High-Throughput Single-Cell Transcriptome Profiling of Plant Cell Types

Abstract

Single-cell transcriptome profiling of heterogeneous tissues can provide high-resolution windows into developmental dynamics and environmental responses, but its application to plants has been limited. Here, we used the high-throughput Drop-seq approach to profile >12,000 cells from Arabidopsis roots. This identified numerous distinct cell types, covering all major root tissues and developmental stages, and illuminated specific marker genes for these populations. In addition, we demonstrate the utility of this approach to study the impact of environmental conditions on developmental processes. Analysis of roots grown with or without sucrose supplementation uncovers changes in the relative frequencies of cell types in response to sucrose. Finally, we characterize the transcriptome changes that occur across endodermis development and identify nearly 800 genes with dynamic expression as this tissue differentiates. Collectively, we demonstrate that single-cell RNA-seq can be used to profile developmental processes in plants and show how they can be altered by external stimuli.

Keywords: Arabidopsis; development; endodermis; plant; root; single-cell RNA-seq; sucrose; transcriptomics.

Figure 1.. Single-Cell RNA-Seq of 12,198 Arabidopsis Root Cells Captures Diverse Cell Types

(A) Cartoon representing the cell types that comprise the Arabidopsis root. (B) t-Distributed Stochastic Neighbor Embedding (t-SNE) dimensional reduction of 12,198 single Arabidopsis root cells that were profiled using Drop-seq. Cells were clustered into 17 populations using Seurat (Butler et al., 2018). Points indicate individual cells and are colored by assigned cell type and cluster according to the legend. (C) Same as (B), except colored according to the top index of cell identity (ICI) classification for each cell regardless of statistical significance. ICI assignments passing statistical significance are shown in Figures S3B and S3C. See also Figures S1–S4 and Table S1.

Figure 2.. Stele Cell Subpopulations

Left: t-SNE representation, as in Figure 1B, of all captured single cells, showing the stele clusters (13–16) colored in shades of green. Right: t-SNE showing just these stele clusters, with each cell colored by the stele subpopulation identity assigned by ICI. Gray cells had no stele cell type identity that reached statistical significance (p < 0.05).

Figure 3.. Identification of Highly Specific Cell Type Marker Genes

(A) Violin plots showing the expression of 10 commonly used cell type marker genes across all clusters. Labels to the left of the gene names indicate the cell population each gene is used to designate. (B) Violin plots showing the cluster-specific expression of the top-ranking novel candidate marker gene for each cell population identified by this study, color-coded as in Figure 1B. See also Table S2.

Figure 4.. Sucrose Alters Root Cell Type Proportions and Tissue-Specific Gene Expression

(A) t-SNE representation as in Figure 1, colored by growth condition (sucrose±). (B) Proportion of cells in each of the 17 clusters by growth condition (sucrose±), colored as in (A). Across all experiments, 50% of cells profiled were from each growth condition (red line). Clusters with significant differences (p < 0.05 by χ² test) between the percentages of sucrose+ and sucrose− cells are denoted by red asterisks above the column. (C) Bar plot characterizing genes that are differentially expressed between sucrose+ and sucrose− growth conditions. Genes were classified by the condition in which they had higher expression (sucrose−, no shading; sucrose+, gray shading) and how many tissue or cell types exhibited differential expression of that gene. Nearly half of all differentially expressed genes have altered expression in only one tissue or cell type. (D and E) Examples of genes with tissue-specific (D) or universal (E) differential expression between sucrose+ conditions (filled violins) or sucrose− conditions (open violins). Violin plots show normalized gene expression by cell or tissue type and growth condition. Red asterisks indicate statistically significant differences (see Table S3 for exact p values).

Figure 5.. Transcriptional Dynamics of Endodermis Development

(A) Left: schematic representation of the trajectory of endodermis development. Right: t-SNE representation, as in Figure 1B, with only the endodermis cells colored blue. (B) Pseudotime trajectory of all 1,660 endodermis cells, generated using Monocle (Trapnell et al., 2014) (see STAR Methods). Each point indicates a unique cell, color coded by the original endodermis cluster as in (A). “Major trajectory” indicates the pseudotime trajectory with most cells. (C) Expression profiles along pseudotime for genes known to have dynamic expression during endodermal development. Each point indicates the log-normalized expression of the indicated gene in a single cell. (D) Right: heatmap showing the scaled and centered gene expression (Z scores) of 798 genes that show dynamic expression over the major endodermis developmental trajectory by scRNA-seq (see STAR Methods). For this plot, all endodermis cells were divided into 20 bins, distributed uniformly across pseudotime. Canonical endodermal development genes are noted in black along the left axis, with red indicating genes whose expression was validated in this study. Genes were grouped by expression pattern, from top to bottom: early, middle, and late expression. Left: top 5 gene ontology (GO) terms in each group by fold enrichment (all significantly enriched; see Table S5). Numbers in parentheses indicate the number of genes in each category. (E) AT4CL1 promoter-driven GFP is highest in mature endodermis, consistent with the AT4CL1 expression pattern predicted from the pseudotime analysis in (D). Scale bar: 200 μm. See also Figure S5 and Tables S4 and S5.