Abstract
Wheat is a major staple crop for over one-third of the world’s population, crucial for global food security, economic stability and cultural traditions. Recently, single-cell and spatial omics approaches have transformed biological discovery, primarily in medical and animal sciences, and they are now beginning to be applied in plant research. Here we summarize the technical innovations and feasibility of spatial omics applications in wheat research, particularly for understanding developmental and environmental responses, thereby potentially enhancing wheat breeding. We highlight how these tools can reveal spatial and temporal patterns in gene expression, cellular heterogeneity and tissue organization in wheat. Furthermore, we propose developing a spatially resolved single-cell atlas of wheat across its life cycle to facilitate breakthroughs in basic research and potential applications in breeding. To achieve these goals, we advocate for a Wheat Spatial Omics Consortium to foster worldwide collaboration for overcoming barriers and developing sustainable and climate-resilient wheat.
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References
Liu, L. et al. Spatiotemporal omics for biology and medicine. Cell 187, 4488–4519 (2024).
Guo, T., Steen, J. A. & Mann, M. Mass-spectrometry-based proteomics: from single cells to clinical applications. Nature 638, 901–911 (2025).
Rao, A., Barkley, D., França, G. S. & Yanai, I. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211–220 (2021).
Zeng, H. et al. Spatially resolved single-cell translatomics at molecular resolution. Science 380, eadd3067 (2023).
Vandereyken, K., Sifrim, A., Thienpont, B. & Voet, T. Methods and applications for single-cell and spatial multi-omics. Nat. Rev. Genet. 24, 494–515 (2023).
Hazard, B. et al. Strategies to improve wheat for human health. Nat. Food 1, 475–480 (2020).
Long, S. P., Marshall-Colon, A. & Zhu, X.-G. Meeting the global food demand of the future by engineering crop photosynthesis and yield potential. Cell 161, 56–66 (2015).
Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA 114, 9326–9331 (2017).
Long, K. A. et al. Spatial transcriptomics reveals expression gradients in developing wheat inflorescences at cellular resolution. Plant Cell 38, koaf282 (2026).
Li, X. et al. Spatiotemporal transcriptomics reveals key gene regulation for grain yield and quality in wheat. Genome Biol. 26, 93 (2025).
Millsteed, T. et al. Spatial transcriptomics of developing wheat seed reveals concentric gene expression zones and subgenome biased expression of key genes. Plant Biotechnol. J. 23, 5934–5949 (2025).
Ke, Y. et al. A single-cell and spatial wheat root atlas with cross-species annotations delineates conserved tissue-specific marker genes and regulators. Cell Rep. 44, 115240 (2025).
Wei, W. Q., Li, S., Zhang, D. & Tang, W. H. Single-cell transcriptomic analysis highlights specific cell types manipulated by Fusarium head blight fungus leading to wheat susceptibility. Dev. Cell 60, 3496–3513 (2025).
Guo, X. et al. An Arabidopsis single-nucleus atlas decodes leaf senescence and nutrient allocation. Cell 188, 2856–2871 (2025).
Zhang, X. et al. A spatially resolved multi-omic single-cell atlas of soybean development. Cell 188, 550–567 (2025).
Fan, J. et al. A large-scale integrated transcriptomic atlas for soybean organ development. Mol. Plant 18, 669–689 (2025).
Yao, J. et al. Spatiotemporal transcriptomic landscape of rice embryonic cells during seed germination. Dev. Cell 59, 2320–2332 (2024).
Peirats-Llobet, M. et al. Spatially resolved transcriptomic analysis of the germinating barley grain. Nucleic Acids Res. 51, 7798–7819 (2023).
Fu, Y. et al. Spatial transcriptomics uncover sucrose post-phloem transport during maize kernel development. Nat. Commun. 14, 7191 (2023).
Dong, Z. et al. Developmental innovation of inferior ovaries and flower sex orchestrated by KNOX1 in cucurbits. Nat. Plants 11, 861–877 (2025).
Wang, Y. et al. A spatial transcriptome map of the developing maize ear. Nat. Plants 10, 815–827 (2024).
Wang, W. et al. Single-cell and spatial transcriptomics reveals a stereoscopic response of rice leaf cells to Magnaporthe oryzae infection. Adv. Sci. (Weinh.) 12, e2416846 (2025).
Zhu, M. et al. Single-cell transcriptomics reveal how root tissues adapt to soil stress. Nature 642, 721–729 (2025).
Serrano, K. et al. Spatial co-transcriptomics reveals discrete stages of the arbuscular mycorrhizal symbiosis. Nat. Plants 10, 673–688 (2024).
Moreno-Villena, J. J. et al. Spatial resolution of an integrated C4+CAM photosynthetic metabolism. Sci. Adv. 8, eabn2349 (2022).
Song, X. et al. Spatial transcriptomics reveals light-induced chlorenchyma cells involved in promoting shoot regeneration in tomato callus. Proc. Natl Acad. Sci. USA 120, e2310163120 (2023).
Ge, X. et al. Spatiotemporal transcriptome and metabolome landscapes of cotton somatic embryos. Nat. Commun. 16, 859 (2025).
Yao, Y. et al. Wheat2035: integrating pan-omics and advanced biotechnology for future wheat design. Mol. Plant 18, 272–297 (2025).
Van De Velde, K. et al. N-terminal truncated RHT-1 proteins generated by translational reinitiation cause semi-dwarfing of wheat Green Revolution alleles. Mol. Plant 14, 679–687 (2021).
Chen, Z. et al. A single nucleotide deletion in the third exon of FT-D1 increases the spikelet number and delays heading date in wheat (Triticum aestivum L.). Plant Biotechnol. J. 20, 920–933 (2022).
Lin, X. et al. Systematic identification of wheat spike developmental regulators by integrated multi-omics, transcriptional network, GWAS, and genetic analyses. Mol. Plant 17, 438–459 (2024).
Qu Y. et al. Spatial transcriptomics identifies distinct domains regulating yield-related traits of the wheat ear. Preprint at bioRxiv https://doi.org/10.1101/2025.08.12.670006 (2025).
Gong, Z. et al. Plant abiotic stress response and nutrient use efficiency. Sci. China Life Sci. 63, 635–674 (2020).
Mao, H. et al. Wheat adaptation to environmental stresses under climate change: molecular basis and genetic improvement. Mol. Plant 16, 1564–1589 (2023).
Zhang, H., Zhu, J., Gong, Z. & Zhu, J.-K. Abiotic stress responses in plants. Nat. Rev. Genet. 23, 104–119 (2022).
Wang, J. et al. Spatial transcriptomics uncover coordinated cellular responses to heat stress in developing wheat grains. Preprint at Res. Sq. https://doi.org/10.21203/rs.3.rs-4253930/v1 (2024).
Singh, R. P. et al. Challenges to wheat disease resistance and current global strategies. Annu. Rev. Phytopathol. 63, 201–224 (2025).
Tong, J. et al. Genome-wide atlas of rust resistance loci in wheat. Theor. Appl. Genet. 137, 179 (2024).
Singh, R., Huerta-Espino, J. & Rajaram, S. Achieving near-immunity to leaf and stripe rusts in wheat by combining slow rusting resistance genes. Acta Phytopathol. Entomol. Hung. 35, 133–139 (2000).
Ding, M., Zhu, Y. & Kinoshita, T. Stomatal properties of Arabidopsis cauline and rice flag leaves and their contributions to seed production and grain yield. J. Exp. Bot. 74, 1957–1973 (2023).
Smith, E. N. et al. Improving photosynthetic efficiency toward food security: strategies, advances, and perspectives. Mol. Plant 16, 1547–1563 (2023).
Li, C., Du, X. & Liu, C. Enhancing crop yields to ensure food security by optimizing photosynthesis. J. Genet. Genomics 52, 1082–1095 (2025).
Lynch, J. P. Steep, cheap and deep: an ideotype to optimize water and N acquisition by maize root systems. Ann. Bot. 112, 347–357 (2013).
Nirmalaruban, R. et al. Root traits: a key for breeding climate-smart wheat (Triticum aestivum). Plant Breed. 144, 310–334 (2025).
Chen, S., Liu, F., Wu, W., Jiang, Y. & Zhan, K. A SNP-based GWAS and functional haplotype-based GWAS of flag leaf-related traits and their influence on the yield of bread wheat (Triticum aestivum L.). Theor. Appl. Genet. 134, 3895–3909 (2021).
Cavalet-Giorsa, E. et al. Origin and evolution of the bread wheat D genome. Nature 633, 848–855 (2024).
Liu, S. et al. A telomere-to-telomere genome assembly coupled with multi-omic data provides insights into the evolution of hexaploid bread wheat. Nat. Genet. 57, 1008–1020 (2025).
Niu, J. et al. Whole-genome sequencing of diverse wheat accessions uncovers genetic changes during modern breeding in China and the United States. Plant Cell 35, 4199–4216 (2023).
Schulthess, A. W. et al. Genomics-informed prebreeding unlocks the diversity in genebanks for wheat improvement. Nat. Genet. 54, 1544–1552 (2022).
Cheng, S. et al. Harnessing landrace diversity empowers wheat breeding. Nature 632, 823–831 (2024).
Zhou, Y. et al. Triticum population sequencing provides insights into wheat adaptation. Nat. Genet. 52, 1412–1422 (2020).
Tiwari, V. K., Saripalli, G., Sharma, P. K. & Poland, J. Wheat genomics: genomes, pangenomes, and beyond. Trends Genet. 40, 982–992 (2024).
Pang, Y. et al. High-resolution genome-wide association study identifies genomic regions and candidate genes for important agronomic traits in wheat. Mol. Plant 13, 1311–1327 (2020).
Song, L., Chen, W., Hou, J., Guo, M. & Yang, J. Spatially resolved mapping of cells associated with human complex traits. Nature 641, 932–941 (2025).
Shen, K. et al. Dissection of genomic drivers of spike morphology changes in wheat by high-throughput phenotyping. Cell Rep. 44, 116120 (2025).
Zhang, Z. et al. Integrating high-throughput phenotyping and genome-wide association studies for enhanced drought resistance and yield prediction in wheat. New Phytol. 243, 1758–1775 (2024).
Zhang, L. et al. Asymmetric gene expression and cell-type-specific regulatory networks in the root of bread wheat revealed by single-cell multiomics analysis. Genome Biol. 24, 65 (2023).
Zhang, D. et al. Spatial epigenome–transcriptome co-profiling of mammalian tissues. Nature 616, 113–122 (2023).
Du, Z., Zhang, B., Weng, H. & Gao, L. Single-cell RNA sequencing reveals the developmental landscape of wheat roots. Plant Cell Environ. 48, 3431–3447 (2025).
Wang, X. et al. A single-cell multi-omics atlas of rice. Nature 644, 722–730 (2025).
Xue, H. C. et al. A unified cell atlas of vascular plants reveals cell-type foundational genes and accelerates gene discovery. Cell 188, 6370–6390 (2025).
Li, B. et al. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nat. Methods 19, 662–670 (2022).
Bartosovic, M., Kabbe, M. & Castelo-Branco, G. Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nat. Biotechnol. 39, 825–835 (2021).
Deng, Y. et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375–383 (2022).
Deng, Y. et al. Spatial-CUT&Tag: spatially resolved chromatin modification profiling at the cellular level. Science 375, 681–686 (2022).
Vickovic, S. et al. SM-Omics is an automated platform for high-throughput spatial multi-omics. Nat. Commun. 13, 795 (2022).
Ben-Chetrit, N. et al. Integration of whole transcriptome spatial profiling with protein markers. Nat. Biotechnol. 41, 788–793 (2023).
Ren, P. et al. Systematic benchmarking of high-throughput subcellular spatial transcriptomics platforms across human tumors. Nat. Commun. 16, 9232 (2025).
Zhang, S. et al. Spatial distribution of proteins and metabolites in developing wheat grain and their differential regulatory response during the grain filling process. Plant J. 107, 669–687 (2021).
Barmukh, R. et al. Spatial omics for accelerating plant research and crop improvement. Trends Biotechnol. 43, 1904–1920 (2025).
Nobori, T. Exploring the untapped potential of single-cell and spatial omics in plant biology. New Phytol. 247, 1098–1116 (2025).
Zhou, X. et al. CRISPR-mediated acceleration of wheat improvement: advances and perspectives. J. Genet. Genomics 50, 815–834 (2023).
Sun, C. et al. Iterative recombinase technologies for efficient and precise genome engineering across kilobase to megabase scales. Cell 188, 4693–4710 (2025).
Peng, J. et al. ‘Green revolution’ genes encode mutant gibberellin response modulators. Nature 400, 256–261 (1999).
Boden, S. A. et al. Ppd-1 is a key regulator of inflorescence architecture and paired spikelet development in wheat. Nat. Plants 1, 14016 (2015).
Zhou, H. et al. Insights into plant salt stress signaling and tolerance. J. Genet. Genomics 51, 16–34 (2024).
He, Z., Webster, S. & He, S. Y. Growth-defense trade-offs in plants. Curr. Biol. 32, R634–R639 (2022).
Liu, Q. et al. Improving crop nitrogen use efficiency toward sustainable green revolution. Annu. Rev. Plant Biol. 73, 523–551 (2022).
Lin, L. et al. STMGraph: spatial-context-aware of transcriptomes via a dual-remasked dynamic graph attention model. Brief. Bioinform. 26, bbae685 (2025).
Ramírez-González, R. H. et al. The transcriptional landscape of polyploid wheat. Science 361, eaar6089 (2018).
Brůna, T., Sreedasyam, A., Harder, A.M. & Lovell, J.T. Evolutionary and methodological considerations when interpreting gene presence-absence variation in pangenomes. NAR Genom.Bioinform. 8, lqag011 (2026).
White, B. et al. De novo annotation reveals transcriptomic complexity across the hexaploid wheat pan-genome. Nat. Commun. 16, 8538 (2025).
Liu, Y. et al. Spatial-CITE-seq: spatially resolved high-plex protein and whole transcriptome co-mapping. Preprint at Res. Sq. https://doi.org/10.21203/rs.3.rs-1499315/v1 (2022).
Liao, S. et al. Integrated spatial transcriptomic and proteomic analysis of fresh frozen tissue based on stereo-seq. Preprint at bioRxiv https://doi.org/10.1101/2023.04.28.538364 (2023).
Guo, H. et al. Identification and expression analysis of heat-shock proteins in wheat infected with powdery mildew and stripe rust. Plant Genome 14, e20092 (2021).
Zhang, Q. et al. Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry. Nat. Commun. 14, 4050 (2023).
Hu, J. et al. SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat. Methods 18, 1342–1351 (2021).
Li, Z. et al. Integrative deep learning of spatial multi-omics with SWITCH. Nat. Comput. Sci. 5, 1051–1063 (2025).
Qiu, X. et al. Spatiotemporal modeling of molecular holograms. Cell 187, 7351–7373 (2024).
Wang, H. et al. SpatialAgent: an autonomous AI agent for spatial biology. Preprint at bioRxiv https://doi.org/10.1101/2025.04.03.646459 (2025).
Reynolds, M. P. et al. A wiring diagram to integrate physiological traits of wheat yield potential. Nat. Food 3, 318–324 (2022).
Bressan, D., Battistoni, G. & Hannon, G. J. The dawn of spatial omics. Science 381, eabq4964 (2023).
Lu, T., Ang, C. E. & Zhuang, X. Spatially resolved epigenomic profiling of single cells in complex tissues. Cell 185, 4448–4464 (2022).
Lundberg, E. & Borner, G. H. H. Spatial proteomics: a powerful discovery tool for cell biology. Nat. Rev. Mol. Cell Biol. 20, 285–302 (2019).
Alexandrov, T. Spatial metabolomics: from a niche field towards a driver of innovation. Nat. Metab. 5, 1443–1445 (2023).
Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792 (2022).
Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 9, 534–546 (2022).
Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
You, Y. et al. Systematic comparison of sequencing-based spatial transcriptomic methods. Nat. Methods 21, 1743–1754 (2024).
Wei, X. et al. Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration. Science 377, eabp9444 (2022).
Zhong, L. et al. Comparative spatial transcriptomics reveals root dryland adaptation mechanism in rice and HMGB1 as a key regulator. Mol. Plant 18, 797–819 (2025).
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
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).
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
Acknowledgements
This work was supported by the Key Research and Development Program of Zhejiang (grant 2024SSYS0099), the Australian Research Council (grants FT210100366, CE230100015 and FT21010810), the Grain Research and Development Corporation (grants WSU2303-001RTX and UMU2404-003RTX), the WA Agricultural Research Collaboration (Wheat NUE Project), the Biological Breeding National Science and Technology Major Project (grant 2023ZD04073) and the National Key R&D Program of China (grant 2022YFC3400400). The authors thank the Plant SpatioTemporal Omics Consortium (STOC Plant) for its support.
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Z.H.C., X.X., S.C.X., R.K.V. and S.A.B. conceived the project. X.Y.T., C.T. and Z.H.C. prepared the manuscript draft. X.Y.T., C.T., S.A.B., R.K.V., S.C.X., X.X., K.H.M.S., X.F., A.C., Y.W., A.R., S.S. and Z.H.C. wrote the paper with the contribution from all authors. Z.H.C., X.X., S.C.X., R.K.V. and S.A.B. finalized the manuscript, and all authors have read and approved the manuscript.
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Tao, XY., Tan, C., Liu, Y. et al. The potential of wheat spatial omics. Nat Genet 58, 962–973 (2026). https://doi.org/10.1038/s41588-026-02542-w
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DOI: https://doi.org/10.1038/s41588-026-02542-w
