Plant single-cell solutions for energy and the environment

Abstract

Progress in sequencing, microfluidics, and analysis strategies has revolutionized the granularity at which multicellular organisms can be studied. In particular, single-cell transcriptomics has led to fundamental new insights into animal biology, such as the discovery of new cell types and cell type-specific disease processes. However, the application of single-cell approaches to plants, fungi, algae, or bacteria (environmental organisms) has been far more limited, largely due to the challenges posed by polysaccharide walls surrounding these species' cells. In this perspective, we discuss opportunities afforded by single-cell technologies for energy and environmental science and grand challenges that must be tackled to apply these approaches to plants, fungi and algae. We highlight the need to develop better and more comprehensive single-cell technologies, analysis and visualization tools, and tissue preparation methods. We advocate for the creation of a centralized, open-access database to house plant single-cell data. Finally, we consider how such efforts should balance the need for deep characterization of select model species while still capturing the diversity in the plant kingdom. Investments into the development of methods, their application to relevant species, and the creation of resources to support data dissemination will enable groundbreaking insights to propel energy and environmental science forward.

Fig. 1. scRNA-seq of Arabidopsis root.

Root development has recently been extensively characterized at the single-cell level in a series of scRNA-seq studies of root cell protoplasts. These methods can confidently identify all major cell types within roots and can begin to shed light on developmental trajectories that underlie root growth. Left, cartoon of generic plant root with different cell layers colored by their major cell type. Right, schematic of single-cell transcriptome data from plant roots.

Fig. 2. Advantages of using single-cell RNA-seq to study plant-pathogen interactions.

Relatively few plant cells interact directly with most pathogens. However, these local interactions often determine disease severity. Thus, understanding gene expression in these few cells could be valuable for enhancing resistance. Unfortunately, bulk tissue RNA-seq greatly dilutes the signal from interacting cells, and signals from genes upregulated throughout the leaf in response to pathogens can mask expression changes in the interacting cells. While methods like microdissection can improve the signal-to-noise ratio to a degree, they are labor-intensive and not universally applicable to all pathogens. Thus, the increased cellular resolution promised by single-cell RNA-seq could revolutionize our understanding of plant-microbe interactions.

Fig. 3. Using single-cell transcriptome data to improve the comparative annotation of plant genomes.

Expression profiles across multiple cell types derived from single-cell transcriptome data of tissues from different plant species (left), in combination with sequence homology-based comparison of protein sequences (top right), can be used to identify functionally homologous genes across different plant species (bottom right), thereby substantially enhancing the ability to assign functional knowledge from deeply annotated model species correctly to other species that are of interest to bioenergy and biomaterial production.

Fig. 4. Using single-cell methods in bioproducts and biomaterials applications.

Top panel: single-cell resolution data can be used to find genes in biosynthesis pathways by identifying co-expressed genes in individual cells or cell types. Middle panel: single-cell expression data can identify cell-specific and condition-specific building blocks, as genes that co-vary across clusters of cells are likely regulated by common components (e.g., transcription factors). This can be exploited to identify promoters useful for bioengineering applications where production in a specific cell type is desired. Bottom panel: improvements to bioproduction targets in plant or algal systems could be achieved through correlating high-throughput phenotyping and single-cell resolution “omics” data. High-throughput analyses of mutant strains or libraries containing engineered biosynthetic clusters could be used to identify or verify which genes and pathways are necessary for the production of specific products and to optimize for higher production yield.

Fig. 5. Deep or wide?.

The schematic root system of a hypothetical plant, covering a large area close to the surface while also penetrating deeper soil layers with some of its roots, provides a visual metaphor for the need to complement “wide and shallow” characterization of many species using a select subset of single-cell assays with “deep and narrow” in-depth studies of select model species using the full arsenal of single-cell methods available.