{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:14:41Z","timestamp":1788394481540,"version":"build-2803163510"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":79,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Spatial transcriptomics platforms such as 10x Visium capture whole-transcriptome expression but at spot-level resolution where each measurement aggregates multiple cells. Existing computational deconvolution methods require external scRNA-seq references, and the growing set of morphology-guided methods (iStar, SpaHDmap, GHIST, Thor, PRTS) each requires per-dataset model training or paired subcellular spatial data. Here we present SpatialCell AI, the first framework to combine training-free operation, reference-free expression integration, and per-cell output granularity for spatial transcriptomics. We validated SpatialCell AI on a matched colorectal cancer sample analyzed across Visium (55\u00a0\u03bcm), Visium HD (8\u00a0\u03bcm and 16\u00a0\u03bcm), and Xenium (single-cell ground truth). On Visium HD 8\u00a0\u03bcm input, SpatialCell AI achieves an expression correlation of\n                    <jats:italic>r<\/jats:italic>\n                    \u2009=\u20090.791 against the Xenium reference \u2014 the highest of any method tested on this sample. Across six distribution-based validation metrics on the matched 408-gene panel, the SpatialCell AI HD variants lead on the majority of metrics, with no competing method winning on any. A strict matched-region head-to-head comparison reveals a clear architectural distinction: training-free integration improves monotonically as input bins approach single-cell scale, while the trained morphology-guided methods iStar and SpaHDmap track the raw baseline on every platform. SpatialCell AI achieves its strongest performance on Visium HD inputs, where it substantially outperforms both raw baselines and competing morphology-guided methods while transforming spot-level data into individual cell records with spatial coordinates and per-cell expression \u2014 an output no raw spot measurement can provide.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-55246-w","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T18:33:51Z","timestamp":1781634831000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SpatialCell AI achieves reference-free single-cell resolution from spot-based spatial transcriptomics through morphology-guided enhancement"],"prefix":"10.1038","volume":"16","author":[{"given":"Abdalla","family":"Elbialy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"key":"55246_CR1","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1038\/s41592-020-01033-y","volume":"18","author":"V Marx","year":"2021","unstructured":"Marx, V. Method of the Year: spatially resolved transcriptomics. Nat. Methods. 18, 9\u201314 (2021).","journal-title":"Nat. Methods"},{"key":"55246_CR2","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1038\/s41592-021-01203-6","volume":"18","author":"SM Lewis","year":"2021","unstructured":"Lewis, S. M. et al. Spatial omics and multiplexed imaging to explore cancer biology. Nat. Methods. 18, 997\u20131012 (2021).","journal-title":"Nat. Methods"},{"key":"55246_CR3","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1038\/s41586-021-03634-9","volume":"596","author":"A Rao","year":"2021","unstructured":"Rao, A., Barkley, D., Fran\u00e7a, G. S. & Yanai, I. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211\u2013220 (2021).","journal-title":"Nature"},{"key":"55246_CR4","doi-asserted-by":"publisher","first-page":"1900221","DOI":"10.1002\/bies.201900221","volume":"42","author":"M Asp","year":"2020","unstructured":"Asp, M., Bergenstr\u00e5hle, J. & Lundeberg, J. Spatially resolved transcriptomes\u2014next generation tools for tissue exploration. BioEssays 42, 1900221 (2020).","journal-title":"BioEssays"},{"key":"55246_CR5","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1126\/science.aaw1219","volume":"363","author":"SG Rodriques","year":"2019","unstructured":"Rodriques, S. G. et al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463\u20131467 (2019).","journal-title":"Science"},{"key":"55246_CR6","doi-asserted-by":"crossref","unstructured":"Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).","DOI":"10.1126\/science.aaa6090"},{"key":"55246_CR7","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1126\/science.aaf2403","volume":"353","author":"PL St\u00e5hl","year":"2016","unstructured":"St\u00e5hl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78\u201382 (2016).","journal-title":"Science"},{"key":"55246_CR8","doi-asserted-by":"publisher","first-page":"1777","DOI":"10.1016\/j.cell.2022.04.003","volume":"185","author":"A Chen","year":"2022","unstructured":"Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777\u20131792 (2022).","journal-title":"Cell"},{"key":"55246_CR9","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1038\/s41587-021-01139-4","volume":"40","author":"V Kleshchevnikov","year":"2022","unstructured":"Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 40, 661\u2013671 (2022).","journal-title":"Nat. Biotechnol."},{"key":"55246_CR10","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1038\/s41587-021-00830-w","volume":"40","author":"DM Cable","year":"2022","unstructured":"Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517\u2013526 (2022).","journal-title":"Nat. Biotechnol."},{"key":"55246_CR11","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1186\/s13059-021-02362-7","volume":"22","author":"R Dong","year":"2021","unstructured":"Dong, R. & Yuan, G. C. SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Genome Biol. 22, 145 (2021).","journal-title":"Genome Biol."},{"key":"55246_CR12","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.1038\/s41467-023-37168-7","volume":"14","author":"H Li","year":"2023","unstructured":"Li, H. et al. A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics. Nat. Commun. 14, 1548 (2023).","journal-title":"Nat. Commun."},{"key":"55246_CR13","doi-asserted-by":"publisher","first-page":"bbac245","DOI":"10.1093\/bib\/bbac245","volume":"23","author":"J Chen","year":"2022","unstructured":"Chen, J. et al. A comprehensive comparison on cell-type composition inference for spatial transcriptomics data. Brief. Bioinform. 23, bbac245 (2022).","journal-title":"Brief. Bioinform."},{"key":"55246_CR14","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1038\/s41592-022-01480-9","volume":"19","author":"B Li","year":"2022","unstructured":"Li, B. et al. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nat. Methods. 19, 662\u2013670 (2022).","journal-title":"Nat. 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Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology. Nat. Biotechnol. 42, 1372\u20131377 (2024).","journal-title":"Nat. Biotechnol."},{"key":"55246_CR18","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1038\/s41556-025-01838-z","volume":"28","author":"J Tang","year":"2026","unstructured":"Tang, J. et al. The interpretable multimodal dimension reduction framework SpaHDmap enhances resolution in spatial transcriptomics. Nat. Cell. Biol. 28, 363\u2013377. https:\/\/doi.org\/10.1038\/s41556-025-01838-z (2026).","journal-title":"Nat. Cell. Biol."},{"key":"55246_CR19","doi-asserted-by":"publisher","first-page":"1900","DOI":"10.1038\/s41592-025-02795-z","volume":"22","author":"X Fu","year":"2025","unstructured":"Fu, X. et al. Spatial gene expression at single-cell resolution from histology using deep learning with GHIST. Nat. Methods. 22, 1900\u20131910 (2025).","journal-title":"Nat. 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BMC Bioinform. 26, 22 (2025).","journal-title":"BMC Bioinform."},{"key":"55246_CR23","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1186\/s13059-021-02404-0","volume":"22","author":"J Zhu","year":"2021","unstructured":"Zhu, J., Sun, S. & Zhou, X. SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies. Genome Biol. 22, 184 (2021).","journal-title":"Genome Biol."},{"key":"55246_CR24","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1038\/s41587-022-01273-7","volume":"40","author":"Y Ma","year":"2022","unstructured":"Ma, Y. & Zhou, X. Spatially informed cell-type deconvolution for spatial transcriptomics. Nat. Biotechnol. 40, 1349\u20131359 (2022).","journal-title":"Nat. Biotechnol."},{"key":"55246_CR25","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1038\/s41592-021-01264-7","volume":"18","author":"T Biancalani","year":"2021","unstructured":"Biancalani, T. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat. Methods. 18, 1352\u20131362 (2021).","journal-title":"Nat. Methods"},{"key":"55246_CR26","doi-asserted-by":"crossref","unstructured":"Macenko, M. et al. A method for normalizing histology slides for quantitative analysis. In 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro 1107\u20131110 (IEEE, 2009).","DOI":"10.1109\/ISBI.2009.5193250"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-55246-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-55246-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-55246-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T23:29:44Z","timestamp":1788391784000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-55246-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,16]]},"references-count":26,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["55246"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-55246-w","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,16]]},"assertion":[{"value":"22 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A.E. is the Founder and CEO of SpatialCell AI, LLC, which developed and owns the proprietary software described in this manuscript. All validation was conducted using publicly available datasets to ensure objectivity.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"27636"}}