Effects of Sample Size on Plant Single-Cell RNA Profiling

Single-cell RNA-sequencing profiles reveal the developmental landscape of the Manihot esculenta Crantz leaves

Cassava (Manihot esculenta Crantz) is an important crop with a high photosynthetic rate and high yield. It is classified as a C3-C4 plant based on its photosynthetic and structural characteristics. To investigate the structural and photosynthetic characteristics of cassava leaves at the cellular level, we created a single-cell transcriptome atlas of cassava leaves. A total of 11,177 high-quality leaf cells were divided into 15 cell clusters. Based on leaf cell marker genes, we identified 3 major tissues of cassava leaves, which were mesophyll, epidermis, and vascular tissue, and analyzed their distinctive properties and metabolic activity. To supplement the genes for identifying the types of leaf cells, we screened 120 candidate marker genes. We constructed a leaf cell development trajectory map and discovered 6 genes related to cell differentiation fate. The structural and photosynthetic properties of cassava leaves analyzed at the single cellular level provide a theoretical foundation for further enhancing cassava yield and nutrition.

Opportunities and challenges in the application of single-cell and spatial transcriptomics in plants

Single-cell and spatial transcriptomics have diverted researchers' attention from the multicellular level to the single-cell level and spatial information. Single-cell transcriptomes provide insights into the transcriptome at the single-cell level, whereas spatial transcriptomes help preserve spatial information. Although these two omics technologies are helpful and mature, further research is needed to ensure their widespread applicability in plant studies. Reviewing recent research on plant single-cell or spatial transcriptomics, we compared the different experimental methods used in various plants. The limitations and challenges are clear for both single-cell and spatial transcriptomic analyses, such as the lack of applicability, spatial information, or high resolution. Subsequently, we put forth further applications, such as cross-species analysis of roots at the single-cell level and the idea that single-cell transcriptome analysis needs to be combined with other omics analyses to achieve superiority over individual omics analyses. Overall, the results of this review suggest that combining single-cell transcriptomics, spatial transcriptomics, and spatial element distribution can provide a promising research direction, particularly for plant research.

Review: Challenges and perspectives in applying single nuclei RNA-seq technology in plant biology

Plant single-cell RNA-seq technology quantifies the abundance of plant transcripts at a single-cell resolution. Deciphering the transcriptomes of each plant cell, their regulation during plant cell development, and their response to environmental stresses will support the functional study of genes, the establishment of precise transcriptional programs, the prediction of more accurate gene regulatory networks, and, in the long term, the design of de novo gene pathways to enhance selected crop traits. In this review, we will discuss the opportunities, challenges, and problems, and share tentative solutions associated with the generation and analysis of plant single-cell transcriptomes. We will discuss the benefit and limitations of using plant protoplasts vs. nuclei to conduct single-cell RNA-seq experiments on various plant species and organs, the functional annotation of plant cell types based on their transcriptomic profile, the characterization of the dynamic regulation of the plant genes during cell development or in response to environmental stress, the need to characterize and integrate additional layers of -omics datasets to capture new molecular modalities at the single-cell level and reveal their causalities, the deposition and access to single-cell datasets, and the accessibility of this technology to plant scientists.