Single-Cell RNA Sequencing Efficiently Predicts Transcription Factor Targets in Plants

Single-cell RNA-sequencing provides new insights into the cell-specific expression patterns and transcriptional regulation of photosynthetic genes in bermudagrass leaf blades

As an important warm-season turfgrass species, bermudagrass (Cynodon dactylon L.) flourishes in warm areas around the world due to the existence of the C4 photosynthetic pathway. However, how C4 photosynthesis operates in bermudagrass leaves is still poorly understood. In this study, we performed single-cell RNA-sequencing on 5296 cells from bermudagrass leaf blades. Eight cell clusters corresponding to mesophyll, bundle sheath, epidermis and vascular bundle cells were successfully identified using known cell marker genes. Expression profiling indicated that genes encoding NADP-dependent malic enzymes (NADP-MEs) were highly expressed in bundle sheath cells, whereas NAD-ME genes were weakly expressed in all cell types, suggesting C4 photosynthesis of bermudagrass leaf blades might be NADP-ME type rather than NAD-ME type. The results also indicated that starch synthesis-related genes showed preferential expression in bundle sheath cells, whereas starch degradation-related genes were highly expressed in mesophyll cells, which agrees with the observed accumulation of starch-filled chloroplasts in bundle sheath cells. Gene co-expression analysis further revealed that different families of transcription factors were co-expressed with multiple C4 photosynthesis-related genes, suggesting a complex transcription regulatory network of C4 photosynthesis might exist in bermudagrass leaf blades. These findings collectively provided new insights into the cell-specific expression patterns and transcriptional regulation of photosynthetic genes in bermudagrass.

Recent advances in exploring transcriptional regulatory landscape of crops

Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.

Advances in the Application of Single-Cell Transcriptomics in Plant Systems and Synthetic Biology

Plants are complex systems hierarchically organized and composed of various cell types. To understand the molecular underpinnings of complex plant systems, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for revealing high resolution of gene expression patterns at the cellular level and investigating the cell-type heterogeneity. Furthermore, scRNA-seq analysis of plant biosystems has great potential for generating new knowledge to inform plant biosystems design and synthetic biology, which aims to modify plants genetically/epigenetically through genome editing, engineering, or re-writing based on rational design for increasing crop yield and quality, promoting the bioeconomy and enhancing environmental sustainability. In particular, data from scRNA-seq studies can be utilized to facilitate the development of high-precision Build-Design-Test-Learn capabilities for maximizing the targeted performance of engineered plant biosystems while minimizing unintended side effects. To date, scRNA-seq has been demonstrated in a limited number of plant species, including model plants (e.g., Arabidopsis thaliana), agricultural crops (e.g., Oryza sativa), and bioenergy crops (e.g., Populus spp.). It is expected that future technical advancements will reduce the cost of scRNA-seq and consequently accelerate the application of this emerging technology in plants. In this review, we summarize current technical advancements in plant scRNA-seq, including sample preparation, sequencing, and data analysis, to provide guidance on how to choose the appropriate scRNA-seq methods for different types of plant samples. We then highlight various applications of scRNA-seq in both plant systems biology and plant synthetic biology research. Finally, we discuss the challenges and opportunities for the application of scRNA-seq in plants.

Isolation, Purification, and Application of Protoplasts and Transient Expression Systems in Plants

Protoplasts, derived from plant cells, exhibit remarkable totipotency and hold significant value across a wide spectrum of biological and biotechnological applications. These versatile applications encompass protein subcellular localization and interaction analysis, gene expression regulation, functional characterization, gene editing techniques, and single-cell sequencing. Protoplasts' usability stems from their inherent accessibility and their ability to efficiently incorporate exogenous genes. In this review, we provide a comprehensive overview, including details on isolation procedures and influencing factors, purification and viability assessment methodologies, and the utilization of the protoplast transient expression system. The aim is to provide a comprehensive overview of current applications and offer valuable insights into protoplast isolation and the establishment of transient expression systems in a diverse range of plant species, thereby serving as a valuable resource for the plant science community.

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.

Deciphering the hormone regulatory mechanisms of storage root initiation in sweet potato: challenges and future prospects

Sweet potato (Ipomoea batatas) is an economically important food crop that is grown primarily for its edible storage roots. Several researchers have consequently been conducting studies to increase sweet potato yield, and an important aspect of this research involves understanding how storage root initiation occurs. Although significant progress has been made, several challenges associated with studying this crop have resulted in lagging progress compared to other crops and thus sweet potato storage root initiation is not clearly understood. This article highlights the most important aspects of the hormone signalling processes during storage root initiation that needs to be investigated further and suggests candidate genes that should be prioritized for further study, based on their importance in storage organ formation in other crops. Lastly, ways of overcoming the challenges associated with studying this crop are suggested.

Charting plant gene functions in the multi-omics and single-cell era

Despite the increased access to high-quality plant genome sequences, the set of genes with a known function remains far from complete. With the advent of novel bulk and single-cell omics profiling methods, we are entering a new era where advanced and highly integrative functional annotation strategies are being developed to elucidate the functions of all plant genes. Here, we review different multi-omics approaches to improve functional and regulatory gene characterization and highlight the power of machine learning and network biology to fully exploit the complementary information embedded in different omics layers. Finally, we discuss the potential of emerging single-cell methods and algorithms to further increase the resolution, allowing generation of functional insights about plant biology.

Plant Single-Cell/Nucleus RNA-seq Workflow

Single-cell transcriptomics technologies allow researchers to investigate how individual cells, in complex multicellular organisms, differentially use their common genomic DNA. In plant biology, these technologies were recently applied to reveal the transcriptomes of various plant cells isolated from different organs and different species and in response to environmental stresses. These first studies support the potential of single-cell transcriptomics technology to decipher the biological function of plant cells, their developmental programs, cell-type-specific gene networks, programs controlling plant cell response to environmental stresses, etc. In this chapter, we provide information regarding the critical steps and important information to consider when developing an experimental design in plant single-cell biology. We also describe the current status of bioinformatics tools used to analyze single-cell RNA-seq datasets and how additional emerging technologies such as spatial transcriptomics and long-read sequencing technologies will provide additional information on the differential use of the genome by plant cells.

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.

Cell-Type-Specific Profiling of the Arabidopsis thaliana Membrane Protein-Encoding Genes

Membrane proteins work in large complexes to perceive and transduce external signals and to trigger a cellular response leading to the adaptation of the cells to their environment. Biochemical assays have been extensively used to reveal the interaction between membrane proteins. However, such analyses do not reveal the unique and complex composition of the membrane proteins of the different plant cell types. Here, we conducted a comprehensive analysis of the expression of Arabidopsis membrane proteins in the different cell types composing the root. Specifically, we analyzed the expression of genes encoding membrane proteins interacting in large complexes. We found that the transcriptional profiles of membrane protein-encoding genes differ between Arabidopsis root cell types. This result suggests that different cell types are characterized by specific sets of plasma membrane proteins, which are likely a reflection of their unique biological functions and interactions. To further explore the complexity of the Arabidopsis root cell membrane proteomes, we conducted a co-expression analysis of genes encoding interacting membrane proteins. This study confirmed previously reported interactions between membrane proteins, suggesting that the co-expression of genes at the single cell-type level can be used to support protein network predictions.

The biology of time: dynamic responses of cell types to developmental, circadian and environmental cues

As sessile organisms, plants are finely tuned to respond dynamically to developmental, circadian and environmental cues. Genome-wide studies investigating these types of cues have uncovered the intrinsically different ways they can impact gene expression over time. Recent advances in single-cell sequencing and time-based bioinformatic algorithms are now beginning to reveal the dynamics of these time-based responses within individual cells and plant tissues. Here, we review what these techniques have revealed about the spatiotemporal nature of gene regulation, paying particular attention to the three distinct ways in which plant tissues are time sensitive. (i) First, we discuss how studying plant cell identity can reveal developmental trajectories hidden in pseudotime. (ii) Next, we present evidence that indicates that plant cell types keep their own local time through tissue-specific regulation of the circadian clock. (iii) Finally, we review what determines the speed of environmental signaling responses, and how they can be contingent on developmental and circadian time. By these means, this review sheds light on how these different scales of time-based responses can act with tissue and cell-type specificity to elicit changes in whole plant systems.