Advances and applications of single-cell omics technologies in plant research

Single-cell transcriptome landscape of the kidney reveals potential innate immune regulation mechanisms in hybrid yellow catfish after Aeromonas hydrophila infection

Aeromonas hydrophila, the pathogen that is the causative agent of motile Aeromonas septicemia (MAS) disease, commonly attacks freshwater fishes, including yellow catfish (Pelteobagrus fulvidraco). Although the kidney is one of the most important organs involved in immunity in fish, its role in disease progression has not been fully elucidated. Understanding the cellular composition and innate immune regulation mechanisms of the kidney of yellow catfish is important for the treatment of MAS. In this study, single-cell RNA sequencing (scRNA-seq) was performed on the kidney of hybrid yellow catfish (Pelteobagrus fulvidraco ♀ × Pelteobagrus vachelli ♂) after A. hydrophila infection. Nine types of kidney cells were identified using marker genes, and a transcription module of marker genes in the main immune cells of hybrid yellow catfish kidney tissue was constructed using in-situ hybridization. In addition, the single-cell transcriptome data showed that the differentially expressed genes of macrophages were primarily enriched in the Toll-like receptor and Nod-like receptor signaling pathways. The expression levels of genes involved in these pathways were upregulated in macrophages following A. hydrophila infection. Transmission electron microscopy and TUNEL analysis revealed the cellular characteristics of macrophages before and after A. hydrophila infection. These data provide empirical support for in-depth research on the role of the kidney in the innate immune response of hybrid yellow catfish.

Integrated Review of Transcriptomic and Proteomic Studies to Understand Molecular Mechanisms of Rice's Response to Environmental Stresses

Rice (Oryza sativa L.) is grown nearly worldwide and is a staple food for more than half of the world's population. With the rise in extreme weather and climate events, there is an urgent need to decode the complex mechanisms of rice's response to environmental stress and to breed high-yield, high-quality and stress-resistant varieties. Over the past few decades, significant advancements in molecular biology have led to the widespread use of several omics methodologies to study all aspects of plant growth, development and environmental adaptation. Transcriptomics and proteomics have become the most popular techniques used to investigate plants' stress-responsive mechanisms despite the complexity of the underlying molecular landscapes. This review offers a comprehensive and current summary of how transcriptomics and proteomics together reveal the molecular details of rice's response to environmental stresses. It also provides a catalog of the current applications of omics in comprehending this imperative crop in relation to stress tolerance improvement and breeding. The evaluation of recent advances in CRISPR/Cas-based genome editing and the application of synthetic biology technologies highlights the possibility of expediting the development of rice cultivars that are resistant to stress and suited to various agroecological environments.

A single-cell transcriptome atlas reveals the trajectory of early cell fate transition during callus induction in Arabidopsis

The acquisition of pluripotent callus from somatic cells plays an important role in plant development studies and crop genetic improvement. This developmental process incorporates a series of cell fate transitions and reprogramming. However, our understanding of cell heterogeneity and mechanisms of cell fate transition during callus induction remains quite limited. Here, we report a time-series single-cell transcriptome experiment on Arabidopsis root explants that were induced in callus induction medium for 0, 1, and 4 days, and the construction of a detailed single-cell transcriptional atlas of the callus induction process. We identify the cell types responsible for initiating the early callus: lateral root primordium-initiating (LRPI)-like cells and quiescent center (QC)-like cells. LRPI-like cells are derived from xylem pole pericycle cells and are similar to lateral root primordia. We delineate the developmental trajectory of the dedifferentiation of LRPI-like cells into QC-like cells. QC-like cells are undifferentiated pluripotent acquired cells that appear in the early stages of callus formation and play a critical role in later callus development and organ regeneration. We also identify the transcription factors that regulate QC-like cells and the gene expression signatures that are related to cell fate decisions. Overall, our cell-lineage transcriptome atlas for callus induction provides a distinct perspective on cell fate transitions during callus formation, significantly improving our understanding of callus formation.

Unraveling plant-microbe symbioses using single-cell and spatial transcriptomics

Plant-microbe symbioses require intense interaction and genetic coordination to successfully establish in specific cell types of the host and symbiont. Traditional RNA-seq methodologies lack the cellular resolution to fully capture these complexities, but single-cell and spatial transcriptomics (ST) are now allowing scientists to probe symbiotic interactions at an unprecedented level of detail. Here, we discuss the advantages that novel spatial and single-cell transcriptomic technologies provide in studying plant-microbe endosymbioses and highlight key recent studies. Finally, we consider the remaining limitations of applying these approaches to symbiosis research, which are mainly related to the simultaneous capture of both plant and microbial transcripts within the same cells.

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

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.

Global dynamics and cytokinin participation of salt gland development trajectory in recretohalophyte Limonium bicolor

Salt gland is an epidermal Na+ secretory structure that enhances salt resistance in the recretohalophyte sea lavender (Limonium bicolor). To elucidate the salt gland development trajectory and related molecular mechanisms, we performed single-cell RNA sequencing of L. bicolor protoplasts from young leaves at salt gland initiation and differentiation stages. Dimensionality reduction analyses defined 19 transcriptionally distinct cell clusters, which were assigned into 4 broad populations-promeristem, epidermis, mesophyll, and vascular tissue-verified by in situ hybridization. Cytokinin was further proposed to participate in salt gland development by the expression patterns of related genes and cytological evidence. By comparison analyses of Single-cell RNA sequencing with exogenous application of 6-benzylaminopurine, we delineated 5 salt gland development-associated subclusters and defined salt gland-specific differentiation trajectories from Subclusters 8, 4, and 6 to Subcluster 3 and 1. Additionally, we validated the participation of TRIPTYCHON and the interacting protein Lb7G34824 in salt gland development, which regulated the expression of cytokinin metabolism and signaling-related genes such as GLABROUS INFLORESCENCE STEMS 2 to maintain cytokinin homeostasis during salt gland development. Our results generated a gene expression map of young leaves at single-cell resolution for the comprehensive investigation of salt gland determinants and cytokinin participation that helps elucidate cell fate determination during epidermis formation and evolution in recretohalophytes.

Current views of drought research: experimental methods, adaptation mechanisms and regulatory strategies

Drought stress is one of the most important abiotic stresses which causes many yield losses every year. This paper presents a comprehensive review of recent advances in international drought research. First, the main types of drought stress and the commonly used drought stress methods in the current experiment were introduced, and the advantages and disadvantages of each method were evaluated. Second, the response of plants to drought stress was reviewed from the aspects of morphology, physiology, biochemistry and molecular progression. Then, the potential methods to improve drought resistance and recent emerging technologies were introduced. Finally, the current research dilemma and future development direction were summarized. In summary, this review provides insights into drought stress research from different perspectives and provides a theoretical reference for scholars engaged in and about to engage in drought research.

Unraveling the molecular mechanisms governing axillary meristem initiation in plants

MAIN CONCLUSION

Axillary meristems (AMs) located in the leaf axils determine the number of shoots or tillers eventually formed, thus contributing significantly to the plant architecture and crop yields. The study of AM initiation is unavoidable and beneficial for crop productivity. Shoot branching is an undoubted determinant of plant architecture and thus greatly impacts crop yield due to the panicle-bearing traits of tillers. The emergence of the AM is essential for the incipient bud formation, and then the bud is dormant or outgrowth immediately to form a branch or tiller. While numerous reviews have focused on plant branching and tillering development networks, fewer specifically address AM initiation and its regulatory mechanisms. This review synthesizes the significant advancements in the genetic and hormonal factors governing AM initiation, with a primary focus on studies conducted in Arabidopsis (Arabidopsis thaliana L.) and rice (Oryza sativa L.). In particular, by elaborating on critical genes like LATERAL SUPPRESSOR (LAS), which specifically regulates AM initiation and the networks in which they are involved, we attempt to unify the cascades through which they are positioned. We concentrate on clarifying the precise mutual regulation between shoot apical meristem (SAM) and AM-related factors. Additionally, we examine challenges in elucidating AM formation mechanisms alongside opportunities provided by emerging omics approaches to identify AM-specific genes. By expanding our comprehension of the genetic and hormonal regulation of AM development, we can develop strategies to optimize crop production and address global food challenges effectively.

Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity

The escalating challenges posed by metal(loid) toxicity in agricultural ecosystems, exacerbated by rapid climate change and anthropogenic pressures, demand urgent attention. Soil contamination is a critical issue because it significantly impacts crop productivity. The widespread threat of metal(loid) toxicity can jeopardize global food security due to contaminated food supplies and pose environmental risks, contributing to soil and water pollution and thus impacting the whole ecosystem. In this context, plants have evolved complex mechanisms to combat metal(loid) stress. Amid the array of innovative approaches, omics, notably transcriptomics, proteomics, and metabolomics, have emerged as transformative tools, shedding light on the genes, proteins, and key metabolites involved in metal(loid) stress responses and tolerance mechanisms. These identified candidates hold promise for developing high-yielding crops with desirable agronomic traits. Computational biology tools like bioinformatics, biological databases, and analytical pipelines support these omics approaches by harnessing diverse information and facilitating the mapping of genotype-to-phenotype relationships under stress conditions. This review explores: (1) the multifaceted strategies that plants use to adapt to metal(loid) toxicity in their environment; (2) the latest findings in metal(loid)-mediated transcriptomics, proteomics, and metabolomics studies across various plant species; (3) the integration of omics data with artificial intelligence and high-throughput phenotyping; (4) the latest bioinformatics databases, tools and pipelines for single and/or multi-omics data integration; (5) the latest insights into stress adaptations and tolerance mechanisms for future outlooks; and (6) the capacity of omics advances for creating sustainable and resilient crop plants that can thrive in metal(loid)-contaminated environments.

Integrative Analyses of Transcriptomics and Metabolomics in Immune Response of Leguminivora glycinivorella Mats to Beauveria bassiana Infection

This study utilized Beauveria bassiana to infect Leguminivora glycinivorella, analyzed the effects on the transcriptome and metabolome, and further investigated the antibacterial function of L. glycinivorella. We performed transcriptome and metabolome sequencing on the L. glycinivorella infected with B. bassiana and its control groups, and performed a joint analysis of transcriptome and metabolome results. Upon screening, 4560 differentially expressed genes were obtained in the transcriptome and 71 differentially expressed metabolites were obtained in the metabolome. On this basis, further integration of the use of transcriptomics and metabonomics combined an analysis of common enrichments of pathways of which there were three. They were glutathione S-transferase (GSTs) genes, heat shock protein (HSP) genes, and cytochrome P450 (CYP450) genes. These three pathways regulate the transport proteins, such as ppars, and thus affect the digestion and absorption of sugars and fats, thus regulating the development of pests. The above conclusion indicates that B. bassiana can affect the sugar metabolism, lipid metabolism, and amino acid metabolism pathways of L. glycinivorella, and can consume the necessary energy, protein, and lipids of L. glycinivorella. The research on the immune response mechanism of pests against pathogens can provide an important scientific basis and target for the development of immunosuppressants. This study laid an information foundation for the application of entomogenous fungi to control soybean borer at the molecular level.

DataColor: unveiling biological data relationships through distinctive color mapping

In the era of rapid advancements in high-throughput omics technologies, the visualization of diverse data types with varying orders of magnitude presents a pressing challenge. To bridge this gap, we introduce DataColor, an all-encompassing software solution meticulously crafted to address this challenge. Our aim is to empower users with the ability to handle a wide array of data types through an assortment of tools, while simultaneously streamlining parameter selection for rapid insights and detailed enhancements. DataColor stands as a robust toolkit, encompassing 23 distinct tools coupled with over 600 parameters. The defining characteristic of this toolkit is its adept utilization of the color spectrum, allowing for the representation of data spanning diverse types and magnitudes. Through the integration of advanced algorithms encompassing data clustering, normalization, squarified layouts, and customizable parameters, DataColor unveils an abundance of insights that lay hidden within the intricate relationships embedded in the data. Whether you find yourself navigating the analysis of expansive datasets or embarking on the quest to visualize intricate patterns, DataColor stands as the comprehensive and potent solution. We extend the availability of DataColor to all users at no cost, accessible through the following link: https://github.com/frankgenome/DataColor.

Introducing single cell stereo-sequencing technology to transform the plant transcriptome landscape

Single cell RNA-sequencing (scRNA-seq) advancements have helped detect transcriptional heterogeneities in biological samples. However, scRNA-seq cannot currently provide high-resolution spatial transcriptome information or identify subcellular organs in biological samples. These limitations have led to the development of spatially enhanced-resolution omics-sequencing (Stereo-seq), which combines spatial information with single cell transcriptomics to address the challenges of scRNA-seq alone. In this review, we discuss the advantages of Stereo-seq technology. We anticipate that the application of such an integrated approach in plant research will advance our understanding of biological process in the plant transcriptomics era. We conclude with an outlook of how such integration will enhance crop improvement.

Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data

In recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data modalities as equals, neglecting the fact that the scRNA mode holds significantly richer information compared to the scATAC. This disregard hinders the model benefits from the insights derived from multiple modalities, compromising the overall clustering performance. To this end, we propose an effective multi-modal clustering model scEMC for parallel scRNA and Assay of Transposase Accessible Chromatin data. Concretely, we have devised a skip aggregation network to simultaneously learn global structural information among cells and integrate data from diverse modalities. To safeguard the quality of integrated cell representation against the influence stemming from sparse scATAC data, we connect the scRNA data with the aggregated representation via skip connection. Moreover, to effectively fit the real distribution of cells, we introduced a Zero Inflated Negative Binomial-based denoising autoencoder that accommodates corrupted data containing synthetic noise, concurrently integrating a joint optimization module that employs multiple losses. Extensive experiments serve to underscore the effectiveness of our model. This work contributes significantly to the ongoing exploration of cell subpopulations and tumor microenvironments, and the code of our work will be public at https://github.com/DayuHuu/scEMC.

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.

Long Non-Coding RNAs: Discoveries, Mechanisms, and Research Strategies in Seeds

Seeds provide nutrients for the embryo and allow for dormancy in stressed environments to better adapt the plant to its environment. In addition, seeds are an essential source of food for human survival and are the basis for the formation of food production and quality. Therefore, the research on the genetic mechanism of seed development and germination will provide a theoretical basis and technical support for the improvement of crop yield and quality. Recent studies have shown that long non-coding RNAs (lncRNAs) occupy a pivotal position in seed development and germination. In this review, we describe the key processes in seed biology and examine discoveries and insights made in seed lncRNA, with emphasis on lncRNAs that regulate seed biology through multiple mechanisms. Given that thousands of lncRNAs are present in the seed transcriptome, characterization has lagged far behind identification. We provide an overview of research strategies and approaches including some exciting new techniques that may uncover the function of lncRNAs in seed. Finally, we discuss the challenges facing the field and the opening questions. All in all, we hope to provide a clear perspective on discoveries of seed lncRNA by linking discoveries, mechanisms, and technologies.

Proteogenomics-based functional genome research: approaches, applications, and perspectives in plants

Proteogenomics (PG) integrates the proteome with the genome and transcriptome to refine gene models and annotation. Coupled with single-cell (SC) assays, PG effectively distinguishes heterogeneity among cell groups. Affiliating spatial information to PG reveals the high-resolution circuitry within SC atlases. Additionally, PG can investigate dynamic changes in protein-coding genes in plants across growth and development as well as stress and external stimulation, significantly contributing to the functional genome. Here we summarize existing PG research in plants and introduce the technical features of various methods. Combining PG with other omics, such as metabolomics and peptidomics, can offer even deeper insights into gene functions. We argue that the application of PG will represent an important font of foundational knowledge for plants.

Spatial transcriptomics drives a new era in plant research

The plant community lags far behind the animal and human fields concerning the application of single‐cell methodologies. This is primarily due to the challenges associated with plant tissue dissection and the limitations of the available technologies. However, recent advances in spatial transcriptomics enable the study of single‐cells derived from plant tissues from a spatial perspective. This technology is already successfully used to identify cell types, reconstruct cell‐fate lineages, and reveal cell‐to‐cell interactions. Future technological advancements will overcome the challenges in sample processing, data analysis, and the integration of multiple‐omics technologies. Thanks to spatial transcriptomics, we anticipate several plant research projects to significantly advance our understanding of critical aspects of plant biology.

Quantifying the impact of dynamic plant-environment interactions on metabolic regulation

A plant's genome encodes enzymes, transporters and many other proteins which constitute metabolism. Interactions of plants with their environment shape their growth, development and resilience towards adverse conditions. Although genome sequencing technologies and applications have experienced triumphantly rapid development during the last decades, enabling nowadays a fast and cheap sequencing of full genomes, prediction of metabolic phenotypes from genotype × environment interactions remains, at best, very incomplete. The main reasons are a lack of understanding of how different levels of molecular organisation depend on each other, and how they are constituted and expressed within a setup of growth conditions. Phenotypic plasticity, e.g., of the genetic model plant Arabidopsis thaliana, has provided important insights into plant-environment interactions and the resulting genotype x phenotype relationships. Here, we summarize previous and current findings about plant development in a changing environment and how this might be shaped and reflected in metabolism and its regulation. We identify current challenges in the study of plant development and metabolic regulation and provide an outlook of how methodological workflows might support the application of findings made in model systems to crops and their cultivation.

Metabolic pathways engineering for drought or/and heat tolerance in cereals

Drought (D) and heat (H) are the two major abiotic stresses hindering cereal crop growth and productivity, either singly or in combination (D/+H), by imposing various negative impacts on plant physiological and biochemical processes. Consequently, this decreases overall cereal crop production and impacts global food availability and human nutrition. To achieve global food and nutrition security vis-a-vis global climate change, deployment of new strategies for enhancing crop D/+H stress tolerance and higher nutritive value in cereals is imperative. This depends on first gaining a mechanistic understanding of the mechanisms underlying D/+H stress response. Meanwhile, functional genomics has revealed several stress-related genes that have been successfully used in target-gene approach to generate stress-tolerant cultivars and sustain crop productivity over the past decades. However, the fast-changing climate, coupled with the complexity and multigenic nature of D/+H tolerance suggest that single-gene/trait targeting may not suffice in improving such traits. Hence, in this review-cum-perspective, we advance that targeted multiple-gene or metabolic pathway manipulation could represent the most effective approach for improving D/+H stress tolerance. First, we highlight the impact of D/+H stress on cereal crops, and the elaborate plant physiological and molecular responses. We then discuss how key primary metabolism- and secondary metabolism-related metabolic pathways, including carbon metabolism, starch metabolism, phenylpropanoid biosynthesis, γ-aminobutyric acid (GABA) biosynthesis, and phytohormone biosynthesis and signaling can be modified using modern molecular biotechnology approaches such as CRISPR-Cas9 system and synthetic biology (Synbio) to enhance D/+H tolerance in cereal crops. Understandably, several bottlenecks hinder metabolic pathway modification, including those related to feedback regulation, gene functional annotation, complex crosstalk between pathways, and metabolomics data and spatiotemporal gene expressions analyses. Nonetheless, recent advances in molecular biotechnology, genome-editing, single-cell metabolomics, and data annotation and analysis approaches, when integrated, offer unprecedented opportunities for pathway engineering for enhancing crop D/+H stress tolerance and improved yield. Especially, Synbio-based strategies will accelerate the development of climate resilient and nutrient-dense cereals, critical for achieving global food security and combating malnutrition.

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.

Application of Multi-Perspectives in Tea Breeding and the Main Directions

Tea plants are an economically important crop and conducting research on tea breeding contributes to enhancing the yield and quality of tea leaves as well as breeding traits that satisfy the requirements of the public. This study reviews the current status of tea plants germplasm resources and their utilization, which has provided genetic material for the application of multi-omics, including genomics and transcriptomics in breeding. Various molecular markers for breeding were designed based on multi-omics, and available approaches in the direction of high yield, quality and resistance in tea plants breeding are proposed. Additionally, future breeding of tea plants based on single-cellomics, pangenomics, plant-microbe interactions and epigenetics are proposed and provided as references. This study aims to provide inspiration and guidance for advancing the development of genetic breeding in tea plants, as well as providing implications for breeding research in other crops.

Meta-learning shows great potential in plant disease recognition under few available samples

Plant diseases worsen the threat of food shortage with the growing global population, and disease recognition is the basis for the effective prevention and control of plant diseases. Deep learning has made significant breakthroughs in the field of plant disease recognition. Compared with traditional deep learning, meta-learning can still maintain more than 90% accuracy in disease recognition with small samples. However, there is no comprehensive review on the application of meta-learning in plant disease recognition. Here, we mainly summarize the functions, advantages, and limitations of meta-learning research methods and their applications for plant disease recognition with a few data scenarios. Finally, we outline several research avenues for utilizing current and future meta-learning in plant science. This review may help plant science researchers obtain faster, more accurate, and more credible solutions through deep learning with fewer labeled samples.

50 years of sperm cell isolations: from structural to omic studies

The fusion of male and female gametes is a fundamental process in the perpetuation and diversification of species. During the last 50 years, significant efforts have been made to isolate and characterize sperm cells from flowering plants, and to identify how these cells interact with female gametes to achieve double fertilization. The first techniques and analytical approaches not only provided structural and biochemical characterizations of plant sperm cells but also paved the way for in vitro fertilization studies. Further technological advances then led to unique insights into sperm biology at transcriptomic, proteomic and epigenetic level. Starting with a historical overview of sperm cell isolation techniques, we provide examples of how these contributed to create our current knowledge of sperm cell biology, and point out remaining challenges.

Single-cell analysis opens a goldmine for plant functional studies

Functional studies in biology require the identification of genes, regulatory elements, and networks, followed by a deep understanding of how they orchestrate to specify cell types, mediate signaling, and respond to internal and external cues over evolutionary timescales. Advances in single-cell analysis have enabled biologists to tackle these questions at the resolution of the individual cell. Here, we highlight recent studies in plants that have embraced single-cell analyses to facilitate functional studies. This review will provide guidance and perspectives for incorporating these advanced approaches in plant research for the coming decades.

Deciphering Macromolecular Interactions Involved in Abiotic Stress Signaling: A Review of Bioinformatics Analysis

Plant functioning and responses to abiotic stresses largely involve regulations at the transcriptomic level via complex interactions of signal molecules, signaling cascades, and regulators. Nevertheless, all the signaling networks involved in responses to abiotic stresses have not yet been fully established. The in-depth analysis of transcriptomes in stressed plants has become a relevant state-of-the-art methodology to study these regulations and signaling pathways that allow plants to cope with or attempt to survive abiotic stresses. The plant science and molecular biology community has developed databases about genes, proteins, protein-protein interactions, protein-DNA interactions and ontologies, which are valuable sources of knowledge for deciphering such regulatory and signaling networks. The use of these data and the development of bioinformatics tools help to make sense of transcriptomic data in specific contexts, such as that of abiotic stress signaling, using functional biological approaches. The aim of this chapter is to present and assess some of the essential online tools and resources that will allow novices in bioinformatics to decipher transcriptomic data in order to characterize the cellular processes and functions involved in abiotic stress responses and signaling. The analysis of case studies further describes how these tools can be used to conceive signaling networks on the basis of transcriptomic data. In these case studies, particular attention was paid to the characterization of abiotic stress responses and signaling related to chemical and xenobiotic stressors.

Plant Nuclei Isolation for Single-Nucleus RNA Sequencing

Transcriptome profiling has been significantly hampered by the heterogeneity among individual cells within a tissue or an organ. Recent advances in single cell transcriptome profiling have significantly advanced our understanding of the transcriptome. However, plant single-cell RNA sequencing (scRNA-seq) relies on the isolation of protoplasts, which is not only impossible for many cell types but also induces acute wounding responses. To solve these problems, single-nucleus RNA sequencing (snRNA-seq) has been applied to plant research, in which nuclei are isolated and subject to encapsulation and profiling. Compared with scRNA-seq, snRNA-seq can be applied to a wider range of tissue types and plant species. Nevertheless, fewer transcripts can be obtained from each nucleus than each protoplast. In this chapter, we describe a detailed and general protocol to prepare nuclei from plant tissues that are ready for subsequent library construction and high-throughput sequencing.

Single-cell RNA-seq reveals fate determination control of an individual fibre cell initiation in cotton (Gossypium hirsutum)

Cotton fibre is a unicellular seed trichome, and lint fibre initials per seed as a factor determines fibre yield. However, the mechanisms controlling fibre initiation from ovule epidermis are not understood well enough. Here, with single-cell RNA sequencing (scRNA-seq), a total of 14 535 cells were identified from cotton ovule outer integument of Xu142_LF line at four developmental stages (1.5, 1, 0.5 days before anthesis and the day of anthesis). Three major cell types, fibre, non-fibre epidermis and outer pigment layer were identified and then verified by RNA in situ hybridization. A comparative analysis on scRNA-seq data between Xu142 and its fibreless mutant Xu142 fl further confirmed fibre cluster definition. The developmental trajectory of fibre cell was reconstructed, and fibre cell was identified differentiated at 1 day before anthesis. Gene regulatory networks at four stages revealed the spatiotemporal pattern of core transcription factors, and MYB25-like and HOX3 were demonstrated played key roles as commanders in fibre differentiation and tip-biased diffuse growth respectively. A model for early development of a single fibre cell was proposed here, which sheds light on further deciphering mechanism of plant trichome and the improvement of cotton fibre yield.

The egg cell is preferentially fertilized in Arabidopsis double fertilization

In flowering plants (angiosperms), fertilization of the egg cell by one sperm cell produces an embryo, whereas fusion of a second sperm cell with the central cell generates the endosperm. In most angiosperms like Arabidopsis, a pollen grain contains two isomorphic sperm cells required for this double fertilization process. A long-standing unsolved question is whether the two fertilization events have any preference. A tool to address this question is the usage of the cyclin-dependent kinase a1 (cdka;1) mutant pollen, which produces a single sperm-like cell (SLC). Here, we first adopt a complementation-based fluorescence-labeling method to successfully separate and collect cdka;1 mutant pollen containing a single SLC. Single-cell RNA-sequencing analysis revealed that cdka;1 SLCs show a gene expression profile highly similar to that of sperm cells and not to the generative cell, precursor of the two sperm cells. Pollination assays using a limited number of cdka;1 mutant pollen revealed that in 98.2% of the ovules, single fertilization of the egg cell occurred. Pollination of pistils with excessive cdka;1 mutant pollen allowed the delivery of a second SLC via fertilization recovery, which fertilized the central cell, resulting in 20.7% double-fertilized ovules. This indicates that cdka;1 SLCs are able to fertilize both the egg and the central cell. Taken together, our findings have answered a long-standing question and support that preferential fertilization of the egg cell is evident in Arabidopsis.