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Review
. 2024 Jul 3;22(2):qzae026.
doi: 10.1093/gpbjnl/qzae026.

Opportunities and Challenges in Advancing Plant Research with Single-cell Omics

Mohammad Saidur Rhaman  1 , Muhammad Ali  1 , Wenxiu Ye  1 , Bosheng Li  1
Affiliations
Review

Opportunities and Challenges in Advancing Plant Research with Single-cell Omics

Mohammad Saidur Rhaman et al. Genomics Proteomics Bioinformatics. .

Abstract

Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies in transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.

Keywords: Multi-omics; Single-cell epigenomics; Single-cell proteomics; Single-cell transcriptomics; Spatial transcriptomics.

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Conflict of interest statement

The authors have declared no competing interests.

Figures

Figure 1
Figure 1
Schematics diagram showing scRNA-seq in leaves A. Available single-cell isolation techniques include micro-pipetting, optical tweezers, microfluidics, LCM, FACS, microwell, and split-pool. B. Diagram showing scRNA-seq in leaves. The process includes tissue dissection, isolation of single cells, RNA extraction and cDNA synthesis, single-cell sequencing, data analysis, and cell clustering. scRNA-seq, single-cell RNA sequencing; LCM, laser capture microdissection; FACS, fluorescence-activated cell sorting; cDNA, complementary DNA; t-SNE, t-distributed stochastic neighbour embedding.
Figure 2
Figure 2
Workflow of 10X Visium ST analysis ST, spatial transcriptomics.
Figure 3
Figure 3
A proposed flowchart for multi-omics study in plants scATAC-seq, single-cell assay for transposase-accessible chromatin with high-throughput sequencing; Stereo-seq, spatial enhanced resolution omics sequencing; SM, spatial metabolomics; DNB, DNA nanoball.
Figure 4
Figure 4
The use of multi-omics techniques to study gene expression, protein abundance, metabolite levels, and epigenetic modifications in plants
See this image and copyright information in PMC

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