Advances and Opportunities in Single-Cell Transcriptomics for Plant Research

Abstract

Single-cell approaches are quickly changing our view on biological systems by increasing the spatiotemporal resolution of our analyses to the level of the individual cell. The field of plant biology has fully embraced single-cell transcriptomics and is rapidly expanding the portfolio of available technologies and applications. In this review, we give an overview of the main advances in plant single-cell transcriptomics over the past few years and provide the reader with an accessible guideline covering all steps, from sample preparation to data analysis. We end by offering a glimpse of how these technologies will shape and accelerate plant-specific research in the near future.

Keywords: cell atlas; cell trajectory; plant transcriptomics; protoplast; scRNA-seq.

Figure 1. Timeline of key events in plant single cell transcriptomics.

Selection of milestones related to the development and establishment of single cell transcriptomics in the plant field are illustrated by green boxes on the timeline. General technological developments are shown in grey and key development outside of the plant field are indicated in pink.

Figure 2. Schematic overview of a typical droplet-based plant scRNA-seq experiment.

Individual cells are isolated from the tissue containing different cell identities (illustrated by differently colored cells) by enzymatic digestion. As an optional step (dashed line), specific cell populations can be enriched using FACS. Next, cells are compartmentalized together with barcoded beads, buffers, and enzymes required for library preparation into oil droplets. After droplet formation, cells are lysed, and the released mRNAs are bound to the bead; followed by library preparation and next generation sequencing. The data is next filtered and normalized after which the reads are mapped against a reference genome. Dimensionality reduction clustering is performed before assigning cell identities. The resulting data set can then be used to e.g. predict gene regulatory networks or perform trajectory inference analysis.

Figure 3. Application of single cell technologies in plant cell research.

(a-b) Enrichment of subpopulation can be used to identify and characterize cell state changes along a developmental trajectory. Slow transitions between two states suggest gradual changes in the transcriptome, while fast cell state transitions, without intermediate stages, can suggesting a switch-like behavior in the cell states. (c) Ultra high-throughput analysis of ten-to hundreds of thousands of single cells could reveal heterogeneity within cells of the same cell type. The transcriptomes of such exceptional responders carry useful information to understand phenotypic plasticity. (d) Multi-omics single cell approaches can be used to correlate cell-specific transcriptome profiles with gene regulatory elements or other cellular information (metabolome, proteome, etc.). (e) Combining single cell transcriptomic profiles with spatial information can reveal cell-to-cell communication signals as seen in ligand-receptor mediated pathways. (f) Data depository and integration initiatives, like the Plant Cell Atlas, aim to unify experimental conditions and sample processing to allow a standardized analysis and integration of scRNA-seq data sets as valuable community resources.