The potential of single-cell profiling in plants

Abstract

Single-cell transcriptomics has been employed in a growing number of animal studies, but the technique has yet to be widely used in plants. Nonetheless, early studies indicate that single-cell RNA-seq protocols developed for animal cells produce informative datasets in plants. We argue that single-cell transcriptomics has the potential to provide a new perspective on plant problems, such as the nature of the stem cells or initials, the plasticity of plant cells, and the extent of localized cellular responses to environmental inputs. Single-cell experimental outputs require different analytical approaches compared with pooled cell profiles and new tools tailored to single-cell assays are being developed. Here, we highlight promising new single-cell profiling approaches, their limitations as applied to plants, and their potential to address fundamental questions in plant biology.

Fig. 1

Single-cell transcriptomic profiles in plants. a The technical noise profile between two single cells of the same cell type, showing high dispersion for transcripts expressed at a low level. The axes are read-counts representing gene expression levels on a log2 scale. As most genes are expected to be expressed at similar levels, the two axes evaluate replication and show that, at these scales, genes expressed at higher levels show the potential to distinguish biological from technical noise. b (upper) The expression distribution of a gene among pooled samples typically shows a peak frequency on a positive expression value. (lower) Gene expression among single-cell samples typically shows a peak frequency at zero, with a subset of cells showing a second peak of positive read counts in a subset of samples. Density represents the frequency of cells showing a given expression level (read count). c Several gold-standard markers in single-cell profiles of cells with known tissue origins. These functional markers are expressed at higher levels (e.g., more replicable expression in a and non-zero expression in b (lower). In these real samples collected from plant cells, markers for the quiescent center (QC), stele, and epidermis all show detectable expression in target cells and are largely absent in non-target cells, with some false-positive and false-negative expression

Fig. 2

Hypothetical example showing the pseudo-time ordering of cells collected from the root meristem. (upper) The green-colored cells represent a reporter marking the endodermis and quiescent center (QC). The color gradient represents a continuum of cellular maturation from birth (at bottom) to differentiation (towards the top). Cells are dissociated and isolated using fluorescence-activated cell sorting (FACS), whereupon ordering information is lost. At the right, single-cell expression profiles are used to infer a pseudo-ordering as cells in an approximate sequence. (lower) Two general methods of pseudo-time ordering are shown. Method 1 is unsupervised, using dimensionality reduction to position cells in a hypothetical space and then imposing an optimal path that infers the developmental progression of cells (e.g., Monocle). Method 2 uses markers to place cells in a specific location or developmental zone, with specific approaches differing in the way they adapt to false negatives and false positives. Seurat infers the expression of missing “gold-standard” markers based on coexpressed genes. Index of cell identity (ICI) employs many markers that “vote” on cell localization, where misleading diagnostic markers from false positives and false negatives are overcome by a majority of true positives. (Schematic by Ramin Rahni)