hu, P. (2026) · Genetic Epidemiology · doi:10.1002/gepi.70044 · Cited by 2 · see citing works
“A multi‐omic dependency score predictor may also correct for these differences, which will require a multi‐omic DA approach to accommodate the unique data characteristics of each molecular data type (Long et al. 2024).”
- Authors
- 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.
- Affiliations
- Agency for Science, Technology and Research (SG)
- BGI Group (China) (CN)
- Yale University (US)
- Shanghai Jiao Tong University (CN)
- National University of Singapore (SG)
- Northwestern University (US)
- Corresponding author
- Chen, Ao
- Chen, Jinmiao
- ORCID
- Venue
- Nature Methods
- Published
- 2024-06-21
- Volume
- 21
- Issue
- 9
- Pages
- 1658-1667
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC11399094
- OpenAlex
- W4399892951
- Cited by
- 223
- References
- 28
- Citations by year
- 2026: 131 · 2025: 78 · 2024: 13 · 2023: 1
- FWCI
- 30.22
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Methods (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Research Foundation (NRF-CRP26-2021-0001)
- National Research Foundation Singapore (NRF-CRP26-2021-0001, MOH-OFLCG18May-0003)
- Medical Research Council
- National Medical Research Council (MOH-OFLCG18May-0003, NMRC /OFLCG/003/2018)
- Biomedical Research Council
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Keywords
- spatial multi-omics, spatial domain identification, graph neural networks, dual attention mechanism, transcriptome-proteome integration, spatial data integration
- MeSH terms
- Epigenome, Multiomics, Animals, Brain, Humans, Spleen, Neural Networks, Computer, Computational Biology, Proteome, Genomics, Proteomics, Mice, Transcriptome
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