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Unlocking single-cell level and continuous whole-slide insights in spatial transcriptomics with PanoSpace

Nature Computational Science (2026)Cite this article

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Abstract

Spatial transcriptomics has transformed the mapping of gene expression within intact tissues, yet current sequencing-based platforms are limited by coarse spot-level resolution and sparse sampling that leaves large interspot regions unmeasured. Here we introduce PanoSpace, a computational framework that integrates low-resolution spatial transcriptomics with high-resolution histology and matched single-cell RNA sequencing to reconstruct a continuous, single-cell-level map across entire tissue sections. Originally developed for tumors, PanoSpace accurately reconstructs cellular locations, cell identities and gene expression profiles, enabling detailed characterization of intracell-type heterogeneity and spatially organized cell–cell interactions. Application to breast and prostate cancers reveals complex cellular architectures and tumor microenvironment dynamics mediated by cancer-associated fibroblasts. Thanks to its modular design, PanoSpace can be seamlessly adapted to noncancerous tissues, as demonstrated by precise spatial reconstruction in mouse brain. Together, these results demonstrate that PanoSpace enables comprehensive spatial transcriptomic analysis and facilitates biological discovery.

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Fig. 1: Overview of the PanoSpace framework.
Fig. 2: Benchmarking and validation of PanoSpace using Xenium breast and lung cancer datasets.
Fig. 3: Single-cell resolution spatial analysis of breast cancer tissue using PanoSpace.
Fig. 4: Single-cell resolution spatial analysis of prostate cancer tissue using PanoSpace.
Fig. 5: Single-cell spatial analysis in noncancer tissues using PanoSpace.

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Data availability

All datasets used in this study are publicly available. For simulation experiments, the 10x Xenium breast cancer dataset7 was obtained from the 10x Genomics official website (https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast), with the paired human breast cancer scRNA-seq ref. 35 available from GEO under accession GSE176078. The 10x Xenium Prime 5K human lung cancer dataset was downloaded from https://www.10xgenomics.com/datasets/xenium-human-lung-cancer-post-xenium-technote, with the matched lung cancer scRNA-seq ref. 54 available at GSE131907. The 10x Xenium Prime 5K fresh-frozen mouse brain hemisphere dataset was obtained from https://www.10xgenomics.com/datasets/xenium-prime-fresh-frozen-mouse-brain, and the paired scRNA-seq ref. 55 can be accessed via the Allen Brain Map. For real data analysis, the 10x Visium human breast cancer dataset was downloaded from https://www.10xgenomics.com/datasets/human-breast-cancer-ductal-carcinoma-in-situ-invasive-carcinoma-ffpe-1-standard-1-3-0, with the same scRNA-seq reference as the Xenium breast cancer dataset. The 10x Visium human prostate cancer dataset was obtained from https://www.10xgenomics.com/datasets/human-prostate-cancer-adenocarcinoma-with-invasive-carcinoma-ffpe-1-standard-1-3-0, and the paired scRNA-seq ref. 38 was downloaded from the European Genome-Phenome Archive (EGA) under accession code EGAS00001005115. The 10x Visium adult mouse olfactory bulb dataset was downloaded from https://www.10xgenomics.com/datasets/adult-mouse-olfactory-bulb-1-standard, and the corresponding scRNA-seq ref. 56 is available at GSE121891. The PanNuke dataset47 can be accessed at https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke. The ISH images from Allen Brain Atlas are available at https://mouse.brain-map.org/. Source data are provided with this paper.

Code availability

The source code of PanoSpace, along with Jupyter notebooks for reproducing the results in this study, is available via GitHub at https://github.com/hehuifeng/PanoSpace and is also available via Zenodo at https://doi.org/10.5281/zenodo.17579559 (ref. 57) under the MIT License.

References

  1. Rao, A., Barkley, D., França, G. S. & Yanai, I. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211–220 (2021).

    Article  Google Scholar 

  2. Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681 (2020).

    Article  Google Scholar 

  3. Stahl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    Article  Google Scholar 

  4. Larsson, L., Frisén, J. & Lundeberg, J. Spatially resolved transcriptomics adds a new dimension to genomics. Nat. Methods 18, 15–18 (2021).

    Article  Google Scholar 

  5. Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

    Article  Google Scholar 

  6. Shah, S. et al. Dynamics and spatial genomics of the nascent transcriptome by intron seqFISH. Cell 174, 363–376 (2018).

    Article  Google Scholar 

  7. Janesick, A. et al. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nat. Commun. 14, 8353 (2023).

    Article  Google Scholar 

  8. Moffitt, J. R. et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018).

    Article  Google Scholar 

  9. Hu, J. et al. Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA. Cell Syst. 14, 404–417 (2023).

    Google Scholar 

  10. Tu, J. J., Yan, H., Zhang, X. F. & Lin, Z. Precise gene expression deconvolution in spatial transcriptomics with STged. Nucleic Acids Res. 53, gkaf087 (2025).

    Article  Google Scholar 

  11. Tu, J. J., Li, H. S., Yan, H. & Zhang, X. F. EnDecon: cell type deconvolution of spatially resolved transcriptomics data via ensemble learning. Bioinformatics 39, btac825 (2023).

    Article  Google Scholar 

  12. Wang, M. G., Chen, L. & Zhang, X. F. Dual decoding of cell types and gene expression in spatial transcriptomics with PANDA. Nucleic Acids Res. 52, 12173–12190 (2024).

    Article  Google Scholar 

  13. Biancalani, T. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat. Methods 18, 1352–1362 (2021).

    Article  Google Scholar 

  14. Vahid, M. R. et al. High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE. Nat. Biotechnol. 41, 1543–1548 (2023).

    Article  Google Scholar 

  15. Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517–526 (2022).

    Article  Google Scholar 

  16. Dong, R. & Yuan, G. C. SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Genome Biol. 22, 145 (2021).

    Article  Google Scholar 

  17. Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 40, 661–671 (2022).

    Article  Google Scholar 

  18. Lopez, R. et al. DestVI identifies continuums of cell types in spatial transcriptomics data. Nat. Biotechnol. 40, 1360–1369 (2022).

    Article  Google Scholar 

  19. Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I. & Heyn, H. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 49, e50 (2021).

    Article  Google Scholar 

  20. Andersson, A. et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol. 3, 565 (2020).

    Article  Google Scholar 

  21. Sun, D., Liu, Z., Li, T., Wu, Q. & Wang, C. STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing. Nucleic Acids Res. 50, e42 (2022).

    Article  Google Scholar 

  22. Ma, Y. & Zhou, X. Spatially informed cell-type deconvolution for spatial transcriptomics. Nat. Biotechnol. 40, 1349–1359 (2022).

    Article  Google Scholar 

  23. Liu, Z., Wu, D., Zhai, W. & Ma, L. SONAR enables cell type deconvolution with spatially weighted Poisson-Gamma model for spatial transcriptomics. Nat. Commun. 14, 4727 (2023).

    Article  Google Scholar 

  24. Chen, J. et al. Cell composition inference and identification of layer-specific spatial transcriptional profiles with POLARIS. Sci. Adv. 9, eadd9818 (2023).

    Article  Google Scholar 

  25. Zhang, D. et al. Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology. Nat. Biotechnol. 42, 1372–1377 (2024).

    Article  Google Scholar 

  26. Bergenstrahle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. Nat. Biotechnol. 40, 476–479 (2022).

    Article  Google Scholar 

  27. He, B. et al. Integrating spatial gene expression and breast tumour morphology via deep learning. Nat. Biomed. Eng. 4, 827–834 (2020).

    Article  Google Scholar 

  28. Wu, Y. et al. STASCAN deciphers fine-resolution cell distribution maps in spatial transcriptomics by deep learning. Genome Biol. 25, 278 (2024).

    Article  Google Scholar 

  29. Fu, Y. C., Das, A., Wang, D., Braun, R. & Yi, R. scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis. Genome Biol. 25, 164 (2024).

    Article  Google Scholar 

  30. Zhang, P. et al. Systematic inference of super-resolution cell spatial profiles from histology images. Nat. Commun. 16, 1838 (2025).

    Article  Google Scholar 

  31. Wan, X. et al. Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope. Nat. Commun. 14, 7848 (2023).

    Article  Google Scholar 

  32. Hörst, F. et al. CellViT: vision transformers for precise cell segmentation and classification. Med. Image Anal. 94, 103143 (2024).

    Article  Google Scholar 

  33. Grant Application Resources for Visium Spatial Gene Expression Document Number LIT000086 Rev H (10x Genomics, 2024).

  34. Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291 (2019).

    Google Scholar 

  35. Wu, S. Z. et al. A single-cell and spatially resolved atlas of human breast cancers. Nat. Genet. 53, 1334–1347 (2021).

    Article  Google Scholar 

  36. Xu, C. et al. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Mol. Syst. Biol. 17, e9620 (2021).

    Article  Google Scholar 

  37. Itoh, G. et al. Cancer-associated fibroblasts induce cancer cell apoptosis that regulates invasion mode of tumours. Oncogene 36, 4434–4444 (2017).

    Article  Google Scholar 

  38. Wu, S. Z. et al. Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis. Genome Med. 13, 81 (2021).

    Article  Google Scholar 

  39. Graham, S. et al. Hover-Net: simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med. Image Anal. 58, 101563 (2019).

    Article  Google Scholar 

  40. Lein, E. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

    Article  Google Scholar 

  41. Nagendran, M. et al. 1457 Visium HD enables spatially resolved, single-cell scale resolution mapping of FFPE human breast cancer tissue. J. Immunother. Cancer 11, A1620 (2023).

    Google Scholar 

  42. Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792 (2022).

    Article  Google Scholar 

  43. Oquab, M. et al. DINOv2: learning robust visual features without supervision. Preprint at https://arxiv.org/abs/2304.07193 (2024).

  44. Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at https://arxiv.org/abs/1409.1556 (2014).

  45. Lu, M. Y. et al. A visual-language foundation model for computational pathology. Nat. Med. 30, 863–874 (2024).

    Article  Google Scholar 

  46. Maurer, K. et al. Coordinated immune networks in leukemia bone marrow microenvironments distinguish response to cellular therapy. Sci. Immunol. 10, eadr0782 (2025).

    Article  Google Scholar 

  47. Gamper, J. et al. in Digital Pathology (eds Reyes-Aldasoro, C. et al.) 11–19 (Springer, 2019).

  48. Flamary, R. et al. POT: Python optimal transport. J. Mach. Learn. Res. 22, 3571–3578 (2021).

    Google Scholar 

  49. Gurobi Optimizer Reference Manual (Gurobi Optimization, LLC, 2024).

  50. Chapelle, O., Schölkopf, B. & Zien, A. Semi-Supervised Learning (MIT, 2006).

  51. Belkin, M., Niyogi, P. & Sindhwani, V. Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399–2434 (2006).

    MathSciNet  Google Scholar 

  52. Weigert, M. & Schmidt, U. Nuclei instance segmentation and classification in histopathology images with StarDist. In The IEEE International Symposium on Biomedical Imaging Challenges (ISBIC, 2022); https://doi.org/10.1109/ISBIC56247.2022.9854534

  53. Yan, L., Cheng, J., Nie, Q. & Sun, X. Dissecting multilayer cell–cell communications with signaling feedback loops from spatial transcriptomics data. Genome Res. 35, 1400–1414 (2025).

    Article  Google Scholar 

  54. Kim, N. et al. Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nat. Commun. 11, 2285 (2020).

    Article  Google Scholar 

  55. Yao, Z. et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell 184, 3222–3241 (2021).

    Article  Google Scholar 

  56. Tepe, B. et al. Single-cell RNA-seq of mouse olfactory bulb reveals cellular heterogeneity and activity-dependent molecular census of adult-born neurons. Cell Rep. 25, 2689–2703 (2018).

    Article  Google Scholar 

  57. He, H. F. PanoSpace: a framework for whole-slide single-cell spatial reconstruction in spatial transcriptomics. Zenodo https://doi.org/10.5281/zenodo.17579559 (2025).

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant nos. 12271198 and 11871026 to X.-F.Z.; 62377019, 12131020, 31930022, T2350003, T2341007, 42450084, 42450135, 42450192, 12326614 and 1202660 to L.C.; and 12426672 to P.P.), the self-determined research funds of CCNU from the colleges’ basic research and operation of MOE (grant nos. CCNU25AI001, CCNU24JC004 and CCNU25JCPT029 to X.-F.Z.), the National Key R&D Program of China (grant nos. 2022YFA1004800 and 2025YFF1207900 to L.C.), the Zhejiang Province Vanguard Goose-Leading Initiative (grant no. 2025C01114 to L.C.), the Science and Technology Commission of Shanghai Municipality (grant no. 23JS1401300 to L.C.) and JST Moonshot R&D (grant no. JPMJMS2021 to L.C.).

Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, and Hubei Key Lab-Math. Sci., Central China Normal University, Wuhan, China

    Hui-Feng He, Shi-Tong Yang, Meng-Guo Wang & Xiao-Fei Zhang

  2. School of Artificial Intelligence, Jianghan University, Wuhan, China

    Pai Peng

  3. Key Laboratory of Nonlinear Analysis and Applications, Ministry of Education, Central China Normal University, Wuhan, China

    Xiao-Fei Zhang

  4. School of Mathematical Sciences and School of AI, Shanghai Jiao Tong University, Shanghai, China

    Luonan Chen

  5. Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China

    Luonan Chen

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  1. Hui-Feng He
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Contributions

This study was conceived and led by X.-F.Z. and L.C. H.-F.H. developed and performed data analyses. H.-F.H. and X.-F.Z. wrote the paper. P.P., S.-T.Y. and M.-G.W. assisted with data analyses and paper writing. X.-F.Z. and L.C. supervised the entire project. All authors read and approved the final version.

Corresponding authors

Correspondence to Xiao-Fei Zhang or Luonan Chen.

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Nature Computational Science thanks Min Wu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.

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Supplementary information

Source data

Source Data Fig. 2 (download XLSX )

Source data underlying Fig. 2b–f, including per-cell spatial expression values and cell-type annotations for Xenium and PanoSpace (Fig. 2b–e), as well as statistical data (Jensen–Shannon divergence values) for cell-type composition and gene-level predictions in breast and lung cancer samples (Fig. 2f). Each sheet corresponds to the dataset used for the respective panel.

Source Data Fig. 3 (download XLSX )

Source data underlying Fig. 3a–f, including per-cell or spot-level values used to generate cell-type composition maps and gene-expression comparisons across computational frameworks (PanoSpace, HisToCell, iStar, TESLA, Tangram, SpatialScope, CytoSPACE and EndoCon). The file also contains data for CAF–cancer epithelial interaction analyses, including ligand–receptor–target network information and importance scores quantifying the contribution of each ligand–receptor pair to downstream targets (Fig. 3d–f). Each sheet corresponds to the dataset used for the respective panel.

Source Data Fig. 4 (download XLSX )

Source data underlying Fig. 4a–f, including per-cell or spot-level values used to generate cell-type composition maps and gene-expression comparisons across computational frameworks (PanoSpace, HisToCell, iStar, TESLA, Tangram, SpatialScope, CytoSPACE and EndoCon). The file also contains data for CAF–cancer epithelial interaction analyses, including ligand–receptor–target network information and importance scores quantifying the contribution of each ligand–receptor pair to downstream targets (Fig. 4d–f). Each sheet corresponds to the dataset used for the respective panel.

Source Data Fig. 5 (download XLSX )

Source data underlying Fig. 5a–e, including per-cell or spot-level values used to generate cell-type distribution maps and gene-expression visualizations across computational frameworks (PanoSpace, Tangram, SpatialScope, CytoSPACE, iStar, TESLA and EnDecon) in mouse brain and olfactory bulb sections. The file also contains statistical data for cell-type and gene-level Jensen–Shannon divergence comparisons among methods (Fig. 5c). Each sheet corresponds to the dataset used for the respective panel.

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He, HF., Peng, P., Yang, ST. et al. Unlocking single-cell level and continuous whole-slide insights in spatial transcriptomics with PanoSpace. Nat Comput Sci (2026). https://doi.org/10.1038/s43588-025-00938-y

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