Single-cell RNA-seq works as efficiently in plant cells as in animal cells. Noise profiles are well understood and an early set of analytical approaches is now capable of extracting information not previously possible in pooled samples. The biggest technical challenges to adapting single-cell protocols to plants will be dissociating cells from the appropriate tissues and obtaining high numbers of cells for high-throughput analysis. In addition, the technical noise associated with single-cell assays and the lack of true biological replicates pose a challenge in distinguishing differences in gene expression between single cells. The unsupervised grouping of cells before statistical analysis has been used to create de facto replicate samples, but researchers need to be cautious of batch effects that can dominate unsupervised clustering. Nonetheless, most of these problems are not unique to single-cell analysis and the ability to profile large numbers of cells can be leveraged to address noise and identify replicate cell states. Towards that end, multiple bioinformatic tools for the analysis of single-cell transcriptomes have been developed and successfully applied. Single-cell analysis of whole organs has the potential to identify highly localized responses to stress and environmental inputs, map developmental trajectories, and rapidly profile emerging models where specific fluorescent markers are not yet available. Thus, in addition to the specific questions discussed herein, single-cell analysis holds the potential to generate datasets that could rapidly accelerate comparative developmental genomics at the cell level.
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