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Integrated single-nucleus and spatial transcriptomics captures transitional states in soybean nodule maturation

Nature Plants volume 9, pages 515–524 (2023)Cite this article

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Abstract

Legumes form symbiosis with rhizobium leading to the development of nitrogen-fixing nodules. By integrating single-nucleus and spatial transcriptomics, we established a cell atlas of soybean nodules and roots. In central infected zones of nodules, we found that uninfected cells specialize into functionally distinct subgroups during nodule development, and revealed a transitional subtype of infected cells with enriched nodulation-related genes. Overall, our results provide a single-cell perspective for understanding rhizobium–legume symbiosis.

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Fig. 1: Combined single-nucleus and spatial transcriptomes reveal nodule heterogeneity at different developmental stages.
Fig. 2: Dissection of central infected zone reveals distinct subtypes of nodule cells.
Fig. 3: A rare subcluster of ICs is essential for nodule maturation and function.

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Data availability

The data generated in this study are deposited in the China National Center for Bioinformation with accession PRJCA009893. Raw sequencing data are deposited in GSA with accession CRA007122 and processed data are deposited in OMIX with accession OMIX002290. Source data are provided with this paper.

Code availability

The source code to reproduce this project can be accessed at https://github.com/ZhaiLab-SUSTech/soybean_sn_st.

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Acknowledgements

This work was supported by the Key Research Program of the Chinese Academy of Sciences, Grant No. ZDRW-ZS-2019-2; Strategic Priority Research Program of the Chinese Academy of Sciences, Grant No. XDA28030100 and XDA24010205; the Agricultural Science and Technology Innovation Program; the CAS Project for Young Scientists in Basic Research (YSBR-011) and NSFC General Projects (32272101). The group of J.Z. was supported by a National Key R&D Program of China Grant (2019YFA0903903); an NSFC grant to J.Z. (31871234); the Shenzhen Sci-Tech Fund (KYTDPT20181011104005); the Key Laboratory of Molecular Design for Plant Cell Factory of Guangdong Higher Education Institutes (2019KSYS006); the Stable Support Plan Program of Shenzhen Natural Science Fund Grant (20200925153345004); and the Center for Computational Science and Engineering at Southern University of Science and Technology.

Author information

Author notes

  1. These authors contributed equally: Zhijian Liu, Xiangying Kong, Yanping Long.

Authors and Affiliations

  1. Institute of Plant and Food Science, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China

    Zhijian Liu, Yanping Long, Hong Zhang, Jinbu Jia & Jixian Zhai

  2. The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China

    Xiangying Kong, Sirui Liu, Wenhui Cui, Zunmian Zhang, Lijuan Qiu & Zhe Yan

  3. Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China

    Xiangying Kong & Zhe Yan

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

    Xiangying Kong

  5. School of Agricultural Science and Engineering, Liaocheng University, Liaocheng, China

    Sirui Liu & Zunmian Zhang

  6. Key Laboratory of Saline‐alkali Vegetation Ecology Restoration (Northeast Forestry University), Ministry of Education, Harbin, China

    Wenhui Cui

  7. State Key Laboratory of Plant Genomics and National Center for Plant Gene Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China

    Xianwei Song

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Contributions

Z.L., X.K., Y.L., S.L., W.C. and Z.Z. performed the experiments. Z.L., Y.L., H.Z. and J.J. analysed the data. Z.Y., J.Z., Z.L., X.K. and Y.L. wrote the manuscript. Z.Y. and J.Z. oversaw the study. L.Q. and X.S. provided conceptual insight.

Corresponding authors

Correspondence to Jixian Zhai or Zhe Yan.

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The authors declare no competing interests.

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Nature Plants thanks Hon-Ming Lam and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1

Heatmap representing the expression pattern of up-regulated genes for each cluster.

Extended Data Fig. 2

UMAP visualizations of clustering results (a) and cell-type specific marker genes (b).

Extended Data Fig. 3 Annotation results using public resources, including marker genes and scRNA-seq data of Arabidopsis.

a. UMAP visualizations of annotation results. Unidentified cluster is masked by grey colour. “*” indicates this cluster is annotated by label transfer method. b. Bar chart represents the percentage of different samples in each clusters. Left, successfully identified clusters. Right, un-identified clusters.

Extended Data Fig. 4 Bright-field image of soybean nodule sections used to prepare the spatial transcriptome.

Two replicates were used as the figure illustrated. Scale bars, 500 μm.

Extended Data Fig. 5 Using cluster-based method to annotate snRNA-seq datasets.

a. Clustering and annotation results of Stereo-seq datasets. b. Expression patterns of spatially transcriptome-identified cell-type upregulated genes in Stereo-seq (upper panel) and snRNA-seq (lower panel). We did not identify up-regulated genes in the epidermis from the spatial transcriptomes, so they were not mapped.

Extended Data Fig. 6 Validation of cluster-specific marker genes by RNA in situ hybridization.

These experiments were repeated in three independent assays and for each section, at least three nodules were analysed, and all showed the same expression pattern.

Extended Data Fig. 7 UMAP visualizations and RNA in situ hybridization of expression pattern of 12-0 and 12-1 specific genes that used in Fig. 2g.

Scale bars, 100 μm. These experiments were repeated in three independent assays and for each section, at least three nodules were analysed, and all showed the same expression pattern.

Extended Data Fig. 8 Developmental trajectory of UCs inferred by scVelo (a) and Monocle 3 (b).

a. Left panel, stream plot of RNA velocities on the UMAP embedding. Right panel, partition-based graph abstraction (PAGA) graph with velocity-directed edges. Arrow width indicates the transition probability between different clusters. b. Pseudo temporal ordering of nuclei after manually specified developmental root cells.

Extended Data Fig. 9

Expression pattern of four leghemoglobin genes and eleven nodulin genes.

Extended Data Fig. 10 Expression pattern of 12-1 specific genes.

Blue box, known SNF genes or homologs of known SNF genes in soybean. Asterisk indicates SNF genes collected by Roy et al1. Red box, GLYMA_02G004800, the example we used to explore the potential function of subcluster 12-1.

Supplementary information

Supplementary Information

Supplementary Figs. 1–15.

Reporting Summary

Supplementary Data 1–10

Tab 1. Gene detected information of snRNA-seq datasets. Tab 2. Upregulated genes and specific genes identified from snRNA-seq datasets. Tab 3. Differentially expressed genes in each cluster. Tab 4. Used marker genes for annotation. Tab 5. Upregulated genes identified from stereo-seq datasets. Tab 6. Used UC- and IC-upregulated genes. Tab 7. Specific genes of IC subclusters. Tab 8. Primer and vector information. Tab 9. Upregulated genes of stele subclusters. Tab 10. Upregulated genes of stele subclusters.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

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Liu, Z., Kong, X., Long, Y. et al. Integrated single-nucleus and spatial transcriptomics captures transitional states in soybean nodule maturation. Nat. Plants 9, 515–524 (2023). https://doi.org/10.1038/s41477-023-01387-z

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