Abstract
Different plant species within the grasses were parallel targets of domestication, giving rise to crops with distinct evolutionary histories and traits1. Key traits that distinguish these species are mediated by specialized cell types2. Here we compare the transcriptomes of root cells in three grass species—Zea mays, Sorghum bicolor and Setaria viridis. We show that single-cell and single-nucleus RNA sequencing provide complementary readouts of cell identity in dicots and monocots, warranting a combined analysis. Cell types were mapped across species to identify robust, orthologous marker genes. The comparative cellular analysis shows that the transcriptomes of some cell types diverged more rapidly than those of others—driven, in part, by recruitment of gene modules from other cell types. The data also show that a recent whole-genome duplication provides a rich source of new, highly localized gene expression domains that favour fast-evolving cell types. Together, the cell-by-cell comparative analysis shows how fine-scale cellular profiling can extract conserved modules from a pan transcriptome and provide insight on the evolution of cells that mediate key functions in crops.
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Data availability
All reference genomes were downloaded from Arabidopsis TAIR10.38, at https://www.arabidopsis.org/, for Maize B73 v4, S. bicolor v3 and S. viridis v2 reference genomes at https://plants.ensembl.org/. All raw single-cell and single-nucleus RNA-seq data, expression matrices and analysed R-Seurat objects are available under Gene Expression Omnibus accession GSE225118. All data used to generate figures are available at https://figshare.com/articles/dataset/Data_for_Guillotin_et_al_/22331002, except for the following figures, for which the data can be found in the following deposited files under GEO accession GSE225118: Arabidopsis_Cells_Nuclei_Seurat_Obj.RData.gz (Fig. 1c and Extended Data Figs. 2c,d and 4a,b), Maize_Sorghum_Setaria_Cells_Nuclei_Seurat_Obj.RData.gz (Extended Data Figs. 3d and 5c,d). Data in Extended Data Figs. 2c,d and 3d, and those in Extended Data Fig. 5c,d are clustered separately. Data on single-cell RNA-seq quality control are provided in Supplementary Table 1. Analysis of single-cell versus single-nucleus RNA-seq data is provided in Supplementary Tables 2 and 3. Cell-specific marker genes for all species, including a shared pan library of marker genes, are provided in Supplementary Table 4. Data on regulon analysis are provided in Supplementary Table 5. Data on duplicate genes are provided in Supplementary Tables 6 and 7. Cellular divergence analysis is provided in Supplementary Table 8 and in situ probe information is provided in Supplementary Table 9.
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Acknowledgements
The authors thank M. Purugganan and G. Coruzzi for helpful comments. This work was funded by National Science Foundation (IOS-1934388) to K.D.B., D.J. and J.G., the National Institutes of Health (R35GM136362) to K.D.B., Human Frontiers of Science (LT000972/2018-L) to B.G. and startup funds from the University of California Riverside to S.C.G. In addition, M.P. is funded by the William Randolph Hearst Scholarship from the School of Biological Sciences. J.G. is also supported by the National Institutes of Health (R01 LM012736 and R01 MH113005).
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B.G. and K.D.B designed the research. B.G. generated all single-cell and single-nucleus RNA-seq data, with early profiles performed by C.O.R. M.A.M. and B.G. designed the single-nucleus RNA-seq protocol. R.R. and B.G. performed the whole-mount in situ hybridization analysis. R.R., X.X. and D.J. performed the tissue preparation and histology for the spatial transcriptomics analysis. S.C.G. and B.G. conceived the analysis strategy and performed the tests for dosage compensation. S.K.R. performed the non-WGD duplication identification. M.P and J.G. performed the MetaNeighbor, MINI-EX, CoCoCoNet and validation analysis. B.G. analysed all the data. K.D.B., B.G. and R.R. wrote the manuscript.
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Extended data figures and tables
Extended Data Fig. 1 Quality control and fidelity analysis of RNA-seq profiles using violin plots.
a Distribution of the number of UMI detected among cells vs. nuclei. b Distribution of the number of genes detected among cells vs. nuclei. c Pearson correlation distributions of gene expression from single-cell or single-nucleus compared to whole-root RNAseq data in Arabidopsis and maize. The distributions are derived by randomly sampling 2,000 genes for correlation analysis between cells and nuclei. The random sampling was repeated 250 times to generate the distribution of correlation values. Violin plots display show the kernel probability density of the data at different values, boxplot inside display as the middle black line is the median, exact media is displayed on the graphs, the lower and upper hinges correspond to the first and third quartiles (Q1,Q3), extreme line shows Q3+1.5xIQR to Q1-1.5xIQR (interquartile range-IQR). Dots beyond the extreme lines shows potential outliers.
Extended Data Fig. 2 Evaluation of agreement in nuclear and cell type profiles.
a, b UMAP clustering in Arabidopsis single-cells (a) and single-nuclei (b) clustered independently, showing clusters with the same diagnosed cell identities. c, d Dot plots showing expression levels per cluster and expression in percent of cells of the same set of cell-type specific markers in cells (c) or nuclei (d). The markers are in the same order in both plots.
Extended Data Fig. 3 Analysis of sensitivity of nuclear and cell profiles in distinguishing clusters and identifying markers.
a Arabidopsis down sampling analysis shows the number of cells needed to resolve different clusters. A branch signifies that a new cluster with a known cell type identity was distinguished at a given sample size. b A similar analysis using the single nucleus RNA-seq dataset, showing that more nuclei are needed to resolve the same number of clusters compared to cells in (a). Tracking the branches of graphs in (a) vs. (b) leads to a rule-of-thumb that two-fold more nuclei than cells are needed to identify clusters. c UMAP of the combined maize single-cell and -nuclei datasets, clusters are colored by cell type identity. d Dotplot of maize marker genes in cells (blue) or in nuclei (red), showing overall concordance of marker gene expression in the two datasets.
Extended Data Fig. 4 Analysis of differentially regulated genes and cell capture efficiency in nuclear vs. cellular profiles.
a, b Heatmaps of genes known to be induced by protoplast generation (Birnbaum et al., 2003) showing their expression in cells (a) vs. nuclei (b). The analysis shows that stress-induced genes also have higher expression in cells vs. nuclei, with a bias in specific cell types. c Distribution of expression levels of genes annotated for mRNA decay in cells or in nuclei, decay values from Sorenson et al., 2018. A significant increase in expression of mRNA decay-related genes was detected in nuclei, (n = 1965 genes, Wilcoxon rank sum test, two-sided, p-value = 1.98e-11), the boxplots display the middle line is the median, the lower and upper hinges correspond to the first and third quartiles (Q1,Q3), extreme line shows Q3+1.5xIQR to Q1-1.5xIQR (interquartile range-IQR). Dots beyond the extreme lines shows potential outliers. d Proportion of cells vs nuclei present in each cell type cluster.
Extended Data Fig. 5 Analysis of marker gene identification in maize single nucleus vs. cell profiles.
a, b UMAPs of maize single-cell and single-nucleus RNA-seq data clustered independently. Only the single nucleus RNA-seq dataset displays a cluster annotated as columella, which is absent in the single-cell dataset. c, d Dotplot of maize marker genes for each cell type cluster, showing expression in cells (c) and in nuclei (d) datasets independently. Markers for columella outlined in the red box are only present in the single nucleus dataset.
Extended Data Fig. 6 Analysis of overall expression similarity among all cellular and nuclear clusters in the three monocot species studied.
a AUROC test comparing every cell type in all species for both cell and nuclei datasets, showing that clusters discovered in either cell or nuclei group by like cell type and not by either species or source of material (cells or nuclei). b–c UMAPs generated by additional integration of the dataset using a Python supervised integration method scGen. This method uses a variational autoencoder to learn the underlying latent space for the cell types. b Different colors represent the clusters identified by the Seurat integration mapped onto the new scGen integration, showing Seurat classification was in relative agreement with the scGen classification. i.e., scGEN clusters have relatively homogenous coloration. c The same UMAP as in (b), this time showing the species distribution. Overall, each cluster has cells from each of the three species.
Extended Data Fig. 7 In-situ hybridization corroborating evidence for marker localization in single cell/nuclei RNA-seq profiles in maize.
a–n in situ hybridization using Hairpin Chain Reaction (HCR) probes labeling various transcripts. Cross sections are on the left and longitudinal sections are on the right. UMAPs showing each transcript’s cluster localization are displayed next to each probe’s fluorescent image. Additionally, spatial transcriptomics imaging data of the same probe is shown in the right column for (c–e). The minimum/maximum values for each fluorescence channel (grey: autofluorescence, magenta: HCR probes) have been adjusted to show the localization more clearly in the merged image.
Extended Data Fig. 8 In-situ hybridization corroborates evidence for localization of marker gene expression from single-cell RNA-seq profiles in sorghum.
a-i In situ hybridization using Hairpin Chain Reaction (HCR) probes labeling various transcripts. Cross sections are on the left and longitudinal sections on the right (a,c,d,e). Longitudinal sections are shown in (f,g,h,i). UMAPs showing each transcript’s cluster localization are shown next to each probe’s fluorescent image. The minimum/maximum values for each fluorescence channel (grey: autofluorescence, magenta: HCR probes) have been adjusted to show the localization more clearly in the merged image.
Extended Data Fig. 9 Regulon conservation across species, and distribution of gene pair expression patterns.
a Conserved regulons found using MINI-EX and their pattern of expression. The regulon is labeled by the transcription factor that putatively regulates it in each row. b–d Distribution of genes pairs on the dominance vs. regulatory subfunctionalization scale for transposed, tandem and proximal duplicate pairs. In blue, neofunctionalized duplicates are shown as a percentage of the bar. e–g Distribution on the dominance to regulatory subfunctionalization scale for dispersed gene duplicate pairs binned in thirds by their Ks value. The graphs suggest that duplicates tend to lose co-expressed patterns and gain dominance over time. h Boxplot of Ks values showing the distribution among all the duplicate classes used in the analysis. In h, statistical analysis was performed using a Kruskal-Wallis one-way ANOVA followed by the Tukey test for all pairwise comparisons. Not sharing a letter represents statistical significance at p < 0.05. In boxplots the middle line is the median, the lower and upper hinges correspond to the first and third quartiles (Q1,Q3), extreme line shows Q3+1.5xIQR to Q1-1.5xIQR (interquartile range-IQR). Dots beyond the extreme lines shows potential outliers. h. n = 10,104 WGD, n = 860 Proximal, n = 3,154 Transposed, n = 7,552 Dispersed, n = 1,448 Tandem.
Extended Data Fig. 10 Overall analysis of expression conservation in duplicate classes and analysis of columella expression across species.
a–c Dosage compensation analysis representing the expression ratios of maize over sorghum orthologous genes in tandem, transposed, and dispersed duplicate pairs. The first two boxplots represent cases in which a sorghum ortholog is expressed in the same homologous cell type as only a single maize duplicate (either M1 or M2). The third and fourth boxplots represent cases in which both homeologs are expressed in the same cell and a sorghum homolog is expressed in a homologous cell type. The last boxplot shows the ratio when both of the co-expressed homeologs are added together in the numerator, showing a mean ratio close to 1. The higher expression in the first two boxplots compared to the second two indicates dosage compensation. d Conservation rate of cis-regulatory elements between WGD homeolog pairs in promoters. The plot shows no major differences between co-expressed and dominant gene pairs, and no major differences among the different classes of duplication. e–h Distribution of maize genes displaying regulatory neofunctionalization of expression into new cell types. Colors signify the cell type of origin. i Expression heatmap of the 443 genes displaying high expression divergence across species in columella cells in maize, according to CoCoCoNet, with the orthologous gene expression in the other two species. j Example of the gene DMR6 switching its expression between columella in maize to epidermis / cortex in sorghum. a-c, statistical analysis was performed using ANOVA followed by the Tukey test for all pairwise comparisons, Not sharing a letter represents statistical significance at p < 0.05. In boxplots the middle line is the median, the lower and upper hinges correspond to the first and third quartiles (Q1,Q3), extreme line shows Q3+1.5xIQR to Q1-1.5xIQR (interquartile range-IQR). Dots beyond the extreme lines shows potential outliers. a–h: n = 10,104 WGD, n = 860 Proximal, n = 3,154 Transposed, n = 7,552 Dispersed, n = 1,448 Tandem.
Supplementary information
Supplementary Table 1
Metric summary and details of different replicates performed in this study, number of genes from cell nucleus profiles and thresholds used.
Supplementary Table 2
Arabidopsis clustering repartition of nucleus or cell profiles alone and merged dataset.
Supplementary Table 3
Differentially expressed genes between nucleus and cell profiles for each species
Supplementary Table 4
Cell-type markers in maize, sorghum and Setaria, including conserved markers for each cell type across species.
Supplementary Table 5
Mini-Ex regulon prediction.
Supplementary Table 6
Metrics of orthologue dominance between M1 and M2, including cis-regulatory element predictions, Ka, Ks and neofunctionalization caracteristics.
Supplementary Table 7
GO_Enrichment_S_SM1_SM2_M1M2_SM1M2 used for Fig. 3e.
Supplementary Table 8
CococoNet Analysis all_functional_scores_calculated_Columella Enrichment Mucilage.
Supplementary Table 9
Probe references for in situ hybridization and molecular cartography.
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Guillotin, B., Rahni, R., Passalacqua, M. et al. A pan-grass transcriptome reveals patterns of cellular divergence in crops. Nature 617, 785–791 (2023). https://doi.org/10.1038/s41586-023-06053-0
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DOI: https://doi.org/10.1038/s41586-023-06053-0
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