Single-cell genomics revolutionizes plant development studies across scales

Abstract

Understanding the development of tissues, organs and entire organisms through the lens of single-cell genomics has revolutionized developmental biology. Although single-cell transcriptomics has been pioneered in animal systems, from an experimental perspective, plant development holds some distinct advantages: cells do not migrate in relation to one another, and new organ formation (of leaves, roots, flowers, etc.) continues post-embryonically from persistent stem cell populations known as meristems. For a time, plant studies lagged behind animal or cell culture-based, single-cell approaches, largely owing to the difficulty in dissociating plant cells from their rigid cell walls. Recent intensive development of single-cell and single-nucleus isolation techniques across plant species has opened up a wide range of experimental approaches. This has produced a rapidly expanding diversity of information across tissue types and species, concomitant with the creative development of methods. In this brief Spotlight, we highlight some of the technical developments and how they have led to profiling single-cell genomics in various plant organs. We also emphasize the contribution of single-cell genomics in revealing developmental trajectories among different cell types within plant organs. Furthermore, we present efforts toward comparative analysis of tissues and organs at a single-cell level. Single-cell genomics is beginning to generate comprehensive information relating to how plant organs emerge from stem cell populations.

Keywords: Arabidopsis; Plant; Plant development; Single cell.

Fig. 1.

Overview of plant single-cell studies. (A-C) General workflow for taking an intact plant and isolating cells or nuclei, before generating barcoded cDNA, leading to a final count matrix. (D) Example species/tissues that have been profiled by single-cell RNA sequencing (Denyer et al., 2019; Gala et al., 2021; Jean-Baptiste et al., 2019; Liu et al., 2020; Lopez-Anido et al., 2021; Ryu et al., 2019; Serrano-Ron et al., 2021; Shahan et al., 2022; Shulse et al., 2019; Zhang et al., 2019). N. attenuata, Nicotiana attenuata.

Fig. 2.

Pseudotime analyses of cell differentiation trajectories. (A) Pseudotime analyses can capture developmental trajectories. Top: A consensus time heat map infers the developmental state of each cell. Warmer colors denote younger cells whereas cooler colors denote older cells (adapted from Shahan et al., 2022). Bottom: RNA velocity field projected onto the UMAP. Different colors indicate different cell types. Arrows represent average velocity and differentiation direction (adapted from Zhang et al., 2021b). (B) Various patterns for gene expression dynamics. Gene expression change could happen at an early stage (e.g. rapid downregulation; top), an intermediate stage (e.g. gradual downregulation and upregulation; middle) and a late stage (e.g. late downregulation and upregulation; bottom). Rapid upregulation and transient downregulation gene expression patterns are not pictured here because of fewer representative genes in the developmental program of maize male meiosis. Adapted from Nelms and Walbot (2019).

Fig. 3.

Comparative single-cell studies reveal genomic conservation among various tissues, cultivars and species. (A) The pairwise Spearman's correlation coefficients for the expression profile between different organs in the same individual plant. Hypothetical cell types 1 and 1′ demonstrate a similar anatomical position and function (experimental example: root phloem and leaf phloem); cell types 2 and 2′ demonstrate a different anatomical position but similar function (experimental example: root endodermis and leaf initial cells); cell types 3 and 3′ demonstrate a similar anatomical position but different function (experimental example: root epidermis and leaf epidermis); and cell types 4 and 4′ demonstrate a different anatomical position and function. Color scale: blue, positive correlation; red, negative correlation; white, no correlation. Areas of circles present the absolute value of corresponding correlation coefficients. More detailed experimental comparison between rice roots and leaves can be found in Wang et al. (2021). (B) Simplified Pearson correlation heatmap comparing cell-type transcriptomes between different cultivars or species. Cell-type homologies between hypothetically similar organs (i.e. cell type 1 in cultivar/species A versus cell type 1 in cultivar/species B) are supported by the high Pearson's correlation coefficients of the expression profiles between cell-type clusters (experimental example: rice japonica Nip root cortex versus rice indica 93-11 root cortex). Color scale: red, high correlation; yellow, low correlation. Areas of squares represent the absolute value of corresponding correlation coefficients. More detailed experimental comparison between rice japonica and indica cell types can be found in Liu et al. (2021). (C) Left: Developmental trajectory of root phloem companion cell (PCC; top) and global alignment of gene accessibility for individual orthologs is performed across A. thaliana and Zea mays PCC pseudotime trajectories (bottom). Right: PCC development-relevant orthologs are clustered into three groups based on pseudotime shifts, which represent the extent of gene accessibility deviation at any given point along the trajectory Although only around 2% of orthologs are associated with PCC pseudotime in both species, ∼50% of these orthologs exhibit similar gene accessibility patterns across pseudotime, indicating highly conserved gene accessibility pattern for genes with conserved functions (adapted from Marand et al., 2021).