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Single-nucleus transcriptomes reveal spatiotemporal symbiotic perception and early response in Medicago

Nature Plants volume 9, pages 1734–1748 (2023)Cite this article

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Abstract

Establishing legume–rhizobial symbiosis requires precise coordination of complex responses in a time- and cell type-specific manner. Encountering Rhizobium, rapid changes of gene expression levels in host plants occur in the first few hours, which prepare the plants to turn off defence and form a symbiotic relationship with the microbes. Here, we applied single-nucleus RNA sequencing to characterize the roots of Medicago truncatula at 30 min, 6 h and 24 h after nod factor treatment. We found drastic global gene expression reprogramming at 30 min in the epidermis and cortex and most of these changes were restored at 6 h. Moreover, plant defence response genes are activated at 30 min and subsequently suppressed at 6 h in non-meristem cells. Only in the cortical cells but not in other cell types, we found the flavonoid synthase genes required to recruit rhizobia are highly expressed 30 min after inoculation with nod factors. A gene module enriched for symbiotic nitrogen fixation genes showed that MtFER (MtFERONIA) and LYK3 (LysM domain receptor-like kinase 3) share similar responses to symbiotic signals. We further found that MtFER can be phosphorylated by LYK3 and it participates in rhizobial symbiosis. Our results expand our understanding of dynamic spatiotemporal symbiotic responses at the single-cell level.

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Fig. 1: Single-nucleus transcriptomes reveal Medicago root heterogeneity during the inoculation time course.
Fig. 2: Single-nucleus transcriptomes reveal cell type-specific responses induced by NFs.
Fig. 3: A subtype of epidermal cells is essential for SNF.
Fig. 4: Single-cell data reveal a nodulation-related co-expression module shared by FER, PUB1 and LYK3.
Fig. 5: MtFER is phosphorylated by LYK3.
Fig. 6: Suppression of MtFER expression inhibits root and nodule development.

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Data availability

The raw sequencing data generated in this study were deposited in China National Center for Bioinformation with accession PRJCA011245. 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/sc_medicago.

References

  1. Nutman, P. S. Centenary lecture. Phil. Trans. R. Soc. B 317, 69–106 (1987).

    Google Scholar 

  2. Kelly, S., Radutoiu, S. & Stougaard, J. Legume LysM receptors mediate symbiotic and pathogenic signalling. Curr. Opin. Plant Biol. 39, 152–158 (2017).

    Article  CAS  PubMed  Google Scholar 

  3. Roy, S. et al. Celebrating 20 years of genetic discoveries in legume nodulation and symbiotic nitrogen fixation. Plant Cell 32, 15–41 (2020).

    Article  CAS  PubMed  Google Scholar 

  4. Zipfel, C. & Oldroyd, G. E. Plant signalling in symbiosis and immunity. Nature 543, 328–336 (2017).

    Article  CAS  PubMed  Google Scholar 

  5. Antolín-Llovera, M., Ried, M. K., Binder, A. & Parniske, M. Receptor kinase signaling pathways in plant–microbe interactions. Annu. Rev. Phytopathol. 50, 451–473 (2012).

    Article  PubMed  Google Scholar 

  6. Fields, S. Global nitrogen: cycling out of control. Environ. Health Perspect. 112, A556–A563 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Oldroyd, G. E. Speak, friend and enter: signalling systems that promote beneficial symbiotic associations in plants. Nat. Rev. Microbiol. 11, 252–263 (2013).

    Article  CAS  PubMed  Google Scholar 

  8. Long, S. R. Rhizobium symbiosis: nod factors in perspective. Plant Cell 8, 1885–1898 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Arrighi, J.-F. et al. The Medicago truncatula lysine motif-receptor-like kinase gene family includes NFP and new nodule-expressed genes. Plant Physiol. 142, 265–279 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Smit, P. et al. Medicago LYK3, an entry receptor in rhizobial nodulation factor signaling. Plant Physiol. 145, 183–191 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Limpens, E. et al. LysM domain receptor kinases regulating rhizobial Nod factor-induced infection. Science 302, 630–633 (2003).

    Article  CAS  PubMed  Google Scholar 

  12. Madsen, E. B. et al. A receptor kinase gene of the LysM type is involved in legume perception of rhizobial signals. Nature 425, 637–640 (2003).

    Article  CAS  PubMed  Google Scholar 

  13. Radutoiu, S. et al. Plant recognition of symbiotic bacteria requires two LysM receptor-like kinases. Nature 425, 585–592 (2003).

    Article  CAS  PubMed  Google Scholar 

  14. Yang, J. et al. Mechanisms underlying legume–rhizobium symbioses. J. Integr. Plant Biol. 64, 244–267 (2022).

    Article  PubMed  Google Scholar 

  15. Timmers, A. C., Auriac, M.-C. & Truchet, G. Refined analysis of early symbiotic steps of the Rhizobium–Medicago interaction in relationship with microtubular cytoskeleton rearrangements. Development 126, 3617–3628 (1999).

    Article  CAS  PubMed  Google Scholar 

  16. Breakspear, A. et al. The root hair ‘infectome’ of Medicago truncatula uncovers changes in cell cycle genes and reveals a requirement for auxin signaling in rhizobial infection. Plant Cell 26, 4680–4701 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Larrainzar, E. et al. Deep sequencing of the Medicago truncatula root transcriptome reveals a massive and early interaction between nodulation factor and ethylene signals. Plant Physiol. 169, 233–265 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Damiani, I. et al. Nod factor effects on root hair-specific transcriptome of Medicago truncatula: focus on plasma membrane transport systems and reactive oxygen species networks. Front. Plant Sci. 7, 794 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Jardinaud, M.-F. et al. A laser dissection-RNAseq analysis highlights the activation of cytokinin pathways by Nod factors in the Medicago truncatula root epidermis. Plant Physiol. 171, 2256–2276 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Pecrix, Y. et al. Whole-genome landscape of Medicago truncatula symbiotic genes. Nat. Plants 4, 1017–1025 (2018).

    Article  CAS  PubMed  Google Scholar 

  21. Lohar, D. P. et al. Transcript analysis of early nodulation events in Medicago truncatula. Plant Physiol. 140, 221–234 (2005).

    Article  PubMed  Google Scholar 

  22. Rich-Griffin, C. et al. Single-cell transcriptomics: a high-resolution avenue for plant functional genomics. Trends Plant Sci. 25, 186–197 (2020).

    Article  CAS  PubMed  Google Scholar 

  23. Libault, M., Pingault, L., Zogli, P. & Schiefelbein, J. Plant systems biology at the single-cell level. Trends Plant Sci. 22, 949–960 (2017).

    Article  CAS  PubMed  Google Scholar 

  24. Denyer, T. et al. Spatiotemporal developmental trajectories in the Arabidopsis root revealed using high-throughput single-cell RNA sequencing. Dev. Cell 48, 840–852 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Birnbaum, K. et al. A gene expression map of the Arabidopsis root. Science 302, 1956–1960 (2003).

    Article  CAS  PubMed  Google Scholar 

  26. Farmer, A., Thibivilliers, S., Ryu, K. H., Schiefelbein, J. & Libault, M. Single-nucleus RNA and ATAC sequencing reveals the impact of chromatin accessibility on gene expression in Arabidopsis roots at the single-cell level. Mol. Plant 14, 372–383 (2021).

    Article  CAS  PubMed  Google Scholar 

  27. Sunaga‐Franze, D. Y. et al. Single-nucleus RNA sequencing of plant tissues using a nanowell-based system. Plant J. 108, 859–869 (2021).

    Article  PubMed  Google Scholar 

  28. Neumann, M. et al. A 3D gene expression atlas of the floral meristem based on spatial reconstruction of single nucleus RNA sequencing data. Nat. Commun. 13, 2838 (2022).

  29. Long, Y. et al. FlsnRNA-seq: protoplasting-free full-length single-nucleus RNA profiling in plants. Genome Biol. 22, 66 (2021).

  30. Cervantes-Pérez, S. A. et al. Cell-specific pathways recruited for symbiotic nodulation in the Medicago truncatula legume. Mol. Plant 15, 1868–1888 (2022).

    Article  PubMed  Google Scholar 

  31. Biała, W., Banasiak, J., Jarzyniak, K., Pawela, A. & Jasiński, M. Medicago truncatula ABCG10 is a transporter of 4-coumarate and liquiritigenin in the medicarpin biosynthetic pathway. J. Exp. Bot. 68, 3231–3241 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Liu, J. et al. Closely related members of the Medicago truncatula PHT1 phosphate transporter gene family encode phosphate transporters with distinct biochemical activities. J. Biol. Chem. 283, 24673–24681 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. De Carvalho-Niebel, F., Timmers, A. C., Chabaud, M., Defaux-Petras, A. & Barker, D. G. The Nod factor-elicited annexin MtAnn1 is preferentially localised at the nuclear periphery in symbiotically activated root tissues of Medicago truncatula. Plant J. 32, 343–352 (2002).

    Article  PubMed  Google Scholar 

  34. Xiao, T. T. et al. Fate map of Medicago truncatula root nodules. Development 141, 3517–3528 (2014).

    Article  CAS  PubMed  Google Scholar 

  35. Chiou, T. J., Liu, H. & Harrison, M. J. The spatial expression patterns of a phosphate transporter (MtPT1) from Medicago truncatula indicate a role in phosphate transport at the root/soil interface. Plant J. 25, 281–293 (2001).

    Article  CAS  PubMed  Google Scholar 

  36. Cerri, M. R. et al. Medicago truncatula ERN transcription factors: regulatory interplay with NSP1/NSP2 GRAS factors and expression dynamics throughout rhizobial infection. Plant Physiol. 160, 2155–2172 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Mbengue, M. et al. The Medicago truncatula E3 ubiquitin ligase PUB1 interacts with the LYK3 symbiotic receptor and negatively regulates infection and nodulation. Plant Cell 22, 3474–3488 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Franssen, H. J. et al. Root developmental programs shape the Medicago truncatula nodule meristem. Development 142, 2941–2950 (2015).

    CAS  PubMed  Google Scholar 

  39. Laffont, C. et al. MtNRLK1, a CLAVATA1-like leucine-rich repeat receptor-like kinase upregulated during nodulation in Medicago truncatula. Sci. Rep. 8, 2046 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Nguyen, N. N. et al. PHO1 family members transport phosphate from infected nodule cells to bacteroids in Medicago truncatula. Plant Physiol. 185, 196–209 (2021).

    CAS  PubMed  Google Scholar 

  41. Roy, S. et al. MtLAX2, a functional homologue of the Arabidopsis auxin influx transporter AUX1, is required for nodule organogenesis. Plant Physiol. 174, 326–338 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Wendrich, J. R. et al. Vascular transcription factors guide plant epidermal responses to limiting phosphate conditions. Science 370, eaay4970 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wang, C. et al. Lotus japonicus clathrin heavy Chain1 is associated with Rho-Like GTPase ROP6 and involved in nodule formation. Plant Physiol. 167, 1497–1510 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Silady, R. A. et al. The GRV2/RME-8 protein of Arabidopsis functions in the late endocytic pathway and is required for vacuolar membrane flow. Plant J. 53, 29–41 (2008).

    Article  CAS  PubMed  Google Scholar 

  45. Robinson, M. S. Adaptable adaptors for coated vesicles. Trends Cell Biol. 14, 167–174 (2004).

    Article  CAS  PubMed  Google Scholar 

  46. Barois, N. & Bakke, O. The adaptor protein AP-4 as a component of the clathrin coat machinery: a morphological study. Biochem. J. 385, 503–510 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Timmers, A., Auriac, M.-C., de Billy, F. & Truchet, G. Nod factor internalization and microtubular cytoskeleton changes occur concomitantly during nodule differentiation in alfalfa. Development 125, 339–349 (1998).

    Article  CAS  PubMed  Google Scholar 

  48. Oldroyd, G. E. & Downie, J. A. Coordinating nodule morphogenesis with rhizobial infection in legumes. Annu. Rev. Plant Biol. 59, 519–546 (2008).

    Article  CAS  PubMed  Google Scholar 

  49. Lin, J., Frank, M. & Reid, D. No home without hormones: how plant hormones control legume nodule organogenesis. Plant Commun. 1, 100104 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Wang, K. L.-C., Li, H. & Ecker, J. R. Ethylene biosynthesis and signaling networks. Plant Cell 14, S131–S151 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Cai, J. et al. Role of the Nod factor hydrolase MtNFH1 in regulating Nod factor levels during rhizobial infection and in mature nodules of Medicago truncatula. Plant Cell 30, 397–414 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Arrighi, J.-F. et al. The RPG gene of Medicago truncatula controls Rhizobium-directed polar growth during infection. Proc. Natl Acad. Sci. USA 105, 9817–9822 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. del Campillo, E., Gaddam, S., Mettle-Amuah, D. & Heneks, J. A tale of two tissues: AtGH9C1 is an endo-β-1, 4-glucanase involved in root hair and endosperm development in Arabidopsis. PLoS ONE 7, e49363 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Camps, C. et al. Combined genetic and transcriptomic analysis reveals three major signalling pathways activated by Myc-LCOs in Medicago truncatula. New Phytol. 208, 224–240 (2015).

    Article  CAS  PubMed  Google Scholar 

  55. Maillet, F. et al. Fungal lipochitooligosaccharide symbiotic signals in arbuscular mycorrhiza. Nature 469, 58–63 (2011).

    Article  CAS  PubMed  Google Scholar 

  56. Kuppusamy, K. T. et al. LIN, a Medicago truncatula gene required for nodule differentiation and persistence of rhizobial infections. Plant Physiol. 136, 3682–3691 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Malolepszy, A. et al. A plant chitinase controls cortical infection thread progression and nitrogen-fixing symbiosis. eLife 7, e38874 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Rival, P. et al. Epidermal and cortical roles of NFP and DMI3 in coordinating early steps of nodulation in Medicago truncatula. Development 139, 3383–3391 (2012).

    Article  CAS  PubMed  Google Scholar 

  59. Yano, K. et al. CYCLOPS, a mediator of symbiotic intracellular accommodation. Proc. Natl Acad. Sci. USA 105, 20540–20545 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Murray, J. D. et al. Vapyrin, a gene essential for intracellular progression of arbuscular mycorrhizal symbiosis, is also essential for infection by rhizobia in the nodule symbiosis of Medicago truncatula. Plant J. 65, 244–252 (2011).

    Article  CAS  PubMed  Google Scholar 

  61. Gao, Q. et al. A receptor–channel trio conducts Ca2+ signalling for pollen tube reception. Nature 607, 534–539 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Duan, Q., Kita, D., Li, C., Cheung, A. Y. & Wu, H.-M. FERONIA receptor-like kinase regulates RHO GTPase signaling of root hair development. Proc. Natl Acad. Sci. USA 107, 17821–17826 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Zhu, S. et al. The RALF1–FERONIA complex phosphorylates eIF4E1 to promote protein synthesis and polar root hair growth. Mol. Plant 13, 698–716 (2020).

    Article  CAS  PubMed  Google Scholar 

  64. Guo, H. et al. FERONIA receptor kinase contributes to plant immunity by suppressing jasmonic acid signaling in Arabidopsis thaliana. Curr. Biol. 28, 3316–3324 (2018).

    Article  CAS  PubMed  Google Scholar 

  65. Procko, C. et al. Leaf cell-specific and single-cell transcriptional profiling reveals a role for the palisade layer in UV light protection. Plant Cell 34, 3261–3279 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).

    Article  CAS  PubMed  Google Scholar 

  68. Grindberg, R. V. et al. RNA-sequencing from single nuclei. Proc. Natl Acad. Sci. USA 110, 19802–19807 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Thrupp, N. et al. Single-nucleus RNA-seq is not suitable for detection of microglial activation genes in humans. Cell Rep. 32, 108189 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. 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).

  71. Czaja, L. F. et al. Transcriptional responses toward diffusible signals from symbiotic microbes reveal MtNFP-and MtDMI3-dependent reprogramming of host gene expression by arbuscular mycorrhizal fungal lipochitooligosaccharides. Plant Physiol. 159, 1671–1685 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Mithöfer, A. Suppression of plant defence in rhizobia–legume symbiosis. Trends Plant Sci. 7, 440–444 (2002).

    Article  PubMed  Google Scholar 

  73. Stegmann, M. et al. The receptor kinase FER is a RALF-regulated scaffold controlling plant immune signaling. Science 355, 287–289 (2017).

    Article  CAS  PubMed  Google Scholar 

  74. Kessler, S. A. et al. Conserved molecular components for pollen tube reception and fungal invasion. Science 330, 968–971 (2010).

    Article  CAS  PubMed  Google Scholar 

  75. Wang, M. et al. Phosphorylation of MtRopGEF2 by LYK3 mediates MtROP activity to regulate rhizobial infection in Medicago truncatula. J. Integr. Plant Biol. 63, 1787–1800 (2021).

    Article  CAS  PubMed  Google Scholar 

  76. Gao, J.-P. et al. Nod factor receptor complex phosphorylates GmGEF2 to stimulate ROP signaling during nodulation. Curr. Biol. 31, 3538–3550 (2021).

    Article  CAS  PubMed  Google Scholar 

  77. Kohring, B., Baier, R., Niehaus, K., Puhler, A. & Flaschel, E. Production of nodulation factors by Rhizobium meliloti: fermentation, purification and characterization of glycolipids. Glycoconj. J. 14, 963–971 (1997).

    Article  CAS  PubMed  Google Scholar 

  78. Boisson-Dernier, A. et al. Agrobacterium rhizogenes-transformed roots of Medicago truncatula for the study of nitrogen-fixing and endomycorrhizal symbiotic associations. Mol. Plant Microbe Interact. 14, 695–700 (2001).

    Article  CAS  PubMed  Google Scholar 

  79. Boivin, C., Camut, S., Malpica, C. A., Truchet, G. & Rosenberg, C. Rhizobium meliloti genes encoding catabolism of trigonelline are induced under symbiotic conditions. Plant Cell 2, 1157–1170 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Thibivilliers, S., Anderson, D. & Libault, M. Isolation of plant root nuclei for single cell RNA sequencing. Curr. Protoc. Plant Biol. 5, e20120 (2020).

    Article  CAS  PubMed  Google Scholar 

  81. 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 

  82. Germain, P.-L., Lun, A., Meixide, C. G., Macnair, W. & Robinson, M. D. Doublet identification in single-cell sequencing data using scDblFinder. F1000Research 10, 979 (2021).

    Article  PubMed  Google Scholar 

  83. Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  85. Timshel, P. N., Thompson, J. J. & Pers, T. H. Genetic mapping of etiologic brain cell types for obesity. eLife 9, e55851 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37, 685–691 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  89. Baran, Y. et al. MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions. Genome Biol. 20, 206 (2019).

  90. Van de Sande, B. et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat. Protoc. 15, 2247–2276 (2020).

    Article  PubMed  Google Scholar 

  91. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  92. Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).

    Article  CAS  PubMed  Google Scholar 

  93. Katoh, K., Misawa, K., Kuma, K. I. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Suyama, M., Torrents, D. & Bork, P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 34, W609–W612 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Stamatakis, A. Using RAxML to infer phylogenies. Curr. Protoc. Bioinformatics 51, 6.14.1–6.14.14 (2015).

    Article  PubMed  Google Scholar 

  96. Mai, U. & Mirarab, S. TreeShrink: fast and accurate detection of outlier long branches in collections of phylogenetic trees. BMC Genomics 19, 272 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Du, H. et al. Multiple optimal reconciliations under the duplication-loss-coalescence model. IEEE/ACM Trans. Comput. Biol. Bioinform. 18, 2144–2156 (2019).

    Article  Google Scholar 

  98. Baker, W. J. et al. A comprehensive phylogenomic platform for exploring the angiosperm tree of life. Syst. Biol. 71, 301–319 (2022).

    Article  CAS  PubMed  Google Scholar 

  99. Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Daniel Gietz, R. & Woods, R. A. in Methods in Enzymology Vol. 350 (eds Guthrie, C. & Fink, G. R.) 87–96 (Academic Press, 2002).

  101. Dong, W. et al. An SHR–SCR module specifies legume cortical cell fate to enable nodulation. Nature 589, 586–590 (2021).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank J. Murray and H. Liu for helpful suggestions on the manuscript. This work is supported by the National Key R&D Program of China (grant nos. 2019YFA0903903 and 2019YFA0904703); National Natural Science Foundation of China (32070270, 32050081, 32088102 and 31825003); the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2016ZT06S172); the Shenzhen Sci-Tech Fund (KYTDPT20181011104005); the Key Laboratory of Molecular Design for Plant Cell Factory of Guangdong Higher Education Institutes (2019KSYS006); CAS Project for Young Scientists in Basic Research (YSBR-011); and the Stable Support Plan Program of Shenzhen Natural Science Fund (20200925153345004).

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Author notes

  1. These authors contributed equally: Zhijian Liu, śJun Yang, Yanping Long, Chi Zhang.

Authors and Affiliations

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

    Zhijian Liu, Yanping Long & Jixian Zhai

  2. New Cornerstone Science Laboratory, National Key Laboratory of Plant Molecular Genetics, Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China

    Jun Yang, Chi Zhang, Dapeng Wang, Xiaowei Zhang, Wentao Dong & Ertao Wang

  3. State Key Laboratory for Managing Biotic and Chemical Treats to the Quality and Safety of Agro-Products, Key Laboratory of Biotechnology in Plant Protection of MOA of China and Zhejiang Province, Institute of Virology and Biotechnology, Zhejiang Academy of Agricultural Sciences, Hangzhou, China

    Chi Zhang

  4. School of Life Sciences, Division of Life Sciences and Medicine, MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, University of Science and Technology of China, Hefei, China

    Li Zhao & Chengwu Liu

  5. School of Life Science and Technology, ShanghaiTech University, Shanghai, China

    Ertao Wang

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Contributions

E.W., J.Z., Z.L. and J.Y. directed the research. Z.L., J.Y., Y.L. and C.Z. performed most of the experiments and analysis. D.W., X.Z., W.D., L.Z. and C.L. contributed to the analytical, molecular cloning and transformation work. E.W. and J.Z. oversaw the entire study. Z.L., J.Y., Y.L., E.W. and J.Z. wrote the manuscript.

Corresponding authors

Correspondence to Jixian Zhai or Ertao Wang.

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Extended data

Extended Data Fig. 1 UMAP visualizations of known cell type-specific marker genes.

For each cell type, three genes are used to annotate its corresponding clusters. The gene locus and references for each gene can be found in Supplementary Table 1. This figure is related with Fig. 2d.

Extended Data Fig. 2 Label transfer results from Arabidopsis single-cell datasets by scANVI.

a. UMAP visualization of transferred annotation. b. The bar plots represent the predicted percentage of cells of different cell types in each cell cluster. N indicates the cell number. c. The predicted results for epidermal cells.

Extended Data Fig. 3 Heatmap representing the expression pattern of cluster-specific genes for each cluster.

The bottom panel shoed a zoomed-in view of the gene expression pattern in cluster 9. The full genes list can be obtained in Supplementary Data. 2. This figure is related with Fig. 2e.

Extended Data Fig. 4 Data validation using a second biological replicate.

a. Quality control information for the snRNA-seq dataset. b. UMAP visualization of data integration and clustering results. The right panel shows the change in nuclei belonging to the replicate 1 between the clustering results of replicate 1 only analysis and the two-replicates analysis. The sequencing data from the second replicate were preprocessed using the same pipeline as the first replicate. We then applied the scVI algorithm for data integration and the Leiden algorithm for clustering the integrated dataset. The parameters used were identical to those used in the analysis of replicate 1 only. Then the clusters obtained from the combined datasets were renamed based on their similarity to the replicate 1 only clustering. c. Dot plot of partial SNF genes with cluster-specific expression patterns. The genes are identical to those shown in Fig. 1e. d. Left: UMAP visualization of the epidermis subcluster. Right: The change in nuclei belonging to replicates between the replicate 1 only clustering and the combined clustering of two replicates. e. Dot plot of partial SNF genes with cluster-specific expression patterns. The genes are identical to those shown in Fig. 3e. f. The number timepoint-specific genes identified from the NF-treatment time course with biological replicates. Left, all genes. Right, timepoint-specific genes with cluster-specific expression patterns (that is spatiotemporal-specific genes). To identify genes specific to each timepoint, we first grouped nuclei from the same replicates, timepoints and clusters together to form the pseudobulk datasets and then used the likelihood-ratio test wrapped in edgeR to perform the differential expression analysis. Only genes with adjusted p-values less than 0.05 and a fold change greater than 2 were retained. The full gene list is provided in Supplementary Data. 5. g. The overlap with the timepoint-specific genes identified in replicate 1.

Extended Data Fig. 5 Comparison of genes differentially expressed in response to NF treatment for snRNA-seq data and public single-cell-type transcriptome datasets.

We identified DEGs in snRNA-seq data by comparison with the control sample. Whole roots DEGs were obtained by directly comparing gene expression in all nuclei at a given inoculation timepoint vs the control, rather than in a particular cluster. We used the two-sided Wald test implemented in diffxpy to identify DEGs and the full list of DEGs is provided in supplementary dataset 4. The p-values for publicly available data are obtained from their original publications. To make the results comparable, we used the following thresholds: fold change > 2 and adjusted p-value < 0.05. a, b. The counts of upregulated genes (a) and downregulated genes (b) in different studies. c, d. Pairwise comparison of upregulated genes (c) and downregulated genes (d) identified by different studies. The colour represents the percentage of DEGs identified in the data corresponding to the row that were also identified in the data corresponding to the column. For example, the black box represents the 50.6% of upregulated expression genes identified in the snRNA-seq that were also upregulated in the Damiani et al.‘s data.

Extended Data Fig. 6 Phylogeny of FER and FER-like genes.

Genomes of Aeschynomene evenia, Arabidopsis thaliana, Bauhinia variegate, Lotus japonicus, Lupinus albu, Medicago truncatula, Oryza sativa, Phaseolus lunatus, Populus trichocarpa and Vitis vinifera were selected, representing species ranging from monocots, basal core eudicots, to legumes. Protein sequences of the orthologs of AtFER (AT3G51550.1) and AtFER-like genes were aligned using mafft-linsi, which were then converted to codon alignments of nucleotide sequences using pal2nal and used to infer phylogenetic relationship with maximum-likelihood approach using RAxML with bootstrap set to 100. Midpoint rooting were performed wiht FigTree and long branches were cut with TreeShrink with quantile set to 0.1. Speciation nodes and duplication nodes were identified with Duplication-Loss-Coalescence Model of dlcpar using species topology extracted from Tree of Life 2.0 with parameter "search". Genes connected via duplication nodes to AtFER were considered as AtFER paralogs and genes connected to AtFER absent of duplication nodes were considered as AtFER orthologs. The final reconciled tree was illustrated with iTOL. Duplication nodes were marked with black dots. The FER clade is highlighted in light red and AtFER and MtFER (Medtr7g073660.1) are highlighted in red and bold.

Extended Data Fig. 7 pLYK3::GUS and pMtFER::GUS show similar expression patterns in nodules inoculated with Sm1021.

pLYK3::GUS (b and e) and pFER::GUS (c and f) show similar expression patterns of roots 24 h after inoculated with Sm1021 when compared with EV (a and d). pLYK3::GUS (h, i and m) and pFER::GUS (j, k and n)) also show similar expression patterns in nodule primordia and mature nodules when compared with EV (g and l). Scale bars, 2 mm (a–f) and 200 μm (g-n). Experiments in a–n were independently repeated three times with similar results.

Extended Data Fig. 8 Suppression of MtFER expression inhibits root hair growth.

a. Representative photographs of root hair phenotype at 21 dpi in EV and MtFERi-1/-2/-3. BF, bright field. Scale bars, 100 μm. b. Quantification of root hair length in EV and MtFERi-1/-2/-3 transgenic hairy roots (EV, n = 26; MtFERi-1/-3, n = 22; MtFERi-2, n = 21). Data are mean ± SD. c. Root hair density of EV and MtFERi-1/-2/-3 transgenic hairy roots (EV, n = 15; MtFERi-1/-2/-3, n = 15). Data are mean ± SD. Experiments in b and c were independently repeated three times with similar results. Statistically significant differences between EV and MtFERi-1/-2/-3 groups in experiments b and c were determined by one-way ANOVA followed by Duncan’s multiple range tests (p < 0.05), different letters indicate significant difference. The exact p values of Duncan’s multiple range tests can be found in Supplementary Data 8.

Extended Data Fig. 9 Representative roots and nodules from EV and MtFERi hairy roots.

Representative photographs of roots and nodules from EV (a, e) and MtFERi hairy roots (b–d and f-h) at 21 dpi with Sm1021 expressing the LacZ gene. Rhizobia in the nodules (e-h) show blue colour when stained by X-Gal. Scale bars, 1 cm (a–d) and 200 μm (e-h). Experiments in e-h were independently repeated three times with similar results.

Extended Data Fig. 10 Expression pattern of defence and symbiosis marker genes in EV and MtFERi hairy roots after NF treatment.

Relative expression levels of WRKY (a), Chitinase (b), NIN (c), ENOD11 (d), Vapyrin (e) and FLOT4 (f) in EV and MtFERi-1/-2 (n ≥ 15) after NFs treatment. Expression levels of defence and symbiosis marker genes were normalized against the reference gene Histone 2A and EF-1, respectively. Data are mean ± SD. Experiments were repeated three times with similar results. Different letters indicate significant difference [Statistically significant difference between control and experimental groups were determined by one-way ANOVA (Duncan’s multiple range tests; p < 0.05)]. The exact p values of Duncan’s multiple range tests can be found in Supplementary Data 8.

Supplementary information

Supplementary Information

Supplementary Figs. 1–18 and Tables 1–4.

Reporting Summary

Supplementary Data

Supplementary Data 1: The basic quality control information of snRNA-seq data. Data 2: Cluster-specific genes identified in each cluster. We used Cellex algorithm to assign a cell-type expression specificity score for each gene and only genes with a score above 0.8 were retained. Data 3: Genes with similar expression patterns across different clusters and timepoints. Here, we used k-means algorithm to classify genes into six distinct categories. Genes belonging to the same category share a similar expression pattern. Data 4: The list of differentially expressed genes. Data 5: The list of timepoint-specific genes identified with biological replicates. Data 6: The hormone-related genes which are specifically expressed in each inoculation timepoint. Data 7: Co-expression gene module identified by WGCNA algorithm. First, we grouped cells with similar transcriptomes together using the metacells algorithm to reduce sampling variance caused by the high degree of sparsity in snRNA-seq data. Then, we used WGCNA to identify the co-expression gene modules. Data 8: P values of Duncan’s multiple range tests.

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Liu, Z., Yang, J., Long, Y. et al. Single-nucleus transcriptomes reveal spatiotemporal symbiotic perception and early response in Medicago. Nat. Plants 9, 1734–1748 (2023). https://doi.org/10.1038/s41477-023-01524-8

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