Bressan, D., Battistoni, G. & Hannon, G. J. The dawn of spatial omics. Science 381, eabq4964 https://doi.org/10.1126/science.abq4964 (2023).
Lewis, S. M. et al. Spatial omics and multiplexed imaging to explore cancer biology. Nat. Methods 18, 997–1012 (2021).
Marx, V. Method of the year: spatially resolved transcriptomics. Nat. Methods 18, 9–14 (2021).
Dezem, F. S., Arjumand, W., DuBose, H., Morosini, N. S. & Plummer, J. Spatially resolved single-cell omics: methods, challenges, and future perspectives. Annu. Rev. Biomed. Data Sci. 7, 131–153 (2024).
Piwecka, M., Rajewsky, N. & Rybak-Wolf, A. Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease. Nat. Rev. Neurol. 19, 346–362 (2023).
Kuppe, C. et al. Spatial multi-omic map of human myocardial infarction. Nature 608, 766–777 (2022).
Kiessling, P. & Kuppe, C. Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases. Genome Med. 16, 14 (2024).
Wu, Y., Cheng, Y., Wang, X., Fan, J. & Gao, Q. Spatial omics: navigating to the golden era of cancer research. Clin. Transl. Med. 12, e696 (2022).
Sun, C. et al. Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer. Nat. Commun. 14, 2692 (2023).
Velten, B. & Stegle, O. Principles and challenges of modeling temporal and spatial omics data. Nat. Methods 20, 1462–1474 (2023).
Greenwald, A. C. et al. Integrative spatial analysis reveals a multi-layered organization of glioblastoma. Cell 187, 2485–2501(2024).
Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 42, 293–304 (2024).
Argelaguet, R. et al. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol. 21, 111 (2020).
Gayoso, A. et al. Joint probabilistic modeling of single-cell multi-omic data with totalVI. Nat. Methods 18, 272–282 (2021).
Xia, C.-R., Cao, Z.-J., Tu, X.-M. & Gao, G. Spatial-linked alignment tool (SLAT) for aligning heterogenous slices. Nat. Commun. 14, 7236 (2023).
Clifton, K. et al. STalign: alignment of spatial transcriptomics data using diffeomorphic metric mapping. Nat. Commun. 14, 8123 (2023).
Wess, M. et al. Spatial integration of multi-omics data from serial sections using the novel Multi-Omics Imaging Integration Toolset. Gigascience 14, giaf035 (2025).
Velten, B. et al. Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO. Nat. Methods 19, 179–186 (2022).
Long, Y. et al. Deciphering spatial domains from spatial multi-omics with SpatialGlue. Nat. Methods 21, 1658–1667 (2024).
Coleman, K. et al. Resolving tissue complexity by multimodal spatial omics modeling with MISO. Nat. Methods 22, 530–538 (2025).
Yan, X. et al. Mosaic integration of spatial multi-omics with SpaMosaic. Nat. Genet. 58, 1126–1137 (2026).
Haviv, D. et al. The covariance environment defines cellular niches for spatial inference. Nat. Biotechnol. 43, 269–280 (2024).
Wan, X. et al. Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope. Nat. Commun. 14, 7848 (2023).
Zhang, M. et al. Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH. Nature 598, 137–143 (2021).
Zhang, M. et al. Molecularly defined and spatially resolved cell atlas of the whole mouse brain. Nature 624, 343–354 (2023).
Song, D. et al. scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics. Nat. Biotechnol. 42, 247–252 (2023).
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
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).
Willmes, D. M. & Birkenfeld, A. L. The role of INDY in metabolic regulation. Comput. Struct. Biotechnol. J. 6, e201303020 (2013).
Sospedra, M. et al. Transcription of a broad range of self-antigens in human thymus suggests a role for central mechanisms in tolerance toward peripheral antigens. J. Immunol. 161, 5918–5929 (1998).
Salou, M. et al. A common transcriptomic program acquired in the thymus defines tissue residency of MAIT and NKT subsets. J. Exp. Med. 216, 133–151 (2019).
Morgan, R. C. et al. TGF-β promotes the postselection thymic development and peripheral function of IFN-γ-producing invariant NKT cells. J. Immunol. 211, 1376–1384 (2023).
Sun, L. et al. Advances in understanding the roles of CD244 (SLAMF4) in immune regulation and associated diseases. Front. Immunol. 12, 648182 (2021).
Chan, S. L. et al. Calcium-modulating cyclophilin ligand is essential for the survival of activated T cells and for adaptive immunity. J. Immunol. 195, 5648–5656 (2015).
He, S. et al. Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs. Nat. Biotechnol. 43, 223–235 (2025).
Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).
Chen, X.-J. et al. Hypoxia-induced ZEB1 promotes cervical cancer immune evasion by strengthening the CD47-SIRPα axis. Cell Commun. Signal. 22, 15 (2024).
Zhou, B. et al. Notch signaling pathway: architecture, disease, and therapeutics. Signal Transduct. Target. Ther. 7, 95 (2022).
Lu, L. et al. Targeting integrin α5 in fibroblasts potentiates colorectal cancer response to PD-L1 blockade by affecting extracellular-matrix deposition. J. Immunother. Cancer. 11, e007447 (2023).
Hu, J. et al. Multi-omic profiling of clear cell renal cell carcinoma identifies metabolic reprogramming associated with disease progression. Nat. Genet. 56, 442–457 (2024).
Tian, R. et al. Integrating cross-sample and cross-modal data for spatial transcriptomics and metabolomics with SpatialMETA. Nat. Commun. 16, 8855 (2025).
Lee, S.-W. et al. Peroxidasin is essential for endothelial cell survival and growth signaling by sulfilimine crosslink-dependent matrix assembly. FASEB J. 34, 10228–10241 (2020).
Krishna, C. et al. Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy. Cancer Cell 39, 662–677 (2021).
Poluektov, Y. O., Kim, A. & Sadegh-Nasseri, S. HLA-DO and its role in MHC class II antigen presentation. Front. Immunol. 4, 260 (2013).
Alnabulsi, A., Swan, R., Cash, B., Alnabulsi, A. & Murray, G. I. The differential expression of omega-3 and omega-6 fatty acid metabolising enzymes in colorectal cancer and its prognostic significance. Br. J. Cancer 116, 1612–1620 (2017).
Gestl, S. A. et al. Expression of UGT2B7, a UDP-glucuronosyltransferase implicated in the metabolism of 4-hydroxyestrone and all-trans retinoic acid, in normal human breast parenchyma and in invasive and in situ breast cancers. Am. J. Pathol. 160, 1467–1479 (2002).
Liu, W., Li, J., Zhao, R., Lu, Y. & Huang, P. The uridine diphosphate (UDP)-glycosyltransferases (UGTs) superfamily: the role in tumor cell metabolism. Front. Oncol. 12, 1088458 (2023).
Bröer, S. Amino acid transport across mammalian intestinal and renal epithelia. Physiol. Rev. 88, 249–286 (2008).
Zhang, D. et al. Spatial epigenome–transcriptome co-profiling of mammalian tissues. Nature 616, 113–122 (2023).
Zu, S. et al. Single-cell analysis of chromatin accessibility in the adult mouse brain. Nature 624, 378–389 (2023).
Hasenpusch-Theil, K. et al. Gli3 controls the onset of cortical neurogenesis by regulating the radial glial cell cycle through Cdk6expression. Development 145, dev163147 (2018).
Llovet, J. M. et al. Hepatocellular carcinoma. Nat. Rev. Dis. Primers 2, 6 (2016).
Finn, R. S. et al. Atezolizumab plus bevacizumab in unresectable hepatocellular carcinoma. N. Engl. J. Med. 382, 1894–1905 (2020).
Mandlik, D. S., Mandlik, S. K. & Choudhary, H. B. et al. Immunotherapy for hepatocellular carcinoma: current status and future perspectives. World J. Gastroenterol. 29, 1054–1075 (2023).
Wu, Z. et al. Spatial multi-omics and deep learning reveal fingerprints of immunotherapy response and resistance in hepatocellular carcinoma. Preprint at bioRxiv https://doi.org/10.1101/2025.06.11.656869 (2025).
Taube, J. M. et al. Colocalization of inflammatory response with B7-H1 expression in human melanocytic lesions supports an adaptive resistance mechanism of immune escape. Sci. Transl. Med. 4, 127ra37https://doi.org/10.1126/scitranslmed.3003689 (2012).
Yi, M. et al. TGF-β: a novel predictor and target for anti-PD-1/PD-L1 therapy. Front. Immunol. 13, 1061394 (2022).
Lemaitre, L. et al. Spatial analysis reveals targetable macrophage-mediated mechanisms of immune evasion in hepatocellular carcinoma minimal residual disease. Nat. Cancer 5, 1534–1556 (2024).
Noy, R. & Pollard, J. W. et al. Tumor-associated macrophages: from mechanisms to therapy. Immunity 41, 49–61 (2014).
Zhu, J. et al. CXCL5/CXCL8 is a promising potential prognostic and tumor microenvironment-related cluster in hepatocellular carcinoma. J. Gastrointest. Oncol.11, 1364–1380 (2020).
Maimela, N. R., Liu, S. & Zhang, Y. Fates of CD8+ T cells in tumor microenvironment. Comput. Struct. Biotechnol.17, 1–13 (2018).
Chen, Y. et al. Tumor-associated macrophages: an accomplice in solid tumor progression. J. Biomed. Sci. 26, 78 (2019).
Kloosterman, D. J. & Akkari, L. Macrophages at the interface of the co-evolving cancer ecosystem. Cell 186, 1627–1651 (2023).
Juneja, V. R. et al. PD-L1 on tumor cells is sufficient for immune evasion in immunogenic tumors and inhibits CD8 T cell cytotoxicity. J. Exp. Med. 214, 895–904 (2017).
Raskov, H., Orhan, A., Christensen, J. P. & Gögenur, I. Cytotoxic CD8+T cells in cancer and cancer immunotherapy. Br. J. Cancer 124, 359–367 (2021).
Yuan, Y. et al. Wnt signaling: modulating tumor-associated macrophages and related immunotherapeutic insights. Biochem. Pharmacol. 223, 116154 (2024).
Ha, H., Debnath, B. & Neamati, N. Role of the CXCL8-CXCR1/2 axis in cancer and inflammatory diseases. Theranostics 7, 1543–1588 (2017).
Lowe, D. G. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004).
Fischler, M. A. & Bolles, R. C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 https://doi.org/10.1145/358669.358692 (1981).
Zhang, K., Zemke, N. R., Armand, E. J. & Ren, B. A fast, scalable and versatile tool for analysis of single-cell omics data. Nat. Methods 21, 217–227 (2024).
Liu, H. et al. Single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain. Nature 624, 366–377 (2023).
Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).
Hu, H. et al. Multiscale footprints reveal the organization of cis-regulatory elements. Nature 638, 779–786 (2025).
Rosenberg, A. & Hirschberg, J. V-measure: a conditional entropy-based external cluster evaluation measure. In Proc. 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (ed Eisner, J.) 410–420 (Association for Computational Linguistics, 2007).
Vinh, N. X., Epps, J. & Bailey, J. Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J. Mach. Learn. Res. 11, 2837–2854 (2010).
Hubert, L. & Arabie, P. Comparing partitions. J. Classif. 2, 193–218 (1985).
Cheng, B., Girshick, R., Dollár, P., Berg, A. C. & Kirillov, A. Boundary IoU: improving object-centric image segmentation evaluation. In Proc. 2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 15334–15342 (IEEE, 2021).
Perazzi, F. et al. A benchmark dataset and evaluation methodology for video object segmentation. In Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 724–732 (IEEE, 2016).
Wu, Z. et al. Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens. Nat. Biomed. Eng. 6, 1435–1448 (2022).
longyahui. JinmiaoChenLab/SpatialGlue: SpatialGlue. Zenodo https://doi.org/10.5281/zenodo.7879713 (2023).
Siyu, H. et al. Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs. figshare https://doi.org/10.6084/m9.figshare.25137320.v1 (2024).
Hu, J. Intergrated multi-omics sequencing reveals metabolic reprograms in the progression of ccRCC [data set]. Zenodo https://doi.org/10.5281/zenodo.8063124 (2023).
Ruonan, T. SpatialMETA: a novel framework for integrating spatial transcriptomics and metabolomics data [data set]. Zenodo https://doi.org/10.5281/zenodo.14986870 (2025).
Siyu, H. et al. CORAL: multi-scale multi-modal integration of spatial omics via deep generative model. figshare https://doi.org/10.6084/m9.figshare.30676556.v4 (2025).
He, S. et al. Multi-scale multi-modal integration of spatial omics via deep generative model with CORAL (version v0.1.0) [computer software]. Zenodo https://doi.org/10.5281/zenodo.22263988 (2026).