Integrative Biomedical Research
Integrative Biomedical Research (Journal of Angiotherapy) | Online ISSN 3068-6326
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Volume 10 Number 1 2026
REVIEWS (Open Access)
Single-Cell and Spatial Transcriptomics in Precision Diagnostics: A Roadmap for Clinical Adoption
Muhammad Rizki Saputra 1*, Heng Yen Khong 2
Integrative Biomedical Research 10 (1) 1-8 https://doi.org/10.25163/biomedical.10110887
Submitted: 21 April 2026 Revised: 08 June 2026 Accepted: 17 June 2026 Published: 19 June 2026
Abstract
Precision diagnostics increasingly demands resolution beyond what bulk tissue profiling can offer. Single-cell RNA sequencing solved much of the cellular-heterogeneity problem but at the cost of native spatial context, while emerging spatial transcriptomics (ST) platforms restore that context at the price of cost, throughput, and analytic complexity. We performed a narrative, synthesis of peer-reviewed literature, covering single-cell and spatial transcriptomic technologies, computational deconvolution frameworks, deep learning architectures, and oncology-focused clinical translation studies. Comparative platform benchmarking shows a persistent resolution-versus-breadth trade-off across NGS-based and imaging-based spatial technologies, while computational tools have matured from simple deconvolution algorithms toward graph neural networks and transformer-based foundation models capable of over 90% cell-annotation accuracy. Clinical oncology applications across breast, colorectal, hepatocellular, and pancreatic cancers demonstrate that spatially resolved tumor-immune architecture carries prognostic and predictive value unavailable from bulk or dissociated single-cell data alone. The “compression-to-clinic” paradigm — using high-dimensional spatial-omics purely as a discovery engine to distill parsimonious, FFPE-compatible biomarker panels — offers the most tractable path from the research bench to the pathology bench. Keywords: spatial transcriptomics; single-cell RNA sequencing; tumor microenvironment; precision diagnostics; deep learning; FFPE; biomarker compression
References
Andersson, A., Larsson, L., Stenbeck, L., Salmén, F., Mollbrink, A., Drewes, J., & Lundeberg, J. (2021). Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions. Nature Communications, 12(1), 6012. https://doi.org/10.1038/s41467-021-26271-2
Bassiouni, R., Idowu, M. O., Gibbs, L. D., Robila, V., Grizzard, P. J., Webb, M. G., Song, J., Noriega, A., Craig, D. W., & Carpten, J. D. (2023). Spatial transcriptomic analysis of a diverse patient cohort reveals a conserved architecture in triple-negative breast cancer. Cancer Research, 83(1), 34–48. https://doi.org/10.1158/0008-5472.CAN-22-1920
Cable, D. M., Murray, E., Zou, L. S., Goeva, A., Macosko, E. Z., Chen, F., & Irizarry, R. A. (2022). Robust decomposition of cell type mixtures in spatial transcriptomics. Nature Biotechnology, 40(4), 517–526. https://doi.org/10.1038/s41587-021-01131-0
Chen, A., Liao, S., Cheng, M., Ma, K., Wu, L., Lai, Y., Qiu, X., Yang, J., Xu, J., Hao, S., & Wang, J. (2022). Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell, 185(10), 1777–1792. https://doi.org/10.1016/j.cell.2022.04.003
Choe, K., Pak, U., Pang, Y., Hao, W., & Yang, X. (2023). Advances and challenges in spatial transcriptomics for developmental biology. Biomolecules, 13(1), 156. https://doi.org/10.3390/biom13010156
Croizer, H., et al. (2024). Deciphering the spatial landscape and plasticity of immunosuppressive fibroblasts in breast cancer. Nature Communications, 15(1), 2806. https://doi.org/10.1038/s41467-024-45208-z
Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., Duan, N., & Wang, B. (2024). scGPT: Toward building a foundation model for single-cell multi-omics using generative AI. Nature Methods, 21(8), 1470–1480. https://doi.org/10.1038/s41592-024-02201-0
Deng, Y., Bartosovic, M., Kukanja, P., Zhang, D., Liu, Y., Su, G., Enninful, A., Bai, Z., Castelo-Branco, G., & Fan, R. (2022). Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level. Science, 375(6581), 681–686. https://doi.org/10.1126/science.abg7216
Dong, K., & Zhang, S. (2022). Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nature Communications, 13(1), 1739. https://doi.org/10.1038/s41467-022-29439-6
Dries, R., Zhu, Q., Dong, R., Eng, C. H. L., Li, H., Liu, K., Fu, Y., Zhao, T., Sarkar, A., Bao, F., & Yuan, G. C. (2021). Giotto: A toolbox for integrative analysis and visualization of spatial expression data. Genome Biology, 22(1), 78. https://doi.org/10.1186/s13059-021-02286-2
Feng, Y., Ma, W., Zang, Y., Guo, Y., Li, Y., Zhang, Y., Dong, X., Liu, Y., Zhan, X., Pan, Z., & Walker, G. (2024). Spatially organized tumor-stroma boundary determines the efficacy of immunotherapy in colorectal cancer patients. Nature Communications, 15(1), 10259. https://doi.org/10.1038/s41467-024-54615-1
Hédou, J., Maric, I., Bellan, G., Einhaus, J., Gaudillière, D. K., Ladant, F.-X., & Gaudillière, B. (2024). Discovery of sparse, reliable omic biomarkers with Stabl. Nature Biotechnology, 42(10), 1581–1593. https://doi.org/10.1038/s41587-023-02033-x
Hu, J., Li, X., Coleman, K., Schroeder, A., Ma, N., Irwin, D. J., Lee, E. B., Shinohara, R. T., & Li, M. (2021). SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nature Methods, 18(11), 1342–1351. https://doi.org/10.1038/s41592-021-01255-8
Hwang, W. L., Jagadeesh, K. A., Guo, J. A., Hoffman, H. I., Yadollahpour, P., & Regev, A. (2022). Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment. Nature Genetics, 54(8), 1178–1191. https://doi.org/10.1038/s41588-022-01134-8
Janesick, A., Shelansky, R., Gottscho, A. D., Wagner, F., Williams, S. R., & Satija, R. (2023). High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nature Communications, 14(1), 8353. https://doi.org/10.1038/s41467-023-43458-x
Khaliq, A. M., et al. (2024). Spatial transcriptomic analysis of primary and metastatic pancreatic cancers highlights tumor microenvironmental heterogeneity. Nature Genetics, 56(11), 2455–2465. https://doi.org/10.1038/s41588-024-01890-9
Khoury, R., Raffoul, C., Khater, C., & Hanna, C. (2025). Precision medicine in hematologic malignancies: Evolving concepts and clinical applications. Biomedicines, 13(7), 1654. https://doi.org/10.3390/biomedicines13071654
Kleshchevnikov, V., Shmatko, A., Dann, E., Aivazidis, A., King, H. W., Li, T., Elmentaite, R., Lomakin, A., Kedm, S., & Bayraktar, O. (2022). Cell2location maps fine-grained cell types in spatial transcriptomics. Nature Biotechnology, 40(5), 661–671. https://doi.org/10.1038/s41587-021-01139-4
Kleshchevnikov, V., Shmatko, A., Dann, E., Aivazidis, A., King, H. W., Li, T., Elmentaite, R., Lomakin, A., Kedm, S., & Bayraktar, O. (2022). Cell2location maps fine-grained cell types in spatial transcriptomics. Nature Biotechnology, 40(5), 661–671. https://doi.org/10.1038/s41587-021-01139-4
Lee, J. H. (2025). From single-cell maps to diagnostics: Enabling biomarker discovery in precision medicine. Academia Molecular Biology and Genomics, 2, Article 7859. https://doi.org/10.20935/AcadMolBioGen7859
Long, Y., Ang, K. S., Li, M., Chong, K. L. K., Sethi, R., Zhong, C., Xu, H., Ong, Z., Sachaphibulkij, K., Chen, A., & Wang, J. (2023). Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nature Communications, 14(1), 1155. https://doi.org/10.1038/s41467-023-36796-3
Long, Y., Ang, K. S., Li, M., Chong, K. L. K., Sethi, R., Zhong, C., Xu, H., Ong, Z., Sachaphibulkij, K., Chen, A., & Wang, J. (2023). Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nature Communications, 14(1), 1155. https://doi.org/10.1038/s41467-023-36796-3
Luo, H., Hussain, A., Abbas, M., Yuan, L., Shen, Y., Zhang, Z., Sun, G., Yin, X., & Huang, S. (2025). Droplet-based single-cell RNA sequencing: Decoding cellular heterogeneity for breakthroughs in cancer, reproduction, and beyond. Journal of Translational Medicine, 23(1), 1091. https://doi.org/10.1186/s12967-025-06996-0
Mondal, S., Kiruba, B., Sudhakaran, S., & Sundararajan, V. (2025). Unraveling the tumor microenvironment: The synergy of single-cell and spatial transcriptomics in clinical oncology. Frontiers in Oncology, 15, 1685565. https://doi.org/10.3389/fonc.2025.1685565
Nesari, A. M., MotieGhader, H., & Ghorbian, S. (2026). Advances and challenges in single-cell RNA sequencing data analysis: A comprehensive review. Briefings in Bioinformatics, 27(1), bbaf723. https://doi.org/10.1093/bib/bbaf723
Olokede, E. U., Ugoagwu, K. U., God-Giveth, O. T., Adigun, M. V., Ebiala, F. I., & Faisal, S. (2026). From spatial transcriptomics to clinic-ready diagnostic panels: A conceptual review of translating tumor microenvironment architecture into practical cancer biomarkers. International Research Journal of Oncology, 9(1), 69–92. https://doi.org/10.9734/irjo/2026/v9i1198
Palla, G., Spitzer, H., Klein, M., Fischer, D., Schaar, A. C., Kuemmerle, L. B., Rybakov, S., Ibarra, I. L., Holmberg, O., Virshup, I., & Theis, F. J. (2022). Squidpy: A scalable framework for spatial omics analysis. Nature Methods, 19(2), 171–178. https://doi.org/10.1038/s41592-021-01358-2
Pentimalli, T. M., Karaiskos, N., & Rajewsky, N. (2025). Challenges and opportunities in the clinical translation of high-resolution spatial transcriptomics. Annual Review of Pathology: Mechanisms of Disease, 20, 405–432. https://doi.org/10.1146/annurev-pathol-042220-024405
Qiao, D., Wang, R. C., & Wang, Z. (2025). Precision oncology: Current landscape, emerging trends, challenges, and future perspectives. Cells, 14(22), 1804. https://doi.org/10.3390/cells14221804
Schürch, C. M., Bhate, S. S., Barlow, G. L., Phillips, D. J., Noti, L., Zlobec, I., Chu, P., Black, S., Demeter, J., McIlwain, D. R., & Nolan, G. P. (2020). Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell, 182(5), 1341–1359. https://doi.org/10.1016/j.cell.2020.07.005
Shao, X., Li, C., Yang, H., Lu, X., Liao, J., Qian, J., Wang, K., Cheng, J., Yang, P., Chen, H., & Fan, X. (2022). Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk. Nature Communications, 13(1), 4429. https://doi.org/10.1038/s41467-022-32111-8
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., & Lundeberg, J. (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
Stur, E., Corvigno, S., Xu, M., Chen, K., Tan, Y., Lee, S., Zeng, X., Kaipparettu, B. A., & Sood, A. K. (2022). Spatially resolved transcriptomics of high-grade serous ovarian carcinoma. iScience, 25(3), 103923. https://doi.org/10.1016/j.isci.2022.103923
Sweileh, M. S. (2026). The global landscape of spatial transcriptomics in oncology: A bibliometric analysis. Discover Oncology, 17, 731. https://doi.org/10.1007/s12672-026-04937-x
Tejada-Lapuerta, A., Gao, X., Bhaduri, A., Ma, F., & Theis, F. J. (2025). Nicheformer: A foundation model for single-cell and spatial omics. Nature Methods, 22, 2525–2538. https://doi.org/10.1038/s41592-025-02768-2
Villacampa, E. G., Larsson, L., Mirzazadeh, R., Kvastad, L., Andersson, A., Mollbrink, A., Kokaraki, G., & Lundeberg, J. (2021). Genome-wide spatial expression profiling in formalin-fixed tissues. Cell Genomics, 1(3), 100065. https://doi.org/10.1016/j.xgen.2021.100065
Walsh, L. A., & Quail, D. F. (2023). Decoding the tumor microenvironment with spatial technologies. Nature Immunology, 24(12), 1982–1993. https://doi.org/10.1038/s41590-023-01678-9
Wang, N., Hong, W., Wu, Y., Chen, Z. S., Bai, M., Wang, W., & Zhu, J. (2024). Next-generation spatial transcriptomics in tumor biology: Technologies, bioinformatics, and applications. MedComm, 5(10), e765. https://doi.org/10.1002/mco2.765
Weiderman, R. M., Hasan, M., & Miller, L. C. (2025). Applications of spatial transcriptomics in veterinary medicine: A scoping review of research, diagnostics, and treatment strategies. International Journal of Molecular Sciences, 26(13), 6163. https://doi.org/10.3390/ijms26136163
Wolf, F. A., Angerer, P., & Theis, F. J. (2018). SCANPY: Large-scale single-cell gene expression data analysis. Genome Biology, 19(1), 15. https://doi.org/10.1186/s13059-017-1382-0
Wu, L., Yan, J., Zhang, Y., et al. (2023). An invasive zone in human liver cancer identified by Stereo-seq promotes hepatocyte–tumor cell crosstalk, local immunosuppression and tumor progression. Cell Research, 33(7), 585–603. https://doi.org/10.1038/s41422-023-00831-1
Wu, S. Z., Al-Eryani, G., Roden, D. L., Junankar, S., Harvey, K., Andersson, A., Thennavan, A., Wang, C., Torpy, J. R., Bartonicek, N., & Swarbrick, A. (2021). A single-cell and spatially resolved atlas of human breast cancers. Nature Genetics, 53(9), 1334–1347. https://doi.org/10.1038/s41588-021-00911-1
Xiao, J., Wang, K., Xing, C., Chen, Y., Zhou, S., Hu, C., Xu, D., & Peng, Y. (2024). Integrating spatial and single-cell transcriptomics reveals tumor heterogeneity and intercellular networks in colorectal cancer. Cell Death & Disease, 15, 326. https://doi.org/10.1038/s41419-024-06714-8
Zhu, J., Deng, R., Guo, J., Yao, T., Lu, S., Qu, C., Tang, Y., & Huo, Y. (2026). A comprehensive survey of computer vision methods for spatial transcriptomics. Briefings in Bioinformatics, 27(3), bbag255. https://doi.org/10.1093/bib/bbag255
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