Advancing root developmental research through single-cell technologies

https://doi.org/10.1016/j.pbi.2021.102113Get rights and content

Highlights

  • Plant root meristems are uniquely suitable for lineage tracing and trajectory analysis.

  • Spatial transcriptomics will assist single-cell RNA-sequencing and might become the main tool for transcriptomic profiling.

  • Gene regulatory networks from single-cell data will be invaluable to identify conserved regulators of root development.

Abstract

Single-cell RNA-sequencing has greatly increased the spatiotemporal resolution of root transcriptomics data, but we are still only scratching the surface of its full potential. Despite the challenges that remain in the field, the orderly aligned structure of the Arabidopsis root meristem makes it specifically suitable for lineage tracing and trajectory analysis. These methods will become even more potent by increasing resolution and specificity using tissue-specific single-cell RNA-sequencing and spatial transcriptomics. Feeding multiple single-cell omics data sets into single-cell gene regulatory networks will accelerate the discovery of regulators of root development in multiple species. By providing transcriptome atlases for virtually any species, single-cell technologies could tempt many root developmental biologists to move beyond the comfort of the well-known Arabidopsis root meristem.

Introduction

Despite having joined the single-cell RNA-sequencing (scRNA-seq) party somewhat later compared with mammalian and medical research colleagues, the plant field pioneered in generating tissue-specific transcriptomic data sets on Arabidopsis root apical meristems by combining fluorescence-activated cell sorting (FACS) and microarray analysis or bulk RNA-sequencing as early as 2003. These atlases became increasingly more detailed over the years as technology advanced and even included cell-type level responses to a spectrum of abiotic and biotic stresses [1, 2, 3, 4, 5, 6, 7]. Although the importance of these data sets for the entire plant community cannot be stressed enough, the introduction of droplet-based single-cell RNA-sequencing (scRNA-seq) has undoubtedly provided a massive increase in resolution of transcriptome maps in the Arabidopsis root apical meristem [8∗, 9∗, 10∗, 11∗, 12∗, 13∗, 14∗, 15∗, 16, 17∗] and in other organs [18, 19, 20, 21, 22, 23∗, 24]. As this technology is not based on the availability of tissue-specific marker lines, it is quickly becoming a very important technology in other plant species as well [25, 26, 27∗, 28, 29, 30, 31, 32, 33, 34, 35]. Despite being fully embraced by the plant community, scRNA-seq technology is mostly being used to query gene expression in a spatiotemporal way, similar to the FACS-based data that have been around for almost 20 years [1, 2, 3, 4]. There are however clear examples of how the increase in spatiotemporal resolution has advanced our understanding of root development [10,15,23,27], but scRNA-seq technology and the available data sets have much more potential (Figure 1). We are thus only scratching the proverbial surface of what is already possible and will become possible in the very near future.

Section snippets

The blessing and the curse of using plants for single-cell analyses

It does not come as a surprise that the Arabidopsis root apical meristem was the first organ to be studied using scRNA-seq, as individual cells can easily be generated by enzymatic digestion of the cell wall in a process called protoplasting [1,36]. The capacity to generate single cells has however been and will continue to be a main bottleneck for the plant community [37, 38, 39, 40]. Besides potentially introducing an unwanted transcriptional response while generating protoplasts, commercial

Increasing specificity in single-cell experiments

Although scRNA-seq is capable of capturing rare cells or cell types, their occurrence in whole-organ atlases might still be insufficient to infer good statistical power or advance to gene discovery and functional characterization studies. Although this issue can be partially solved by profiling a larger number of cells [14], this comes with an unrealistic financial cost if high data content per cell is required or many samples are involved. As mentioned previously, this issue can be resolved by

Seeing is believing

In all cases, predictions derived from scRNA-seq data should be validated experimentally, as conclusions drawn from scRNA-seq data analyses can be skewed by biases generated during sample or library preparation and the downstream computational analysis. This can be achieved by generating reporter lines [8,15,17,20] or by performing in situ hybridization [24,27,33]. However, constructing reporter lines is limited to species that are amenable to transformation and in situ hybridization is

From off-the-shelf to out-of-the-box

Coordinated growth and development in a changing environment require interplay among many components in complex gene regulatory networks (GRNs), where transcription factors and noncoding functional cis-regulatory elements cooperatively regulate gene expression and as such determine the final cell differentiation start and phenotypical response. Owing to the high spatiotemporal resolution, single-cell data are able to deconstruct tissue heterogeneity, making it highly suited for GRN analysis [72

Conclusion

In the few years since they have been adopted by the plant field, single-cell applications are revolutionizing the way we study root development. Although they are still mostly increasing our spatiotemporal resolution and identifying specific developmental regulators, soon, they might tempt many root biologists to move beyond the well-studied Arabidopsis root meristems and quickly prepare other species for molecular biology applications by providing fully annotated transcriptome atlases. The

Author contributions

M.M., Y.K., M.S.S. and B.D.R. conceptualized and wrote the article.

Funding

This work was funded by the Ghent University Special Research Fund (BOF20/GOA/012) and the Flemish Government (VLAIO.HBC.2019.2917).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors would like to thank Carolin Seyfferth, Tina Kyndt, and Tom Beeckman for critical reading of the article and valuable comments.

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