Published April 30, 2025 | Published

Journal Article Open

Single-cell transcriptomics reveal how root tissues adapt to soil stress

Creators: Zhu, Mingyuan¹; Hsu, Che-Wei¹; Peralta Ogorek, Lucas L.²; Taylor, Isaiah W.¹; La Cavera, Salvatore²; Oliveira, Dyoni M.³; Verma, Lokesh²; Mehra, Poonam²; Mijar, Medhavinee¹; Sadanandom, Ari⁴; Perez-Cota, Fernando²; Boerjan, Wout³; Nolan, Trevor M.^{1, 5}; Bennett, Malcolm J.²; Benfey, Philip N.¹; Pandey, Bipin K.²

1. Duke University
2. University of Nottingham
3. Ghent University
4. Durham University
5. California Institute of Technology

Abstract

Land plants thrive in soils showing vastly different properties and environmental stresses¹. Root systems can adapt to contrasting soil conditions and stresses, yet how their responses are programmed at the individual cell scale remains unclear. Using single-cell RNA sequencing and spatial transcriptomic approaches, we showed major expression changes in outer root cell types when comparing the single-cell transcriptomes of rice roots grown in gel versus soil conditions. These tissue-specific transcriptional responses are related to nutrient homeostasis, cell wall integrity and defence in response to heterogeneous soil versus homogeneous gel growth conditions. We also demonstrate how the model soil stress, termed compaction, triggers expression changes in cell wall remodelling and barrier formation in outer and inner root tissues, regulated by abscisic acid released from phloem cells. Our study reveals how root tissues communicate and adapt to contrasting soil conditions at single-cell resolution.

Copyright and License

© 2025, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Acknowledgement

We thank S. Brady, S.-Y. He, A. Roeder, K. Birnbaum, R. Shahan and J. Scharwies for critical reading of the manuscript. We thank S. Bhattacharya and J. Kläver for their valuable support on our spatial transcriptomic experiments with Resolve Biosciences. We thank Xiaoying Zhu for drawing and editing the anatomical diagrams. We thank B. Cole for sharing the codes to generate 3D scRNA-seq UMAP video. We thank A. Franzluebbers for sharing the natural soils collected in the North Carolina region. We thank J. Zhang (CAS, China) and G. Huang (SJTU, China) for sharing the mhz5, aba1, aba2 and proCSLD1-VENUS-N7 seeds. We thank the staff of the Duke Phytotron for plant care, and the Duke Center for Genomic and Computational Biology for Illumina sequencing services. This study was supported by the Howard Hughes Medical Institute (P.N.B.); National Institutes of Health grant no. MIRA 1R35GM131725 (P.N.B.); NSF grant no. NSF PHY-1915445 (P.N.B.), grant no. USDA-NIFA 2021-67034-35139 (I.W.T). Biotechnology and Biological Sciences Research Council (BBSRC) Discovery Fellowship grant no. BB/V00557X/1, Royal Society Research grant no. RGS\R1\231374 and UKRI Frontiers Research (ERC StG, EP/Y036697/1) (B.K.P.); BBSRC grant nos. BB/W008874/1 and BB/S020551/1 (M.J.B.); ERC SYNERGY (grant no. 101118769—HYDROSENSING) grant no. BB/V003534/1 (sLOLA) (M.J.B.) and EMBO-ALTF (grant no. 619-2022) (L.L.P.O.). D.M.O is indebted to the Research Foundation Flanders (FWO; grant no. 1246123N) for a postdoctoral fellowship. P.M. acknowledges BBSRC Discovery Fellowship (grant no. BB/Z514482/1) and Horizon Europe ERC Starting grant (no. 101161820-WATER-BLIND). S.L.C. acknowledges funding from the Royal Academy of Engineering Research Fellowships scheme (grant no. RF-2324-23-223) and the Nottingham Research Fellowship scheme. F.P.-C. acknowledges funding from the Royal academy of Engineering (grant no. RF\201718\17144) and the Engineering and Physical Sciences Research Council (grant no. EP/W031876/1).

Data Availability

All information supporting the conclusions are provided with the paper. scRNA-seq data for Xkitaake and Kitaake roots grown under gel and soil conditions is available at National Center for Biotechnology Information (NCBI) BioProject PRJNA1055099 (GSE251706). scRNA-seq from ref. ⁸ (PMID 33824350) is available at NCBI BioProject PRJNA706435 and PRJNA706099. Bulk RNA-seq data for developmental-stage annotation is available at NCBI BioProject PRJNA1082669 (GSE260671). Bulk RNA-seq data for protoplasting-induced genes is available at NCBI BioProject PRJNA1194134 (GSE283509). Bulk RNA-seq data for Xkitaake roots grown under compacted and non-compacted soil conditions are available at NCBI BioProject PRJNA1193632 (GSE283428). Raw data for spatial transcriptomics (Molecular Cartography) is provided in Supplementary Data 4 (gel), Supplementary Data 6 (non-compacted soils) and Supplementary Data 8 (compacted soils). Source data are provided with this paper. Gene accession number information is available in Supplementary Table 14. Supplementary tables are provided with this paper. Supplementary Data 1–10 are available on the Nature Figshare platform at https://doi.org/10.6084/m9.figshare.25146260. The processed scRNA-seq for gel-grown rice roots is now publicly accessible through a user-friendly platform hosted on Shiny (https://rice-singlecell.shinyapps.io/orvex_app/).

Code Availability

We adapted codes published in ref. ⁹ (https://doi.org/10.1016/j.xpro.2022.101729), ref. ⁴⁵ (https://doi.org/10.1016/j.cell.2019.05.031), ref. ⁴⁸ (https://doi.org/10.1038/s41467-020-19894-4) and ref. ⁵¹ (https://doi.org/10.12688/f1000research.24956.2) for our scRNA-seq analysis. The adapted codes for analysing the scRNA-seq data are available at GitHub at https://github.com/zhumy09/scRNA-seq-for-rice.

Conflict of Interest

P.N.B. was the cofounder and Chair of the Scientific Advisory Board of Hi Fidelity Genetics, Inc., a company that works on crop root growth. The other authors declare no competing interests.

Supplemental Material

Supplementary Table 1

scRNA-seq sample high-quality cell numbers related to cell type and developmental-stage annotation related to Figs. 1–3.

Supplementary Table 2

Protoplast inducing gene list (include both protoplasted versus non-protoplasted and protoplasted for 3 h versus protoplasted for 2.5 h), together with rice mitochondrial and chloroplast gene lists.

Supplementary Table 3

Cell type marker list identified through the integration of published data, single-cell RNA-seq data and spatial transcriptomics.

Supplementary Table 4

Clusters annotation for Xkitaake gel, non-compacted soils and compacted soils scRNA-seq integrated object based on average z-scores of cell type markers.

Supplementary Table 5

DEG list in each cell type in the comparison of Xkitaake scRNA-seq data between gel-grown roots and soil-grown roots.

Supplementary Table 6

Enriched GO term for DEGs in each cell type in the comparison of Xkitaake scRNA-seq data between gel-grown roots and soil-grown roots.

Supplementary Table 7

Clusters annotation for Xkitaake gel, non-compacted soils scRNA-seq and Kitaake gel, non-compacted soils scRNA-seq integrated object based on average z-scores of cell type markers.

Supplementary Table 8

DEG list in each cell type in the comparison of Kitaake scRNA-seq data between gel-grown roots and soil-grown roots.

Supplementary Table 9

Enriched GO term for DEGs in each cell type in the comparison of Kitaake scRNA-seq data between gel-grown roots and soil-grown roots.

Supplementary Table 10

DEG list in each cell type in the comparison of Xkitaake scRNA-seq data between non-compacted-soil-grown roots and compacted-soil-grown roots.

Supplementary Table 11

Enriched GO term for DEGs in each cell type in the comparison of Xkitaake scRNA-seq data between non-compacted-soil-grown roots and compacted-soil-grown roots.

Supplementary Table 12

The summary file of our bulk RNA-seq results, including (1) the normalized read counts for Xkitaake roots protoplasted for 2.5 h (P2.5 hours), Xkitaake roots protoplasted for 3 h (P3 hours), Xkitaake roots grown in gel conditions (Gel), Xkitaake roots grown in non-compacted soil conditions (NC) and Xkitaake roots grown in compacted soil conditions (CMP); (2) DEGs for comparison between Xkitaake roots P3 hours and P2.5 hours, DEGs for comparison between Xkitaake CMP and NC; (3) enriched GO terms for Xkitaake CMP and NC upregulated genes and Xkitaake CMP and NC downregulated genes.

Supplementary Table 13

Cell area of exodermis and cortex cells quantified for transverse sections of rice roots grown in non-compacted and compacted soils.

Supplementary Table 14

Locus ID or putative gene annotation, and relevant biological functions for the genes included in figures in this paper.

Supplementary Data 1

scRNA-seq sample COPILOT summary information and details related to annotation. A combined PDF summary file is also included.

Supplementary Data 2

Raw reads processed file and averaged read counts for the RNA-seq data of manually dissected root tissue segments corresponding to meristematic, elongation and maturation zones. R codes for generating the reads files are also included.

Supplementary Data 3

Combined feature plots representing the expression patterns of cell type markers in single-cell RNA-seq data. Each image represents the gene expressions of markers for one certain cell type.

Supplementary Data 4

Expression patterns of cell type markers in spatial transcriptomics data for gel-grown roots. A PDF summary file that includes all the sample and gene information for visualization is included. The raw spatial transcriptomics data for gel-grown roots, as well as the list of candidate genes for probe designing are also included.

Supplementary Data 5

Combined Feature plots representing the expression patterns of cell type markers in single-cell RNA-seq data for non-compacted soil grown roots. Each image represents the gene expressions of markers for one certain cell type.

Supplementary Data 6

Expression patterns of cell type markers in spatial transcriptomics data for non-compacted soil grown roots. A PDF summary file that includes the sample and gene information for visualization is included. The raw spatial transcriptomics data for non-compacted soil grown roots is also included.

Supplementary Data 7

Combined feature plots representing the expression patterns of cell type markers in scRNA-seq data for compacted soil grown roots. Each image represents the gene expressions of markers for one certain cell type.

Supplementary Data 8

Expression patterns of cell type markers in spatial transcriptomics data for compacted soil grown roots. A PDF summary file that includes the sample and gene information for visualization is included. The raw spatial transcriptomics data for compacted soil grown roots is also included.

Supplementary Data 9

Jupyter notebook for checking the cell type identity for low-quality cells after the COPILOT processing of soil-based scRNA-seq data is included.

Supplementary Data 10

Full gene and GO term for differential expression analysis. CSV files including the full (all, not filtered by P value or fold change) gene and GO term for differential expression analysis of our scRNA-seq data. The comparison includes Xkitaake_Soil versus Gel, Xkitaake_CMP versus NC and Kitaake_Soil versus Gel (where CMP represents compacted soil conditions and NC represents non-compacted soil conditions). These are raw data to generate gene expression and GO term enrichment files for heatmap plotting.

Supplementary Video 1

Animation showing 3D UMAP of the atlas with cell type annotations. The rotating 3D UMAP of the gel-based scRNA-seq atlas with cell type annotations. Each dot represents an individual cell with a high-quality single-cell transcriptome. Colours indicate different cell types. This 3D UMAP corresponds to Fig. 1b.

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