Single cell RNA sequencing can yield high-resolution cell-type-specific expression signatures that reveal new cell types and the developmental trajectories of cell lineages. Here, we apply this approach to Arabidopsis (Arabidopsis thaliana) root cells to capture gene expression in 3,121 root cells. We analyze these data with Monocle 3, which orders single cell transcriptomes in an unsupervised manner and uses machine learning to reconstruct single cell developmental trajectories along pseudotime. We identify hundreds of genes with cell-type-specific expression, with pseudotime analysis of several cell lineages revealing both known and novel genes that are expressed along a developmental trajectory. We identify transcription factor motifs that are enriched in early and late cells, together with the corresponding candidate transcription factors that likely drive the observed expression patterns. We assess and interpret changes in total RNA expression along developmental trajectories and show that trajectory branch points mark developmental decisions. Finally, by applying heat stress to whole seedlings, we address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress. Although the response of canonical heat-shock genes dominates expression across cell types, subtle but significant differences in other genes can be detected among cell types. Taken together, our results demonstrate that single cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution. RESULTS Single-Cell RNA-Seq of Whole A. thaliana Roots Reveals Distinct Populations of Cortex, Endodermis, Hair, Nonhair, and Stele CellsWe used whole Arabidopsis roots from 7d-old seedlings to generate protoplasts for transcriptome analysis using the 103

Root architecture is a major determinant of plant fitness and is under constant modification in response to favorable and unfavorable environmental stimuli. Beyond impacts on the primary root, the environment can alter the position, spacing, density, and length of secondary or lateral roots. Lateral root development is among the best-studied examples of plant organogenesis, yet there are still many unanswered questions about its earliest steps. Among the challenges faced in capturing these first molecular events is the fact that this process occurs in a small number of cells with unpredictable timing. Single-cell sequencing methods afford the opportunity to isolate the specific transcriptional changes occurring in cells undergoing this fate transition. Using this approach, we successfully captured the transcriptomes of initiating lateral root primordia in Arabidopsis thaliana and discovered many upregulated genes associated with this process. We developed a method to selectively repress target gene transcription in the xylem pole pericycle cells where lateral roots originate and demonstrated that the expression of several of these targets is required for normal root development. We also discovered subpopulations of cells in the pericycle and endodermal cell files that respond to lateral root initiation, highlighting the coordination across cell files required for this fate transition.

Variation in regulatory DNA is thought to drive phenotypic variation, evolution, and disease. Prior studies of regulatory DNA and transcription factors across animal species highlighted a fundamental conundrum: Transcription factor binding domains and cognate binding sites are conserved, while regulatory DNA sequences are not. It remains unclear how conserved transcription factors and dynamic regulatory sites produce conserved expression patterns across species. Here, we explore regulatory DNA variation and its functional consequences within Arabidopsis thaliana, using chromatin accessibility to delineate regulatory DNA genome-wide. Unlike in previous cross-species comparisons, the positional homology of regulatory DNA is maintained among A. thaliana ecotypes and less nucleotide divergence has occurred. Of the ∼50,000 regulatory sites in A. thaliana, we found that 15% varied in accessibility among ecotypes. Some of these accessibility differences were associated with extensive, previously unannotated sequence variation, encompassing many deletions and ancient hypervariable alleles. Unexpectedly, for the majority of such regulatory sites, nearby gene expression was unaffected. Nevertheless, regulatory sites with high levels of sequence variation and differential chromatin accessibility were the most likely to be associated with differential gene expression. Finally, and most surprising, we found that the vast majority of differentially accessible sites show no underlying sequence variation. We argue that these surprising results highlight the necessity to consider higher-order regulatory context in evaluating regulatory variation and predicting its phenotypic consequences.

19Single-cell RNA-seq can yield high-resolution cell-type-specific expression signatures that 20 reveal new cell types and the developmental trajectories of cell lineages. Here, we apply this 21 approach to A. thaliana root cells to capture gene expression in 3,121 root cells. We analyze 22 these data with Monocle 3, which orders single cell transcriptomes in an unsupervised 23 manner and uses machine learning to reconstruct single-cell developmental trajectories 24 along pseudotime. We identify hundreds of genes with cell-type-specific expression, with 25 pseudotime analysis of several cell lineages revealing both known and novel genes that are 26 expressed along a developmental trajectory. We identify transcription factor motifs that 27 are enriched in early and late cells, together with the corresponding candidate 28 transcription factors that likely drive the observed expression patterns. We assess and 29interpret changes in total RNA expression along developmental trajectories and show that 30 trajectory branch points mark developmental decisions. Finally, by applying heat stress to 31 whole seedlings, we address the longstanding question of possible heterogeneity among cell 32 types in the response to an abiotic stress. Although the response of canonical heat shock 33 genes dominates expression across cell types, subtle but significant differences in other 34 genes can be detected among cell types. Taken together, our results demonstrate that 35 single-cell transcriptomics holds promise for studying plant development and plant 36 physiology with unprecedented resolution. 37 38 39 2 40 41 RESULTS 75 76 Single cell RNA-seq of whole A. thaliana roots reveals distinct populations of cortex, 77 endodermis, hair, non-hair, and stele cells 78We used whole A. thaliana roots from seven-day-old seedlings to generate protoplasts for 79 transcriptome analysis using the 10x Genomics platform (Supplemental Figure 1A). We 80 captured 3,121 root cells to obtain a median of 6,152 unique molecular identifiers (UMIs) per 81 cell. UMIs here are 10 base random tags added to the cDNA molecules that allow us to 82 differentiate unique cDNAs from PCR duplicates. These UMIs corresponded to the expression of 83 a median of 2,445 genes per cell and a total of 22,419 genes, close to the gene content of A. 84 thaliana. Quality measures for sequencing and read mapping were high. Of the approximately 85 79,483,000 reads, 73.5% mapped to the TAIR10 A. thaliana genome assembly, with 67% of 86 these annotated transcripts. These values are well within the range reported for droplet-based 87 single-cell RNA-seq in animals and humans. 88 89 For data analysis, we applied Monocle 3, which orders transcriptome profiles of single cells in an 90 unsupervised manner without a priori knowledge of marker genes (Qiu et al., 2017a; Qiu et al., 91 2017b; Trapnell et al., 2014). We used the 1500 genes in the data set (Supplemental Data Set 1) 92 that showed the highest variation in expression (Supplemental Figure 1B). For unsupervised 93 clustering, we...

Root architecture is a major determinant of fitness, and is under constant modification in response to favorable and unfavorable environmental stimuli. Beyond impacts on the primary root, the environment can alter the position, spacing, density and length of secondary or lateral roots. Lateral root development is among the best-studied examples of plant organogenesis, yet there are still many unanswered questions about its earliest steps. Among the challenges faced in capturing these first molecular events is the fact that this process occurs in a small number of cells with unpredictable timing. Single-cell sequencing methods afford the opportunity to isolate the specific transcriptional changes occurring in cells undergoing this fate transition. Using this approach, we successfully captured the transcriptomes of initiating lateral root primordia, and discovered many previously unreported upregulated genes associated with this process. We developed a method to selectively repress target gene transcription in the xylem pole pericycle cells where lateral roots originate, and demonstrated that expression of several of these targets was required for normal root development. We also discovered novel subpopulations of cells in the pericycle and endodermal cell files that respond to lateral root initiation, highlighting the coordination across cell files required for this fate transition.