Dynamics of Gene Expression in Single Root Cells of Arabidopsis thaliana

Abstract

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

Single-cell transcriptome analyses recapitulate the cellular and developmental responses to abiotic stresses in rice

“…One of the strategies is to encapsulate cells into individual liquid droplets using a microfluidic device, and therefore, the transcriptomes of thousands of cells were obtained in a single experiment; identifies of individual cells are further recognized with following-up computational analyses (Klein et al, 2015;Macosko et al, 2015). Such strategy was mainly applied to animal tissues, although a growing number of applications in plants were implemented in the year of 2019 (Denyer et al, 2019;Jean-Baptiste et al, 2019;Rich-Griffin et al, 2019;Ryu et al, 2019;Shulse et al, 2019;Zhang et al, 2019). All these studies have been focused on the Arabidopsis root; the application of scRNA-seq to other plant cells remains absent, presumably because less prior knowledge on the cellular composition and developmental processes makes it technically more challenging.…”

Expression partitioning of duplicate genes at single cell resolution inArabidopsisroots

“…This is by no means a new idea, but it has not been applied to polyploidy, with the notable exception of the elegant and extensive work on cotton fibers, which are single cells of great economic importance and an excellent system for studying development at the cellular level (Shi et al, 2006;Gou et al, 2007;Taliercio and Boykin, 2007;Hovav et al, 2008a, b;Al-Ghazi et al, 2009;Rapp et al, 2010;Wang et al, 2010;Nigam et al, 2014;Yoo and Wendel, 2014;Hu et al, 2015;Tuttle et al, 2015;Bao et al, 2019;Gallagher et al, 2020). The recent spate of papers on Arabidopsis root biology at single cell resolution (Jean-Baptiste et al, 2019;Ryu et al, 2019;Shulse et al, 2019;Denyer et al, 2019;Zhang et al, 2019) genome that has led to the classification of its duplicated genes by mechanism of duplication (e.g., Wang et al, 2013;Hao et al, 2018;Qiao et al, 2019), this has provided us with the opportunity to explore the evolution of gene expression in over 11,000 paralogous pairs at a finer scale than has previously been reported. We observed numerous cases of greatly biased paralogue expression among cell types and states of the root, in several cases involving alternate bias of the two paralogues ( Figure 5-7).…”

“…Single cell methods that already have revolutionized biology continue to develop and promise ever more powerful and precise data (Lähnemann et al, 2020). In plants, several groups recently published single cell transcriptomic studies of Arabidopsis roots (Jean-Baptiste et al, 2019;Ryu et al, 2019;Shulse et al, 2019;Denyer et al, 2019;Zhang et al, 2019) that not only D if fe r e n ti a ti n g M a tu r e C e ll 1 C e ll 2 C e ll 3 C e ll 4 C e ll 5 C e ll 6…”

Visualization of Protein Coding, Long Noncoding, and Nuclear RNAs by Fluorescence in Situ Hybridization in Sections of Shoot Apical Meristems and Developing Flowers

“…To further unravel the functions of coding and noncoding transcripts, it is necessary to resolve their distribution at the single cell and subcellular levels. Single-cell RNA sequencing has been recently applied in plants to resolve transcriptome dynamics in different root cell types (Denyer et al, 2019;Jean-Baptiste et al, 2019;Ryu et al, 2019;Shulse et al, 2019;Zhang et al, 2019). However, only some genes are expressed in a cell typespecific manner (consider cell cycle-expressed genes as a counterexample), and these methods do not yet, at least, resolve subcellular locations of RNA molecules.…”