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A spatial transcriptome map of the developing maize ear

Nature Plants volume 10, pages 815–827 (2024)Cite this article

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Abstract

A comprehensive understanding of inflorescence development is crucial for crop genetic improvement, as inflorescence meristems give rise to reproductive organs and determine grain yield. However, dissecting inflorescence development at the cellular level has been challenging owing to a lack of specific marker genes to distinguish among cell types, particularly in different types of meristems that are vital for organ formation. In this study, we used spatial enhanced resolution omics-sequencing (Stereo-seq) to construct a precise spatial transcriptome map of the developing maize ear primordium, identifying 12 cell types, including 4 newly defined cell types found mainly in the inflorescence meristem. By extracting the meristem components for detailed clustering, we identified three subtypes of meristem and validated two MADS-box genes that were specifically expressed at the apex of determinate meristems and involved in stem cell determinacy. Furthermore, by integrating single-cell RNA transcriptomes, we identified a series of spatially specific networks and hub genes that may provide new insights into the formation of different tissues. In summary, this study provides a valuable resource for research on cereal inflorescence development, offering new clues for yield improvement.

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Fig. 1: A spatial transcriptomic atlas of the developing maize ear.
Fig. 2: ZmMADS8 and ZmMADS14 identified using Stereo-seq data contribute to stem cell differentiation.
Fig. 3: Developmental trajectories of the 6 mm maize ear.
Fig. 4: Construction of spatial co-expression networks by integrating scRNA-seq and Stereo-seq data.

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Data availability

The scRNA-seq and Stereo-seq data from this study can be found in CNGBdb (https://db.cngb.org/) and under project accession code CNP0004249 (https://db.cngb.org/search/project/CNP0004249/). Additional data, including the processed H5ad data, the original gene expression matrix and expression patterns of marker genes across all sections can be accessed at the STOmicsDB database90, https://db.cngb.org/stomics/mdesta/. Source data are provided with this paper.

Code availability

The code for counting and annotating mapped reads is available via GitHub at https://github.com/BGIResearch/handleBam.

References

  1. Tanaka, W., Pautler, M., Jackson, D. & Hirano, H. Y. Grass meristems II: inflorescence architecture, flower development and meristem fate. Plant Cell Physiol. 54, 313–324 (2013).

    Article  CAS  PubMed  Google Scholar 

  2. Benlloch, R., Berbel, A., Serrano-Mislata, A. & Madueño, F. Floral initiation and inflorescence architecture: a comparative view. Ann. Bot. 100, 659–676 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Irish, E. Class II tassel seed mutations provide evidence for multiple types of inflorescence meristems in maize (Poaceae). Am. J. Bot. 84, 1502 (1997).

    Article  CAS  PubMed  Google Scholar 

  4. Jackson, D., Veit, B. & Hake, S. Expression of maize KNOTTED1 related homeobox genes in the shoot apical meristem predicts patterns of morphogenesis in the vegetative shoot. Development 120, 405–413 (1994).

    Article  CAS  Google Scholar 

  5. Gallavotti, A. et al. The role of barren stalk1 in the architecture of maize. Nature 432, 630–635 (2004).

    Article  CAS  PubMed  Google Scholar 

  6. Vollbrecht, E., Springer, P. S., Goh, L., Buckler, E. S. T. & Martienssen, R. Architecture of floral branch systems in maize and related grasses. Nature 436, 1119–1126 (2005).

    Article  CAS  PubMed  Google Scholar 

  7. Satoh-Nagasawa, N., Nagasawa, N., Malcomber, S., Sakai, H. & Jackson, D. A trehalose metabolic enzyme controls inflorescence architecture in maize. Nature 441, 227–230 (2006).

    Article  CAS  PubMed  Google Scholar 

  8. Chuck, G., Muszynski, M., Kellogg, E., Hake, S. & Schmidt, R. J. The control of spikelet meristem identity by the branched silkless1 gene in maize. Science 298, 1238–1241 (2002).

    Article  CAS  PubMed  Google Scholar 

  9. Ning, Q. et al. An ethylene biosynthesis enzyme controls quantitative variation in maize ear length and kernel yield. Nat. Commun. 12, 5832 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Luo, Y. et al. Genetic variation in YIGE1 contributes to ear length and grain yield in maize. N. Phytol. 234, 513–526 (2022).

    Article  CAS  Google Scholar 

  11. Lloyd, J. & Meinke, D. A comprehensive dataset of genes with a loss-of-function mutant phenotype in Arabidopsis. Plant Physiol. 158, 1115–1129 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Goldman, S. L. et al. The impact of heterogeneity on single-cell sequencing. Front. Genet. 10, 8 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhang, T. Q., Chen, Y., Liu, Y., Lin, W. H. & Wang, J. W. Single-cell transcriptome atlas and chromatin accessibility landscape reveal differentiation trajectories in the rice root. Nat. Commun. 12, 2053 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhang, T. Q., Chen, Y. & Wang, J. W. A single-cell analysis of the Arabidopsis vegetative shoot apex. Dev. Cell 56, 1056–1074.e8 (2021).

    Article  CAS  PubMed  Google Scholar 

  15. Satterlee, J. W., Strable, J. & Scanlon, M. J. Plant stem-cell organization and differentiation at single-cell resolution. Proc. Natl Acad. Sci. USA 117, 33689–33699 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Xu, X. et al. Single-cell RNA sequencing of developing maize ears facilitates functional analysis and trait candidate gene discovery. Dev. Cell 56, 557–568.e6 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Ortiz-Ramírez, C. et al. Ground tissue circuitry regulates organ complexity in maize and Setaria. Science 374, 1247–1252 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792.e21 (2022).

    Article  CAS  PubMed  Google Scholar 

  19. Wei, X. et al. Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration. Science 377, eabp9444 (2022).

    Article  CAS  PubMed  Google Scholar 

  20. Liu, C. et al. Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis. Dev. Cell 57, 1284–1298.e5 (2022).

    Article  CAS  PubMed  Google Scholar 

  21. Xia, K. et al. The single-cell stereo-seq reveals region-specific cell subtypes and transcriptome profiling in Arabidopsis leaves. Dev. Cell 57, 1299–1310.e4 (2022).

    Article  CAS  PubMed  Google Scholar 

  22. Liu, Y. et al. Spatial transcriptome analysis on peanut tissues shed light on cell heterogeneity of the peg. Plant Biotechnol. J. 20, 1648–1650 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  23. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Liu, Z. et al. Integrated single-nucleus and spatial transcriptomics captures transitional states in soybean nodule maturation. Nat. Plants 9, 515–524 (2023).

    Article  CAS  PubMed  Google Scholar 

  25. Vollbrecht, E. & Schmidt, R.J. in Handbook of Maize: Its Biology (eds Bennetzen, J. L. & Hake, S. C.) 13–40 (Springer New York, 2009).

  26. Ambrose, B. A. et al. Molecular and genetic analyses of the silky1 gene reveal conservation in floral organ specification between eudicots and monocots. Mol. Cell 5, 569–579 (2000).

    Article  CAS  PubMed  Google Scholar 

  27. Bommert, P. et al. thick tassel dwarf1 encodes a putative maize ortholog of the Arabidopsis CLAVATA1 leucine-rich repeat receptor-like kinase. Development 132, 1235–1245 (2005).

    Article  CAS  PubMed  Google Scholar 

  28. Bortiri, E. et al. ramosa2 encodes a LATERAL ORGAN BOUNDARY domain protein that determines the fate of stem cells in branch meristems of maize. Plant Cell 18, 574–585 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Chen, Z. et al. Structural variation at the maize WUSCHEL1 locus alters stem cell organization in inflorescences. Nat. Commun. 12, 2378 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Chuck, G., Meeley, R. & Hake, S. Floral meristem initiation and meristem cell fate are regulated by the maize AP2 genes ids1 and sid1. Development 135, 3013–3019 (2008).

    Article  CAS  PubMed  Google Scholar 

  31. Chuck, G., Meeley, R. B. & Hake, S. The control of maize spikelet meristem fate by the APETALA2-like gene indeterminate spikelet1. Genes Dev. 12, 1145–1154 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Chuck, G. S., Brown, P. J., Meeley, R. & Hake, S. Maize SBP-box transcription factors unbranched2 and unbranched3 affect yield traits by regulating the rate of lateral primordia initiation. Proc. Natl Acad. Sci. USA 111, 18775–18780 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Gallavotti, A. et al. The control of axillary meristem fate in the maize ramosa pathway. Development 137, 2849–2856 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Galli, M. et al. Auxin signaling modules regulate maize inflorescence architecture. Proc. Natl Acad. Sci. USA 112, 13372–13377 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kerstetter, R. A., Laudencia-Chingcuanco, D., Smith, L. G. & Hake, S. Loss-of-function mutations in the maize homeobox gene, knotted1, are defective in shoot meristem maintenance. Development 124, 3045–3054 (1997).

    Article  CAS  PubMed  Google Scholar 

  36. Liu, R. et al. Fine mapping and candidate gene prediction of a pleiotropic quantitative trait locus for yield-related trait in Zea mays. PLoS ONE 7, e49836 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Pautler, M. et al. FASCIATED EAR4 encodes a bZIP transcription factor that regulates shoot meristem size in maize. Plant Cell 27, 104–120 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Skirpan, A., Wu, X. & McSteen, P. Genetic and physical interaction suggest that BARREN STALK 1 is a target of BARREN INFLORESCENCE2 in maize inflorescence development. Plant J. 55, 787–797 (2008).

    Article  CAS  PubMed  Google Scholar 

  39. Strable, J. & Vollbrecht, E. Maize YABBY genes drooping leaf1 and drooping leaf2 regulate floret development and floral meristem determinacy. Development 146, dev171181 (2019).

    Article  CAS  PubMed  Google Scholar 

  40. Strable, J. et al. Maize YABBY genes drooping leaf1 and drooping leaf2 regulate plant architecture. Plant Cell 29, 1622–1641 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Xu, G. et al. Complex genetic architecture underlies maize tassel domestication. N. Phytol. 214, 852–864 (2017).

    Article  CAS  Google Scholar 

  42. Zhang, D. et al. GRF-interacting factor1 regulates shoot architecture and meristem determinacy in maize. Plant Cell 30, 360–374 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Whipple, C. J. et al. A conserved mechanism of bract suppression in the grass family. Plant Cell 22, 565–578 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Chuck, G., Whipple, C., Jackson, D. & Hake, S. The maize SBP-box transcription factor encoded by tasselsheath4 regulates bract development and the establishment of meristem boundaries. Development 137, 1243–1250 (2010).

    Article  CAS  PubMed  Google Scholar 

  45. Du, Y. et al. UNBRANCHED3 expression and inflorescence development is mediated by UNBRANCHED2 and the distal enhancer, KRN4, in maize. PLoS Genet. 16, e1008764 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Jia, H. et al. A serine/threonine protein kinase encoding gene KERNEL NUMBER PER ROW6 regulates maize grain yield. Nat. Commun. 11, 988 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Xiao, Y. et al. Boundary domain genes were recruited to suppress bract growth and promote branching in maize. Sci. Adv. 8, eabm6835 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Chuck, G. & Bortiri, E. The unique relationship between tsh4 and ra2 in patterning floral phytomers. Plant Signal. Behav. 5, 979–981 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Bartlett, M. E. et al. The maize PI/GLO ortholog Zmm16/sterile tassel silky ear1 interacts with the zygomorphy and sex determination pathways in flower development. Plant Cell 27, 3081–3098 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kim, D. E. et al. The impact of fasciation on maize inflorescence architecture. J. Plant Biol. 65, 87–98 (2022).

    Article  CAS  Google Scholar 

  52. Du, Y., Wu, B., Xing, Y. & Zhang, Z. Conservation and divergence: regulatory networks underlying reproductive branching in rice and maize. J. Adv. Res. 41, 179–190 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 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  CAS  PubMed  PubMed Central  Google Scholar 

  56. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9, 559 (2008).

    Article  Google Scholar 

  57. Douglas, C. J. Phenylpropanoid metabolism and lignin biosynthesis: from weeds to trees. Trends Plant Sci. 1, 171–178 (1996).

    Article  Google Scholar 

  58. Saha, P. et al. Phenylpropanoid biosynthesis gene expression precedes lignin accumulation during shoot development in lowland and upland switchgrass genotypes. Front. Plant Sci. 12, 640930 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Suh, M. C. et al. Cuticular lipid composition, surface structure, and gene expression in Arabidopsis stem epidermis. Plant Physiol. 139, 1649–1665 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Luo, B., Xue, X. Y., Hu, W. L., Wang, L. J. & Chen, X. Y. An ABC transporter gene of Arabidopsis thaliana, AtWBC11, is involved in cuticle development and prevention of organ fusion. Plant Cell Physiol. 48, 1790–1802 (2007).

    Article  CAS  PubMed  Google Scholar 

  62. Panikashvili, D. et al. The Arabidopsis DESPERADO/AtWBC11 transporter is required for cutin and wax secretion. Plant Physiol. 145, 1345–1360 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Javelle, M. et al. Overexpression of the epidermis-specific homeodomain-leucine zipper IV transcription factor OUTER CELL LAYER1 in maize identifies target genes involved in lipid metabolism and cuticle biosynthesis. Plant Physiol. 154, 273–286 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Todd, J., Post-Beittenmiller, D. & Jaworski, J. G. KCS1 encodes a fatty acid elongase 3-ketoacyl-CoA synthase affecting wax biosynthesis in Arabidopsis thaliana. Plant J. 17, 119–130 (1999).

    Article  CAS  PubMed  Google Scholar 

  65. Sturaro, M. et al. Cloning and characterization of GLOSSY1, a maize gene involved in cuticle membrane and wax production. Plant Physiol. 138, 478–489 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Wang, J. et al. Lignin engineering through laccase modification: a promising field for energy plant improvement. Biotechnol. Biofuels 8, 145 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Zhao, Q. et al. Laccase is necessary and nonredundant with peroxidase for lignin polymerization during vascular development in Arabidopsis. Plant Cell 25, 3976–3987 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Nuccio, M. L. et al. Expression of trehalose-6-phosphate phosphatase in maize ears improves yield in well-watered and drought conditions. Nat. Biotechnol. 33, 862–869 (2015).

    Article  CAS  PubMed  Google Scholar 

  69. Kataya, A. R. A. et al. Multi-targeted trehalose-6-phosphate phosphatase I harbors a novel peroxisomal targeting signal 1 and is essential for flowering and development. Planta 251, 98 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Li, Z. et al. The OsNAC23–Tre6P–SnRK1a feed-forward loop regulates sugar homeostasis and grain yield in rice. Mol. Plant 15, 706–722 (2022).

    Article  CAS  PubMed  Google Scholar 

  71. Yang, X. et al. Characterization of a global germplasm collection and its potential utilization for analysis of complex quantitative traits in maize. Mol. Breed. 28, 511–526 (2011).

    Article  Google Scholar 

  72. Yang, N. et al. Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel. PLoS Genet. 10, e1004573 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Gui, S. et al. A pan-Zea genome map for enhancing maize improvement. Genome Biol. 23, 178 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Li, M., Zhong, W., Yang, F. & Zhang, Z. Genetic and molecular mechanisms of quantitative trait loci controlling maize inflorescence architecture. Plant Cell Physiol. 59, 448–457 (2018).

    Article  CAS  PubMed  Google Scholar 

  75. Liu, L. et al. Enhancing grain-yield-related traits by CRISPR–Cas9 promoter editing of maize CLE genes. Nat. Plants 7, 287–294 (2021).

    Article  CAS  PubMed  Google Scholar 

  76. Wu, Q., Xu, F. & Jackson, D. All together now, a magical mystery tour of the maize shoot meristem. Curr. Opin. Plant Biol. 45, 26–35 (2018).

    Article  CAS  PubMed  Google Scholar 

  77. Claeys, H. et al. Control of meristem determinacy by trehalose 6-phosphate phosphatases is uncoupled from enzymatic activity. Nat. Plants 5, 352–357 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Hufford, M. B. et al. De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes. Science 373, 655–662 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  80. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Crow, M., Paul, A., Ballouz, S., Huang, Z. J. & Gillis, J. Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nat. Commun. 9, 884 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  83. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).

    Article  CAS  PubMed  Google Scholar 

  86. Tu, X. et al. Reconstructing the maize leaf regulatory network using ChIP-seq data of 104 transcription factors. Nat. Commun. 11, 5089 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Yoo, S. D., Cho, Y. H. & Sheen, J. Arabidopsis mesophyll protoplasts: a versatile cell system for transient gene expression analysis. Nat. Protoc. 2, 1565–1572 (2007).

    Article  CAS  PubMed  Google Scholar 

  88. McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337.e4 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Speed, D., Hemani, G., Johnson, M. R. & Balding, D. J. Improved heritability estimation from genome-wide SNPs. Am. J. Hum. Genet. 91, 1011–1021 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Xu, Z. et al. STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization. Nucleic Acids Res. 5, D1053–D1061 (2023).

    Google Scholar 

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Acknowledgements

This research was supported by funding from the National Key R&D Program of China (grant no. 2023ZD04073) to N.Y. and H.L., the National Natural Science Foundation of China (grant nos 32222062 and 32321005) and the National Key Laboratory of Crop Genetic Improvement Self-Research Program (grant no. ZW22B0102) to N.Y., the Outstanding Youth Team Cultivation Project of Center Universities (grant no. 2662023PY007) to L. Liu, the Shenzhen Science and Technology Program (grant no. KQTD20230301092839007) to H.L., the China Postdoctoral Science Foundation (grant no. 2023M731239) to Y. Luo, the National Key Research and Development Program of China (grant nos 2022YFD1201500 and 2020YFE0202300) to Jianbing Yan, and NSF-IOS grant no. 1934388 to D.J. Computation resources were provided by the high-throughput computing platform of the National Key Laboratory of Crop Genetic Improvement at Huazhong Agricultural University and supported by H.L. The construction of the spatial transcriptome visualization website is supported by the Guangdong Genomics Data Center (grant no. 2021B1212100001).

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Author notes

  1. These authors contributed equally: Yuebin Wang, Yun Luo, Xing Guo, Yunfu Li, Jiali Yan.

Authors and Affiliations

  1. National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China

    Yuebin Wang, Yun Luo, Yunfu Li, Jiali Yan, Wenjie Wei, Qian Ding, Minji Bai, Lin Zhuo, David Jackson, Zuxin Zhang, Jianbing Yan, Lei Liu & Ning Yang

  2. State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China

    Xing Guo, Wenwen Shao, Xiaofeng Wei, Tao Yang, Jing Chen, Lihua Chen, Li Li, Xun Xu & Huan Liu

  3. BGI Research, Wuhan, China

    Xing Guo & Wenwen Shao

  4. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China

    Wenwen Shao

  5. China National GeneBank, Shenzhen, China

    Xiaofeng Wei, Tao Yang & Jing Chen

  6. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    David Jackson

  7. Hubei Hongshan Laboratory, Wuhan, China

    Zuxin Zhang, Jianbing Yan, Lei Liu & Ning Yang

  8. Guangdong Laboratory of Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Shenzhen, China

    Huan Liu

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  1. Yuebin Wang
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Contributions

N.Y., L. Liu and H.L. conceived and supervised this study. Y.W., Y. Luo, X.G., Jiali Yan, W.S., L.C., Q.D., L. Li and L.Z. performed the library preparation and sequencing. Y.W., X.G., W.S., M.B. and W.W. performed the bioinformatics analysis. X.W., T.Y. and J.C. established the online database. Y. Li finished the transgenic and in situ experiments. Y.W., Y. Luo, X.G., Jiali Yan, Y. Li, D.J., Z.Z., X.X., Jianbing Yan, N.Y., L. Liu and H.L. discussed the data and prepared the manuscript. All authors read and approved the manuscript.

Corresponding authors

Correspondence to Huan Liu, Lei Liu or Ning Yang.

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The authors declare no competing interests.

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Nature Plants thanks Clinton Whipple, Jixian Zhai and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–30.

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Supplementary Tables

Supplementary Tables 1–10.

Supplementary Data 1

mRNA in situ hybridization of marker genes identified in Stereo-seq. Left, mRNA in situ hybridization results. Right, Stereo-seq expression pattern; the arrow indicates the gene expression region that matches the in situ results. Scale bar, 0.1 mm.

Supplementary Data 2

Statistical data for Supplementary Figs. 2, 3, 6, 9, 10, 17, 24–26, 29 and 30.

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Wang, Y., Luo, Y., Guo, X. et al. A spatial transcriptome map of the developing maize ear. Nat. Plants 10, 815–827 (2024). https://doi.org/10.1038/s41477-024-01683-2

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