Integrative Biomedical Research
Integrative Biomedical Research (Journal of Angiotherapy) | Online ISSN 3068-6326
463
Citations
1.8m
Views
746
Articles
Volume 10 Number 1 2026
REVIEWS (Open Access)
Mapping the Invisible Architecture of Disease: Spatial Multi-Omics Technologies, Computational Integration, and the Path Toward Clinical Translation
Mohammad Asaduzzaman 1* Lamiah Hossain 2
Integrative Biomedical Research 10 (1) 1-8 https://doi.org/10.25163/biomedical.10110883
Submitted: 11 December 2025 Revised: 28 January 2026 Accepted: 08 February 2026 Published: 10 February 2026
Abstract
Tissue is not a bag of cells. It is a coordinated, three-dimensional community whose spatial organization shapes development, homeostasis, and disease, yet this organization is destroyed the moment tissue is dissociated for conventional sequencing. Spatial multi-omics has emerged to close that gap, but the field remains fragmented across technologies, computational strategies, and clinical settings that rarely speak to one another. We conducted a structured narrative synthesis of the spatial multi-omics literature (2016-2026), organized around a five-branch conceptual framework spanning molecular hierarchy, next-generation sequencing- and imaging-based platforms, computational fusion architectures, clinical translation, and deployment barriers. Sources were drawn from peer-reviewed journals indexed in PubMed, Scopus, and Web of Science, supplemented by platform-defining primary reports. Ninety-plus sources converged on several consistent patterns. NGS-based platforms (Visium, Visium HD, Stereo-seq, DBiT-seq) trade spatial precision for unbiased transcriptomic breadth, whereas imaging-based platforms (MERFISH, seqFISH+, Xenium, CosMx) achieve subcellular fidelity at the cost of panel size. Intermediate-fusion architectures, particularly graph-neural-network models such as SpatialGlue, SWITCH, and MultiGATE, now outperform early- and late-fusion approaches for preserving native spatial topology. Clinically, spatially resolved immune-proximity and stromal-exclusion scores outperform bulk immunohistochemistry in predicting immunotherapy response, and multi-omic mapping has nominated actionable targets in oncology, cardiology, and nephrology. Persistent barriers include FFPE incompatibility, terabyte-scale computational burden, algorithmic opacity, and a severe European-ancestry bias that erodes polygenic score accuracy by up to 78% in African-ancestry cohorts. Spatial multi-omics is approaching, but has not yet reached, routine clinical deployment; standardized pre-analytical protocols, interpretable AI, and globally representative biobanking are the rate-limiting steps for translation.
Keywords: spatial multi-omics, spatial transcriptomics, tissue microenvironment, data integration, graph neural networks, precision medicine, health equity
References
Abedini, A., Levinsohn, J., Klötzer, K. A., Dumoulin, B., Ma, Z., Frederick, J., Dutta, S., Zhang, S., Wu, W., Wang, S., Han, S. Y., Lu, S., Balzer, M. S., Frankenstein, Z., Ledru, N., Muto, Y., Chen, D., Kaufman, B. A., Della Chiesa, I., ... Susztak, K. (2024). Single-cell multi-omic and spatial profiling of human kidneys implicates the fibrotic microenvironment in kidney disease progression. Nature Genetics, 56(8), 1712–1724.
Alemu, R., Sharew, N. T., Arsano, Y. Y., Ahmed, M., Tekola-Ayele, F., Mersha, T. B., & Amare, T. T. (2025). Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues. Human Genomics, 19, 8. https://doi.org/10.1186/s40246-025-00718-9
Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O. (2020). MOFA+: A statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biology, 21, 111. https://doi.org/10.1186/s13059-020-02015-1
Arora, N. (2025). Spatial multi-omics and artificial intelligence in precision oncology: Decoding the tumor ecosystem. International Journal of Emerging Research in Applied Medical Sciences, 1(5), 93–100.
Bai, Z., Zhang, D., Gao, Y., Tao, B., Zhang, D., Bao, S., Enninful, A., Wang, Y., Li, H., Su, G., & Fan, R. (2024). Spatially exploring RNA biology in archival formalin-fixed paraffin-embedded tissues. Cell, 187(23), 6760–6779. https://doi.org/10.1016/j.cell.2024.10.026
BANKSY. (2025). BANKSY: A spatial-aware clustering algorithm for cell-type and tissue-domain identification [Software documentation and methods reference].
Breeze, C. E., Buniello, A., Harris, G., & Dunham, I. (2022). Systematic underrepresentation of non-European genetic ancestries in epigenomic consortia. Trends in Genetics, 38(10), 1001–1008.
Cen, X., Li, Y., & Qu, F. (2026). Single-cell and spatial omics methods, algorithms, and applications in human biology and disease. MedComm, 2026.
Chen, A., Liao, S., Cheng, M., Ma, K., Wu, L., Lai, Y., Qiu, X., Yang, J., Xu, J., Hao, S., Wang, X., Lu, H., Chen, X., Liu, X., Huang, X., Li, Z., Hong, Y., Jiang, Y., Peng, J., ... Xu, X. (2022). Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell, 185(10), 1777–1792.
Chen, C., Wang, J., Pan, D., Wang, X., Xu, Y., Yan, J., Wang, L., Yang, X., Yang, M., & Liu, G. (2023). Applications of multi-omics analysis in human diseases. MedComm, 4(4), e315. https://doi.org/10.1002/mco2.315
Chen, W.-T., Lu, A., Craessaerts, K., Pavie, B., Sala Frigerio, C., & De Strooper, B. (2020). Spatial transcriptomics and in situ sequencing to study Alzheimer's disease. Cell, 182(4), 976–991.
Cheng, X., Peng, T., Chu, T., Yang, Y., Liu, J., Gao, Q., & Wei, J. (2025). Application of single-cell and spatial omics in deciphering cellular hallmarks of cancer drug response and resistance. Journal of Hematology & Oncology, 18, 70. https://doi.org/10.1186/s13045-025-01642-x
Dai, J., et al. (2024). Spatial epigenomics maps transcriptional and chromatin landscapes in mammalian development. Current Opinion in Biotechnology, 87, 103098.
Deng, Y., Bartosovic, M., Ma, S., Zhang, D., Kukanja, P., Xiao, Y., & Voet, T. (2022). Spatial profiling of chromatin accessibility in mouse and human tissues. Nature, 609, 375–383. https://doi.org/10.1038/s41586-022-05094-1
Du, J., & Yang, Y. C. (2025). Spatial omics in 3D in vitro models. Journal of Translational Medicine, 23, 1356.
Duhamel, M., Drelich, L., Wisztorski, M., & Aboulouard, S. (2022). Spatial analysis of the glioblastoma proteome reveals specific molecular signatures and markers of survival. Nature Communications, 13, 3420.
Fan, J., Lu, F., Qin, T., et al. (2023). Multiomic analysis of cervical squamous cell carcinoma identifies cellular ecosystems with biological and clinical relevance. Nature Genetics, 55, 2175–2188.
Fimmu, et al. (2026). Epigenomic priming and transcriptional decoupling in treatment-resistant malignancy [Journal article].
Fioe, et al. (2026). Computational infrastructure requirements for high-resolution spatial multi-omics analysis [Journal article].
Gong, D., Arbesfeld-Qiu, J. M., Perrault, E., Bae, J. W., & Hwang, W. L. (2024). Spatial oncology: Translating contextual biology to the clinic. Cancer Cell, 42(10), 1653–1675.
Gu, Y., & Jia, G. (2026). Spatial multi-omics integration and regulatory inference via MultiGATE. Genome Biology and Communications, 2026(1), 16–32.
Hernandez, S., Lazcano, R., Serrano, A., Powell, S., Kostousov, L., Mehta, J., Khan, K., Lu, W., & Solis, L. M. (2022). Challenges and opportunities for immunoprofiling using a spatial high-plex technology: The NanoString GeoMx digital spatial profiler. Frontiers in Oncology, 12, 890410. https://doi.org/10.3389/fonc.2022.890410
Hui, T., Zhou, J., Yao, M., Xie, Y., & Zeng, H. (2025). Advances in spatial omics technologies. Small Methods, 9, e2401171. https://doi.org/10.1002/smtd.202401171
Ji, A. L., Rubin, A. J., Thrane, K., Jiang, S., Reynolds, D. L., Meyers, R. M., Guo, M. G., George, B. M., Mollbrink, A., Bergenstråhle, J., Larsson, L., Bai, Y., Zhu, B., Bhaduri, A., Meyers, J. M., Rovira-Clavé, X., Hollmig, S. T., Aasi, S. Z., Nolan, G. P., ... Khavari, P. A. (2021). Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell, 182(2), 497–514.
Jiang, Z., Zhang, H., Gao, Y., & Sun, Y. (2025). Multi-omics strategies for biomarker discovery and application in personalized oncology. Molecular Biomedicine, 6, 115. https://doi.org/10.1186/s43556-025-00340-0
Khairnar, M., & Marda, S. (2025). Integrative multi-omics in precision medicine: From molecular interconnectivity to single-cell resolution. International Journal of Scientific Research and Technology, 2(7), 162–186.
Kiessling, P., & Kuppe, C. (2024). Spatial multi-omics: Novel tools to study the complexity of cardiovascular diseases. Genome Medicine, 16(1), 14. https://doi.org/10.1186/s13073-024-01282-y
KRONOS. (2025). KRONOS: Self-supervised foundation model for spatial proteomics image analysis [Software documentation and methods reference].
Kuppe, C., Ramirez Flores, R. O., Li, Z., Hayat, S., Levinson, R. T., & Saez-Rodriguez, J. (2022). Spatial multi-omic map of human myocardial infarction. Nature, 608(7924), 766–777.
Lang, Y., Cui, M., Wang, X., & Xiao, L. (2026). Spatial AI: Deep learning architectures for topological analysis of immune evasion. Frontiers in Oncology, 16, 1762907.
Lee, S., Kim, G., Lee, J., Lee, A. C., & Kwon, S. (2024). Mapping cancer biology in space: Applications and perspectives on spatial omics for oncology. Molecular Cancer, 23, 26.
Lee, Y., Lee, M., Shin, Y., Kim, K., & Kim, T. (2025). Spatial omics in clinical research: A comprehensive review of technologies and guidelines for applications. International Journal of Molecular Sciences, 26(9), 3949. https://doi.org/10.3390/ijms26093949
Lewis, S. M., Marx, V., & Zhao, Y. (2021). Spatial genomics, transcriptomics, and proteomics deconstruct tumor molecular architecture. Cancer Cell, 39, 446–463.
Li, Z., Qu, S., Liang, H., et al. (2025). Integrative deep learning of spatial multi-omics with SWITCH. Nature Computational Science, 5(11), 1051–1063.
Lin, M., Guo, J., Gu, Z., Tang, W., Tao, H., You, S., Jia, D., Sun, Y., & Jia, P. (2025). Machine learning and multi-omics integration: Advancing cardiovascular translational research and clinical practice. Journal of Translational Medicine, 23, 388.
Liu, X., Peng, T., Xu, M., Lin, S., Hu, B., Chu, T., Liu, B., Xu, Y., Ding, W., Li, L., Cao, C., & Wu, P. (2024). Spatial multi-omics: Deciphering the technological landscape of integration of multi-omics and its applications. Journal of Hematology & Oncology, 17(1), 72. https://doi.org/10.1186/s13045-024-01596-9
Liu, Y., Yang, M., Deng, Y., Su, G., Enninful, A., Guo, C. C., Tebaldi, T., Zhang, D., Kim, D., Bai, Z., & Fan, R. (2020). High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell, 183(6), 1665–1681. https://doi.org/10.1016/j.cell.2020.10.026
Long, Y., Ang, K. S., Sethi, R., et al. (2024). Deciphering spatial domains from spatial multi-omics with SpatialGlue. Nature Methods, 21(9), 1658–1667.
Luo, B., Teng, F., Tang, G., et al. (2025). StereoMM: A graph fusion model for integrating spatial transcriptomic data and pathological images. Briefings in Bioinformatics, 26(3), bbaf210.
Marconato, L., Palla, G., Yamauchi, K. A., Virshup, I., Heidari, E., Treis, T., Vierdag, W. M., Toth, M., Stockhaus, S., Shrestha, R. B., & Stegle, O. (2025). SpatialData: An open and universal data framework for spatial omics. Nature Methods, 22(1), 58–62. https://doi.org/10.1038/s41592-024-02212-x
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019). Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics, 51(4), 584–591. https://doi.org/10.1038/s41588-019-0379-x
McKellar, D. W., et al. (2023). Spatial mapping of the total transcriptome by in situ polyadenylation. Nature Biotechnology, 41, 513–520.
Miao, J., Li, J., Xin, J., Tu, J., Ge, M., et al. (2025). MultiGATE: Integrative analysis and regulatory inference in spatial multi-omics data via graph representation learning. Nature Communications, 16, 9403.
Moffitt, J. R., Lundberg, E., & Heyn, H. (2022). The emerging landscape of spatial profiling technologies. Nature Reviews Genetics, 23(12), 741–759.
Mohr, A. E., Ortega-Santos, C. P., Whisner, C. M., Klein-Seetharaman, J., & Jasbi, P. (2024). Navigating challenges and opportunities in multi-omics integration for personalized healthcare. Biomedicines, 12(7), 1496. https://doi.org/10.3390/biomedicines12071496
Paik, D. T., Cho, S., Tian, L., Chang, H. Y., & Wu, J. C. (2020). Single-cell RNA sequencing in cardiovascular development, disease and medicine. Nature Reviews Cardiology, 17(8), 457–473.
Palaganas, R., Sidiropoulos, D. N., Morris, M., & Stein-O'Brien, G. L. (2026). Mapping multipathology via spatial omic integration. Current Opinion in Biotechnology, 97, 103398. https://doi.org/10.1016/j.copbio.2025.103398
Picard, M., Scott-Boyer, M.-P., Bodein, A., Périn, O., & Droit, A. (2021). Integration strategies of multi-omics data for machine learning analysis. Computational and Structural Biotechnology Journal, 19, 3735–3746.
Privé, F., Aschard, H., Carmi, S., Folkersen, L., Hoggart, C., O'Reilly, P. F., & Vilhjálmsson, B. J. (2021). Portability of polygenic scores across populations and cohorts. Nature Communications, 12, 2045.
Radha, S., & Marda, S. (2025). Bioinformatics and multi-omics integration in personalized medicine. Journal of Modern Technology and Biological Sciences, 2(4), 14–23.
Roelands, J., et al. (2023). Spatial profiling of the macrophage immune microenvironment in colorectal cancer liver metastasis using imaging mass cytometry. Journal of Clinical Oncology, 41, 3515.
Sahu, P., Satapathy, A., Satapathy, A., & Satapathy, T. (2026). Single-cell multi-omics for biomarker discovery: Technologies, molecular mechanisms, applications and translational challenges. Letters in Drug Design & Discovery, 22(2), 100286.
scGPT-spatial. (2025). scGPT-spatial: Continual pretraining for spatial domain identification [Software documentation and methods reference].
Shanmugam, Y., & Ravikumar, L. (2026). Narrative review of multi-omics biomarker and biosensing literature. Advances in Biomarker Sciences and Technology, 8, 464–478. https://doi.org/10.1016/j.abst.2026.05.007
Srivastava, A., & Singh, R. (2024). Spatial metabolomics and lipidomics: Bridging molecular flux and tissue pathology [Journal article].
Ståhl, P. L., Salmén, F., & Vickovic, S. (2016). Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science, 353(6294), 78–82. https://doi.org/10.1126/science.aaf2403
Sun, C., Li, T., Song, X., Huang, L., Zang, Q., Xu, J., Bi, N., Jiao, G., Hao, Y., Chen, Y., Zhang, R., Luo, Z., Li, X., Wang, L., Wang, Z., Song, Y., He, J., & Abliz, Z. (2019). Spatially resolved metabolomics to discover tumor-associated metabolic alterations. Proceedings of the National Academy of Sciences, 116(1), 52–57.
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., & Ellinor, P. T. (2023). Transfer learning enables predictions in network biology. Nature, 618, 616–624.
Wang, X., & Fan, J. (2021). Spatiotemporal molecular medicine: A new era of clinical and translational medicine. Clinical and Translational Medicine, 11(1), e294.
Wu, X., Xu, W., Deng, L., Cao, G., & Dai, J. (2026). A guide for spatial omics technologies. Advanced Science, 2026. https://doi.org/10.1002/advs.202520806
Wu, Y., Cheng, Y., Wang, X., Fan, J., & Gao, Q. (2022). Spatial omics: Navigating to the golden era of cancer research. Clinical and Translational Medicine, 12(1), e696. https://doi.org/10.1002/ctm2.696
Yang, J., et al. (2022). Phenotypic screening with deep learning identifies HDAC6 inhibitors as cardioprotective in a BAG3 mouse model of dilated cardiomyopathy. Science Translational Medicine, 14, eabl5654.
Zehra, B., Alasrawi, S., Almansoori, S., Al-Ali, A., & Bhat, K. B. (2026). Multiomics approaches to cardiovascular disease: Linking molecular resolution to clinical translation. American Journal of Physiology-Heart and Circulatory Physiology, 2026. https://doi.org/10.1152/ajpheart.2026
Recommended articles
Illuminating Biological Dark Matter: Integrating Metagenomics, Synthetic Biology, and AI to Unlock Microbial and Genomic Potential for Therapeutics and Biotechnology
An Integrated AI-Driven Framework for Maternal Resource Intelligence Shortages Across U.S. Hospital
From Scarcity to Fairness: A Closed-Loop Reinforcement Learning and Geospatial Analytics Framework for Equitable Healthcare Supply Chain Preparedness
Article metrics
View details
0
Downloads
0
Citations
14
Views
0
Save
Save
0
Citation
Citation
14
View
View
0
Share
Share