Dynamics of Gene Expression in Single Root Cells of Arabidopsis thaliana

An optimized FACS-free single-nucleus RNA sequencing (snRNA-seq) method for plant science research

Recently, single-cell RNA sequencing (scRNA-seq) provides unprecedented power for accurately understanding gene expression regulatory mechanisms. However, scRNA-seq studies have limitations in plants, due to difficulty in protoplast isolation that requires enzymatic digestion of the cell walls from various plant tissues. Therefore, to overcome this problem, we developed a nuclei isolation approach that does not rely on Fluorescence Activated Cell Sorting (FACS). We validated the robustness of the FACS-free single-nucleus RNA sequencing (snRNA-seq) methodology in mature Arabidopsis plant tissue by comparing it to scRNA-seq results based on protoplasts extracted from the same batch of leaf materials. Sequencing results demonstrated the high quality of snRNA-seq data, as well as its utility in cell type classification and marker gene identification. This approach also showed several advantages, including the ability to use frozen samples, taking less suspension preparation time, and reducing biased cellular coverage and dissociation-induced transcriptional artifacts. Surprisingly, snRNA-seq detected two epidermal pavement cell clusters, while scRNA-seq only had one. Furthermore, we hypothesized that these two epidermal cells represent the top and lower epidermis based on differences in expression patterns of cluster-specific expressed genes. In summary, this study has advanced the application of snRNA-seq in Arabidopsis leaves and confirmed the advantages of snRNA-seq in plant research.

Gene Regulatory Network Modeling Using Single-Cell Multi-Omics in Plants

Single-cell multi-omics technology can be applied to plant cells to characterize gene expression and open chromatin regions in individual cells. In this chapter, we describe a computational pipeline for the analysis of single-cell data to construct gene regulatory networks. The major steps of this pipeline include the following: (1) normalize and integrate scRNA-seq and scATAC-seq data (2) identify cluster maker genes (3) perform motif finding for selected marker genes, and (4) identify regulatory networks with machine learning. The pipeline has been tested using data from the model species Arabidopsis and is generally applicable to other plant and animal species to characterize regulatory networks using single-cell multi-omics data.

Analysis of Chromatin Accessibility, Histone Modifications, and Transcriptional States in Specific Cell Types Using Flow Cytometry

The past two decades in biomedical research have experienced an explosion of cell type-specific and single-cell studies, especially concerning the concomitant dissection of regulatory and transcriptional landscapes of those under investigation. Additionally, leveraging next-generation sequencing (NGS) platforms efforts have been undertaken to evaluate the effects of chromatin accessibility, histone modifications, or even transcription factor binding sites. We have shown that Fluorescence-Activated Nuclear Sorting (FANS) is an effective means to characterize the transcriptomes of nuclei from different tissues. In light of our own technical and experimental developments, we extend this effort to combine FACS/FANS with Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), Chromatin Immunoprecipitation sequencing (ChIP-seq), and RNA sequencing (RNA-seq) for profiling individual cell types according to their chromatin and transcriptional states.

Single-cell transcriptome reveals dominant subgenome expression and transcriptional response to heat stress in Chinese cabbage

BACKGROUND

Chinese cabbage (Brassica rapa ssp. pekinensis) experienced a whole-genome triplication event and thus has three subgenomes: least fractioned, medium fractioned, and most fractioned subgenome. Environmental changes affect leaf development, which in turn influence the yield. To improve the yield and resistance to different climate scenarios, a comprehensive understanding of leaf development is required including insights into the full diversity of cell types and transcriptional networks underlying their specificity.

RESULTS

Here, we generate the transcriptional landscape of Chinese cabbage leaf at single-cell resolution by performing single-cell RNA sequencing of 30,000 individual cells. We characterize seven major cell types with 19 transcriptionally distinct cell clusters based on the expression of the reported marker genes. We find that genes in the least fractioned subgenome are predominantly expressed compared with those in the medium and most fractioned subgenomes in different cell types. Moreover, we generate a single-cell transcriptional map of leaves in response to high temperature. We find that heat stress not only affects gene expression in a cell type-specific manner but also impacts subgenome dominance.

CONCLUSIONS

Our study highlights the transcriptional networks in different cell types and provides a better understanding of transcriptional regulation during leaf development and transcriptional response to heat stress in Chinese cabbage.

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.

Chromatin accessibility illuminates single-cell regulatory dynamics of rice root tips

BACKGROUND

Root development and function have central roles in plant adaptation to the environment. The modification of root traits has additionally been a major driver of crop performance since the green revolution; however, the molecular underpinnings and the regulatory programmes defining root development and response to environmental stress remain largely unknown. Single-cell reconstruction of gene regulatory programmes provides an important tool to understand the cellular phenotypic variation in complex tissues and their response to endogenous and environmental stimuli. While single-cell transcriptomes of several plant organs have been elucidated, the underlying chromatin landscapes associated with cell type-specific gene expression remain largely unexplored.

RESULTS

To comprehensively delineate chromatin accessibility during root development of an important crop, we applied single-cell ATAC-seq (scATAC-seq) to 46,758 cells from rice root tips under normal and heat stress conditions. Our data revealed cell type-specific accessibility variance across most of the major cell types and allowed us to identify sets of transcription factors which associate with accessible chromatin regions (ACRs). Using root hair differentiation as a model, we demonstrate that chromatin and gene expression dynamics during cell type differentiation correlate in pseudotime analyses. In addition to developmental trajectories, we describe chromatin responses to heat and identify cell type-specific accessibility changes to this key environmental stimulus.

CONCLUSIONS

We report chromatin landscapes during rice root development at single-cell resolution. Our work provides a framework for the integrative analysis of regulatory dynamics in this important crop organ at single-cell resolution.

Cell-specific pathways recruited for symbiotic nodulation in the Medicago truncatula legume

Medicago truncatula is a model legume species that has been studied for decades to understand the symbiotic relationship between legumes and soil bacteria collectively named rhizobia. This symbiosis called nodulation is initiated in roots with the infection of root hair cells by the bacteria, as well as the initiation of nodule primordia from root cortical, endodermal, and pericycle cells, leading to the development of a new root organ, the nodule, where bacteria fix and assimilate the atmospheric dinitrogen for the benefit of the plant. Here, we report the isolation and use of the nuclei from mock and rhizobia-inoculated roots for the single nuclei RNA-seq (sNucRNA-seq) profiling to gain a deeper understanding of early responses to rhizobial infection in Medicago roots. A gene expression map of the Medicago root was generated, comprising 25 clusters, which were annotated as specific cell types using 119 Medicago marker genes and orthologs to Arabidopsis cell-type marker genes. A focus on root hair, cortex, endodermis, and pericycle cell types, showing the strongest differential regulation in response to a short-term (48 h) rhizobium inoculation, revealed not only known genes and functional pathways, validating the sNucRNA-seq approach, but also numerous novel genes and pathways, allowing a comprehensive analysis of early root symbiotic responses at a cell type-specific level.

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.

Plant synthetic biology innovations for biofuels and bioproducts

Plant-based biosynthesis of fuels, chemicals, and materials promotes environmental sustainability, which includes decreases in greenhouse gas emissions, water pollution, and loss of biodiversity. Advances in plant synthetic biology (synbio) should improve precision and efficacy of genetic engineering for sustainability. Applicable synbio innovations include genome editing, gene circuit design, synthetic promoter development, gene stacking technologies, and the design of environmental sensors. Moreover, recent advancements in developing spatially resolved and single-cell omics contribute to the discovery and characterization of cell-type-specific mechanisms and spatiotemporal gene regulations in distinct plant tissues for the expression of cell- and tissue-specific genes, resulting in improved bioproduction. This review highlights recent plant synbio progress and new single-cell molecular profiling towards sustainable biofuel and biomaterial production.

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.

Single-cell technologies: From research to application

In recent years, more and more single-cell technologies have been developed. A vast amount of single-cell omics data has been generated by large projects, such as the Human Cell Atlas, the Mouse Cell Atlas, the Mouse RNA Atlas, the Mouse ATAC Atlas, and the Plant Cell Atlas. Based on these single-cell big data, thousands of bioinformatics algorithms for quality control, clustering, cell-type annotation, developmental inference, cell-cell transition, cell-cell interaction, and spatial analysis are developed. With powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence, the molecular landscape at the single-cell level can be revealed. With spatial transcriptomics and single-cell multi-omics, even the spatial dynamic multi-level regulatory mechanisms can be deciphered. Such single-cell technologies have many successful applications in oncology, assisted reproduction, embryonic development, and plant breeding. We not only review the experimental and bioinformatics methods for single-cell research, but also discuss their applications in various fields and forecast the future directions for single-cell technologies. We believe that spatial transcriptomics and single-cell multi-omics will become the next booming business for mechanism research and commercial industry.

MINI-EX: Integrative inference of single-cell gene regulatory networks in plants

Multicellular organisms, such as plants, are characterized by highly specialized and tightly regulated cell populations, establishing specific morphological structures and executing distinct functions. Gene regulatory networks (GRNs) describe condition-specific interactions of transcription factors (TFs) regulating the expression of target genes, underpinning these specific functions. As efficient and validated methods to identify cell-type-specific GRNs from single-cell data in plants are lacking, limiting our understanding of the organization of specific cell types in both model species and crops, we developed MINI-EX (Motif-Informed Network Inference based on single-cell EXpression data), an integrative approach to infer cell-type-specific networks in plants. MINI-EX uses single-cell transcriptomic data to define expression-based networks and integrates TF motif information to filter the inferred regulons, resulting in networks with increased accuracy. Next, regulons are assigned to different cell types, leveraging cell-specific expression, and candidate regulators are prioritized using network centrality measures, functional annotations, and expression specificity. This embedded prioritization strategy offers a unique and efficient means to unravel signaling cascades in specific cell types controlling a biological process of interest. We demonstrate the stability of MINI-EX toward input data sets with low number of cells and its robustness toward missing data, and show that it infers state-of-the-art networks with a better performance compared with other related single-cell network tools. MINI-EX successfully identifies key regulators controlling root development in Arabidopsis and rice, leaf development in Arabidopsis, and ear development in maize, enhancing our understanding of cell-type-specific regulation and unraveling the roles of different regulators controlling the development of specific cell types in plants.

PlantPhoneDB: A manually curated pan-plant database of ligand-receptor pairs infers cell-cell communication

Ligand-receptor pairs play important roles in cell-cell communication for multicellular organisms in response to environmental cues. Recently, the emergence of single-cell RNA-sequencing (scRNA-seq) provides unprecedented opportunities to investigate cellular communication based on ligand-receptor expression. However, so far, no reliable ligand-receptor interaction database is available for plant species. In this study, we developed PlantPhoneDB (https://jasonxu.shinyapps.io/PlantPhoneDB/), a pan-plant database comprising a large number of high-confidence ligand-receptor pairs manually curated from seven resources. Also, we developed a PlantPhoneDB R package, which not only provided optional four scoring approaches that calculate interaction scores of ligand-receptor pairs between cell types but also provided visualization functions to present analysis results. At the PlantPhoneDB web interface, the processed datasets and results can be searched, browsed, and downloaded. To uncover novel cell-cell communication events in plants, we applied the PlantPhoneDB R package on GSE121619 dataset to infer significant cell-cell interactions of heat-shocked root cells in Arabidopsis thaliana. As a result, the PlantPhoneDB predicted the actively communicating AT1G28290-AT2G14890 ligand-receptor pair in atrichoblast-cortex cell pair in Arabidopsis thaliana. Importantly, the downstream target genes of this ligand-receptor pair were significantly enriched in the ribosome pathway, which facilitated plants adapting to environmental changes. In conclusion, PlantPhoneDB provided researchers with integrated resources to infer cell-cell communication from scRNA-seq datasets.

Single-cell transcriptome of Nepeta tenuifolia leaves reveal differentiation trajectories in glandular trichomes

The peltate glandular trichomes (PGTs) on Nepeta tenuifolia leaves can secrete and store bioactive essential oils. ScRNA-seq is a powerful tool for uncovering heterogeneous cells and exploring the development and differentiation of specific cells. Due to leaves rich in PGTs, the young leaves were used to isolated protoplasts and successfully captured 33,254 protoplasts for sequencing purposes. After cell type annotation, all the cells were partitioned into six broad populations with 19 clusters. Cells from PGTs were identified based on the expression patterns of trichome-specific genes, monoterpene biosynthetic genes, and metabolic analysis of PGT secretions. The developmental trajectories of PGTs were delineated by pseudotime analysis. Integrative analysis of scRNA-seq data from N. tenuifolia leaves and Arabidopsis thaliana shoot revealed that PGTs were specific to N. tenuifolia. Thus, our results provide a promising basis for exploring cell development and differentiation in plants, especially glandular trichome initiation and development.

Global transcriptome and coexpression network analyses reveal cultivar-specific molecular signatures associated with different rooting depth responses to drought stress in potato

Potato is one of the most important vegetable crops worldwide. Its growth, development and ultimately yield is hindered by drought stress condition. Breeding and selection of deep-rooted and drought-tolerant potato varieties has become a prime approach for improving the yield and quality of potato (Solanum tuberosum L.) in arid and semiarid areas. A comprehensive understanding of root development-related genes has enabled scientists to formulate strategies to incorporate them into breeding to improve complex agronomic traits and provide opportunities for the development of stress tolerant germplasm. Root response to drought stress is an intricate process regulated through complex transcriptional regulatory network. To understand the rooting depth and molecular mechanism, regulating root response to drought stress in potato, transcriptome dynamics of roots at different stages of drought stress were analyzed in deep (C119) and shallow-rooted (C16) cultivars. Stage-specific expression was observed for a significant proportion of genes in each cultivar and it was inferred that as compared to C16 (shallow-rooted), approximately half of the genes were differentially expressed in deep-rooted cultivar (C119). In C16 and C119, 11 and 14 coexpressed gene modules, respectively, were significantly associated with physiological traits under drought stress. In a comparative analysis, some modules were different between the two cultivars and were associated with differential response to specific drought stress stage. Transcriptional regulatory networks were constructed, and key components determining rooting depth were identified. Through the results, we found that rooting depth (shallow vs deep) was largely determined by plant-type, cell wall organization or biogenesis, hemicellulose metabolic process, and polysaccharide metabolic process. In addition, candidate genes responding to drought stress were identified in deep (C119) and shallow (C16) rooted potato varieties. The results of this study will be a valuable source for further investigations on the role of candidate gene(s) that affect rooting depth and drought tolerance mechanisms in potato.

Comparative transcriptomics analysis of developing peanut (Arachis hypogaea L.) pods reveals candidate genes affecting peanut seed size

Pod size is one of the most important agronomic features of peanuts, which directly affects peanut yield. Studies on the regulation mechanism underpinning pod size in cultivated peanuts remain hitherto limited compared to model plant systems. To better understand the molecular elements that underpin peanut pod development, we conducted a comprehensive analysis of chronological transcriptomics during pod development in four peanut accessions with similar genetic backgrounds, but varying pod sizes. Several plant transcription factors, phytohormones, and the mitogen-activated protein kinase (MAPK) signaling pathways were significantly enriched among differentially expressed genes (DEGs) at five consecutive developmental stages, revealing an eclectic range of candidate genes, including PNC, YUC, and IAA that regulate auxin synthesis and metabolism, CYCD and CYCU that regulate cell differentiation and proliferation, and GASA that regulates seed size and pod elongation via gibberellin pathway. It is plausible that MPK3 promotes integument cell division and regulates mitotic activity through phosphorylation, and the interactions between these genes form a network of molecular pathways that affect peanut pod size. Furthermore, two variant sites, GCP4 and RPPL1, were identified which are stable at the QTL interval for seed size attributes and function in plant cell tissue microtubule nucleation. These findings may facilitate the identification of candidate genes that regulate pod size and impart yield improvement in cultivated peanuts.

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.

Current status and trends in forest genomics

Forests are not only the most predominant of the Earth's terrestrial ecosystems, but are also the core supply for essential products for human use. However, global climate change and ongoing population explosion severely threatens the health of the forest ecosystem and aggravtes the deforestation and forest degradation. Forest genomics has great potential of increasing forest productivity and adaptation to the changing climate. In the last two decades, the field of forest genomics has advanced quickly owing to the advent of multiple high-throughput sequencing technologies, single cell RNA-seq, clustered regularly interspaced short palindromic repeats (CRISPR)-mediated genome editing, and spatial transcriptomes, as well as bioinformatics analysis technologies, which have led to the generation of multidimensional, multilayered, and spatiotemporal gene expression data. These technologies, together with basic technologies routinely used in plant biotechnology, enable us to tackle many important or unique issues in forest biology, and provide a panoramic view and an integrative elucidation of molecular regulatory mechanisms underlying phenotypic changes and variations. In this review, we recapitulated the advancement and current status of 12 research branches of forest genomics, and then provided future research directions and focuses for each area. Evidently, a shift from simple biotechnology-based research to advanced and integrative genomics research, and a setup for investigation and interpretation of many spatiotemporal development and differentiation issues in forest genomics have just begun to emerge.

Investigation of Brassica and its relative genomes in the post-genomics era

The Brassicaceae family includes many economically important crop species, as well as cosmopolitan agricultural weed species. In addition, Arabidopsis thaliana, a member of this family, is used as a molecular model plant species. The genus Brassica is mesopolyploid, and the genus comprises comparatively recently originated tetrapolyploid species. With these characteristics, Brassicas have achieved the commonly accepted status of model organisms for genomic studies. This paper reviews the rapid research progress in the Brassicaceae family from diverse omics studies, including genomics, transcriptomics, epigenomics, and three-dimensional (3D) genomics, with a focus on cultivated crops. The morphological plasticity of Brassicaceae crops is largely due to their highly variable genomes. The origin of several important Brassicaceae crops has been established. Genes or loci domesticated or contributing to important traits are summarized. Epigenetic alterations and 3D structures have been found to play roles in subgenome dominance, either in tetraploid Brassica species or their diploid ancestors. Based on this progress, we propose future directions and prospects for the genomic investigation of Brassicaceae crops.

Root Cap to Soil Interface: A Driving Force Toward Plant Adaptation and Development

Land plants have developed robust roots to grow in diverse soil ecosystems. The distal end of the root tip has a specialized organ called the 'root cap'. The root cap assists the roots in penetrating the ground, absorbing water and minerals, avoiding heavy metals and regulating the rhizosphere microbiota. Furthermore, root-cap-derived auxin governs the lateral root patterning and directs root growth under varying soil conditions. The root cap formation is hypothesized as one of the key innovations during root evolution. Morphologically diversified root caps in early land plant lineage and later in angiosperms aid in improving the adaptation of roots and, thereby, plants in diverse soil environments. This review article presents a retrospective view of the root cap's important morphological and physiological characteristics for the root-soil interaction and their response toward various abiotic and biotic stimuli. Recent single-cell RNAseq data shed light on root cap cell-type-enriched genes. We compiled root cap cell-type-enriched genes from Arabidopsis, rice, maize and tomato and analyzed their transcription factor (TF) binding site enrichment. Further, the putative gene regulatory networks derived from root-cap-enriched genes and their TF regulators highlight the species-specific biological functions of root cap genes across the four plant species.

Transcriptional analysis of Ceratopteris richardii young sporophyte reveals conservation of stem cell factors in the root apical meristem

Gene expression in roots has been assessed in different plant species in studies ranging from complete organs to specific cell layers, and more recently at the single cell level. While certain genes or functional categories are expressed in the root of all or most plant species, lineage-specific genes have also been discovered. An increasing amount of transcriptomic data is available for angiosperms, while a limited amount of data is available for ferns, and few studies have focused on fern roots. Here, we present a de novo transcriptome assembly from three different parts of the Ceratopteris richardii young sporophyte. Differential gene expression analysis of the root tip transcriptional program showed an enrichment of functional categories related to histogenesis and cell division, indicating an active apical meristem. Analysis of a diverse set of orthologous genes revealed conserved expression in the root meristem, suggesting a preserved role for different developmental roles in this tissue, including stem cell maintenance. The reconstruction of evolutionary trajectories for ground tissue specification genes suggests a high degree of conservation in vascular plants, but not for genes involved in root cap development, showing that certain genes are absent in Ceratopteris or have intricate evolutionary paths difficult to track. Overall, our results suggest different processes of conservation and divergence of genes involved in root development.

Practical Considerations for Single-Cell Genomics

The single-cell revolution in the field of genomics is in full bloom, with clever new molecular biology tricks appearing regularly that allow researchers to explore new modalities or scale up their projects to millions of cells and beyond. Techniques abound to measure RNA expression, DNA alterations, protein abundance, chromatin accessibility, and more, all with single-cell resolution and often in combination. Despite such a rapidly changing technology landscape, there are several fundamental principles that are applicable to the majority of experimental workflows to help users avoid pitfalls and exploit the advantages of the chosen platform. In this overview article, we describe a variety of popular single-cell genomics technologies and address some common questions pertaining to study design, sample preparation, quality control, and sequencing strategy. As the majority of relevant publications currently revolve around single-cell RNA-seq, we will prioritize this genomics modality in our discussion. © 2022 Wiley Periodicals LLC.

Transcriptional landscapes of de novo root regeneration from detached Arabidopsis leaves revealed by time-lapse and single-cell RNA sequencing analyses

Detached Arabidopsis thaliana leaves can regenerate adventitious roots, providing a platform for studying de novo root regeneration (DNRR). However, the comprehensive transcriptional framework of DNRR remains elusive. Here, we provide a high-resolution landscape of transcriptome reprogramming from wound response to root organogenesis in DNRR and show key factors involved in DNRR. Time-lapse RNA sequencing (RNA-seq) of the entire leaf within 12 h of leaf detachment revealed rapid activation of jasmonate, ethylene, and reactive oxygen species (ROS) pathways in response to wounding. Genetic analyses confirmed that ethylene and ROS may serve as wound signals to promote DNRR. Next, time-lapse RNA-seq within 5 d of leaf detachment revealed the activation of genes involved in organogenesis, wound-induced regeneration, and resource allocation in the wounded region of detached leaves during adventitious rooting. Genetic studies showed that BLADE-ON-PETIOLE1/2, which control aboveground organs, PLETHORA3/5/7, which control root organogenesis, and ETHYLENE RESPONSE FACTOR115, which controls wound-induced regeneration, are involved in DNRR. Furthermore, single-cell RNA-seq data revealed gene expression patterns in the wounded region of detached leaves during adventitious rooting. Overall, our study not only provides transcriptome tools but also reveals key factors involved in DNRR from detached Arabidopsis leaves.

A combinatorial indexing strategy for low-cost epigenomic profiling of plant single cells

Understanding how cis-regulatory elements facilitate gene expression is a key question in biology. Recent advances in single-cell genomics have led to the discovery of cell-specific chromatin landscapes that underlie transcription programs in animal models. However, the high equipment and reagent costs of commercial systems limit their applications for many laboratories. In this study, we developed a combinatorial index and dual PCR barcode strategy to profile the Arabidopsis thaliana root single-cell epigenome without any specialized equipment. We generated chromatin accessibility profiles for 13 576 root nuclei with an average of 12 784 unique Tn5 integrations per cell. Integration of the single-cell assay for transposase-accessible chromatin sequencing and RNA sequencing data sets enabled the identification of 24 cell clusters with unique transcription, chromatin, and cis-regulatory signatures. Comparison with single-cell data generated using the commercial microfluidic platform from 10X Genomics revealed that this low-cost combinatorial index method is capable of unbiased identification of cell-type-specific chromatin accessibility. We anticipate that, by removing cost, instrumentation, and other technical obstacles, this method will be a valuable tool for routine investigation of single-cell epigenomes and provide new insights into plant growth and development and plant interactions with the environment.

scCloudMine: A cloud-based app for visualization, comparison, and exploration of single-cell transcriptomic data

scCloudMine is a cloud-based application for visualization, comparison, and exploration of single-cell transcriptome data. It does not require an on-site, high-power computing server, installation, or associated expertise and expense. Users upload their own or publicly available scRNA-seq datasets after pre-processing for visualization using a web browser. The data can be viewed in two color modes-Cluster, representing cell identity, and Values, showing levels of expression-and data can be queried using keywords or gene identification number(s). Using the app to compare studies, we determined that some genes frequently used as cell-type markers are in fact study specific. The apparent cell-specific expression of PHO1;H3 differed between GFP-tagging and scRNA-seq studies. Some phosphate transporter genes were induced by protoplasting, but they retained cell specificity, suggesting that cell-specific responses to stress (i.e., protoplasting) can occur. Examination of the cell specificity of hormone response genes revealed that 132 hormone-responsive genes display restricted expression and that the jasmonate response gene TIFY8 is expressed in endodermal cells, in contrast to previous reports. It also appears that JAZ repressors have cell-type-specific functions. These features identified using scCloudMine highlight the need for resources to enable biological researchers to compare their datasets of interest under a variety of parameters. scCloudMine enables researchers to form new hypotheses and perform comparative studies and allows for the easy re-use of data from this emerging technology by a wide variety of users who may not have access or funding for high-performance on-site computing and support.

Laser microirradiation as a versatile system for probing protein recruitment and protein-protein interactions at DNA lesions in plants

Plant protoplasts are generated by treatment with digestion enzymes, producing plant cells devoid of the cell wall and competent for efficient polyethylene glycol mediated transformation. This way fluorescently tagged proteins can be introduced to the protoplasts creating an excellent system to probe the localization and function of uncharacterized plant proteins in vivo. We implement the method of laser microirradiation to generate DNA lesions in Arabidopsis thaliana, which enables monitoring the recruitment and dynamics of the DNA repair factors as well as bimolecular fluorescence complementation assay to test transient, conditional interactions of proteins directly at sites of DNA damage. We demonstrate that laser microirradiation in protoplasts yields a physiological cellular response to DNA lesions, based on proliferating cell nuclear antigen (PCNA) redistribution in the nucleus and show that factors involved in DNA repair, such as MRE11 or PCNA are recruited to induced DNA lesions. This technique is relatively easy to adopt by other laboratories and extends the current toolkit of methods aimed to understand the details of DNA damage response in plants. The presented method is fast, flexible and facilitates work with different mutant backgrounds or even different species, extending the utility of the system.

Increase Crop Resilience to Heat Stress Using Omic Strategies

Varieties of various crops with high resilience are urgently needed to feed the increased population in climate change conditions. Human activities and climate change have led to frequent and strong weather fluctuation, which cause various abiotic stresses to crops. The understanding of crops' responses to abiotic stresses in different aspects including genes, RNAs, proteins, metabolites, and phenotypes can facilitate crop breeding. Using multi-omics methods, mainly genomics, transcriptomics, proteomics, metabolomics, and phenomics, to study crops' responses to abiotic stresses will generate a better, deeper, and more comprehensive understanding. More importantly, multi-omics can provide multiple layers of information on biological data to understand plant biology, which will open windows for new opportunities to improve crop resilience and tolerance. However, the opportunities and challenges coexist. Interpretation of the multidimensional data from multi-omics and translation of the data into biological meaningful context remained a challenge. More reasonable experimental designs starting from sowing seed, cultivating the plant, and collecting and extracting samples were necessary for a multi-omics study as the first step. The normalization, transformation, and scaling of single-omics data should consider the integration of multi-omics. This review reports the current study of crops at abiotic stresses in particular heat stress using omics, which will help to accelerate crop improvement to better tolerate and adapt to climate change.

Machine learning: its challenges and opportunities in plant system biology

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.

Coexpression reveals conserved gene programs that co-vary with cell type across kingdoms

What makes a mouse a mouse, and not a hamster? Differences in gene regulation between the two organisms play a critical role. Comparative analysis of gene coexpression networks provides a general framework for investigating the evolution of gene regulation across species. Here, we compare coexpression networks from 37 species and quantify the conservation of gene activity 1) as a function of evolutionary time, 2) across orthology prediction algorithms, and 3) with reference to cell- and tissue-specificity. We find that ancient genes are expressed in multiple cell types and have well conserved coexpression patterns, however they are expressed at different levels across cell types. Thus, differential regulation of ancient gene programs contributes to transcriptional cell identity. We propose that this differential regulation may play a role in cell diversification in both the animal and plant kingdoms.

scESI: evolutionary sparse imputation for single-cell transcriptomes from nearest neighbor cells

The ubiquitous dropout problem in single-cell RNA sequencing technology causes a large amount of data noise in the gene expression profile. For this reason, we propose an evolutionary sparse imputation (ESI) algorithm for single-cell transcriptomes, which constructs a sparse representation model based on gene regulation relationships between cells. To solve this model, we design an optimization framework based on nondominated sorting genetics. This framework takes into account the topological relationship between cells and the variety of gene expression to iteratively search the global optimal solution, thereby learning the Pareto optimal cell-cell affinity matrix. Finally, we use the learned sparse relationship model between cells to improve data quality and reduce data noise. In simulated datasets, scESI performed significantly better than benchmark methods with various metrics. By applying scESI to real scRNA-seq datasets, we discovered scESI can not only further classify the cell types and separate cells in visualization successfully but also improve the performance in reconstructing trajectories differentiation and identifying differentially expressed genes. In addition, scESI successfully recovered the expression trends of marker genes in stem cell differentiation and can discover new cell types and putative pathways regulating biological processes.

Identification of new marker genes from plant single-cell RNA-seq data using interpretable machine learning methods

An essential step in the analysis of single-cell RNA sequencing data is to classify cells into specific cell types using marker genes. In this study, we have developed a machine learning pipeline called single-cell predictive marker (SPmarker) to identify novel cell-type marker genes in the Arabidopsis root. Unlike traditional approaches, our method uses interpretable machine learning models to select marker genes. We have demonstrated that our method can: assign cell types based on cells that were labelled using published methods; project cell types identified by trajectory analysis from one data set to other data sets; and assign cell types based on internal GFP markers. Using SPmarker, we have identified hundreds of new marker genes that were not identified before. As compared to known marker genes, the new marker genes have more orthologous genes identifiable in the corresponding rice single-cell clusters. The new root hair marker genes also include 172 genes with orthologs expressed in root hair cells in five non-Arabidopsis species, which expands the number of marker genes for this cell type by 35-154%. Our results represent a new approach to identifying cell-type marker genes from scRNA-seq data and pave the way for cross-species mapping of scRNA-seq data in plants.

Strategies and Bottlenecks in Hexaploid Wheat to Mobilize Soil Iron to Grains

Our knowledge of iron (Fe) uptake and mobilization in plants is mainly based on Arabidopsis and rice. Although multiple players of Fe homeostasis have been elucidated, there is a significant gap in our understanding of crop species, such as wheat. It is, therefore, imperative not only to understand the different hurdles for Fe enrichment in tissues but also to address specifically the knowns/unknowns involved in the plausible mechanism of Fe sensing, signaling, transport, and subsequent storage in plants. In the present review, a unique perspective has been described in light of recent knowledge generated in wheat, an economically important crop. The strategies to boost efficient Fe uptake, transcriptional regulation, and long-distance mobilization in grains have been discussed, emphasizing recent biotechnological routes to load Fe in grains. This article also highlights the new elements of physiological and molecular genetics that underpin the mechanistic insight for the identified Fe-related genes and discusses the bottlenecks in unloading the Fe in grains. The information presented here will provide much-needed resources and directions to overcome challenges and design efficient strategies to enhance the Fe density in wheat grains.

The maize single-nucleus transcriptome comprehensively describes signaling networks governing movement and development of grass stomata

The unique morphology of grass stomata enables rapid responses to environmental changes. Deciphering the basis for these responses is critical for improving food security. We have developed a planta platform of single-nucleus RNA-sequencing by combined fluorescence-activated nuclei flow sorting, and used it to identify cell types in mature and developing stomata from 33,098 nuclei of the maize epidermis-enriched tissues. Guard cells (GCs) and subsidiary cells (SCs) displayed differential expression of genes, besides those encoding transporters, involved in the abscisic acid, CO2, Ca2+, starch metabolism, and blue light signaling pathways, implicating coordinated signal integration in speedy stomatal responses, and of genes affecting cell wall plasticity, implying a more sophisticated relationship between GCs and SCs in stomatal development and dumbbell-shaped guard cell formation. The trajectory of stomatal development identified in young tissues, and by comparison to the bulk RNA-seq data of the MUTE defective mutant in stomatal development, confirmed known features, and shed light on key participants in stomatal development. Our study provides a valuable, comprehensive, and fundamental foundation for further insights into grass stomatal function.

Single-Cell RNA Sequencing for Plant Research: Insights and Possible Benefits

In recent years, advances in single-cell RNA sequencing (scRNA-seq) technologies have continued to change our views on biological systems by increasing the spatiotemporal resolution of our analysis to single-cell resolution. Application of scRNA-seq to plants enables the comprehensive characterization of both common and rare cell types and cell states, uncovering new cell types and revealing how cell types relate to each other spatially and developmentally. This review provides an overview of scRNA-seq methodologies, highlights the application of scRNA-seq in plant science, justifies why scRNA-seq is a master player of sequencing, and explains the role of single-cell transcriptomics technologies in environmental stress adaptation, alongside the challenges and prospects of single-cell transcriptomics. Collectively, we put forward a central role of single-cell sequencing in plant research.

Applications of cell- and tissue-specific 'omics to improve plant productivity

The individual tissues and cell types of plants each have characteristic properties that contribute to the function of the plant as a whole. These are reflected by unique patterns of gene expression, protein and metabolite content, which enable cell-type-specific patterns of growth, development and physiology. Gene regulatory networks act within the cell types to govern the production and activity of these components. For the broader organism to grow and reproduce successfully, cell-type-specific activity must also function within the context of surrounding cell types, which is achieved by coordination of signalling pathways. We can investigate how gene regulatory networks are constructed and function using integrative 'omics technologies. Historically such experiments in plant biological research have been performed at the bulk tissue level, to organ resolution at best. In this review, we describe recent advances in cell- and tissue-specific 'omics technologies that allow investigation at much improved resolution. We discuss the advantages of these approaches for fundamental and translational plant biology, illustrated through the examples of specialised metabolism in medicinal plants and seed germination. We also discuss the challenges that must be overcome for such approaches to be adopted widely by the community.

A rice single cell transcriptomic atlas defines the developmental trajectories of rice floret and inflorescence meristems

Rice inflorescence development determines yield and relies on the activity of axillary meristems (AMs); however, high-resolution analysis of its early development is lacking. Here, we have used high-throughput single-cell RNA sequencing to profile 37 571 rice inflorescence cells and constructed a genome-scale gene expression resource covering the inflorescence-to-floret transition during early reproductive development. The differentiation trajectories of florets and AMs were reconstructed, and discrete cell types and groups of regulators in the highly heterogeneous young inflorescence were identified and then validated by in situ hybridization and with fluorescent marker lines. Our data demonstrate that a WOX transcription factor, DWARF TILLER1, regulates flower meristem activity, and provide evidence for the role of auxin in rice inflorescence branching by exploring the expression and biological role of the auxin importer OsAUX1. Our comprehensive transcriptomic atlas of early rice inflorescence development, supported by genetic evidence, provides single-cell-level insights into AM differentiation and floret development.

Single-cell RNA-sequencing of Nicotiana attenuata corolla cells reveals the biosynthetic pathway of a floral scent

High-throughput single-cell RNA sequencing (scRNA-Seq) identifies distinct cell populations based on cell-to-cell heterogeneity in gene expression. By examining the distribution of the density of gene expression profiles, we can observe the metabolic features of each cell population. Here, we employ the scRNA-Seq technique to reveal the entire biosynthetic pathway of a flower volatile. The corolla of the wild tobacco Nicotiana attenuata emits a bouquet of scents that are composed mainly of benzylacetone (BA). Protoplasts from the N. attenuata corolla limbs and throat cups were isolated at three different time points, and the transcript levels of > 16 000 genes were analyzed in 3756 single cells. We performed unsupervised clustering analysis to determine which cell clusters were involved in BA biosynthesis. The biosynthetic pathway of BA was uncovered by analyzing gene co-expression in scRNA-Seq datasets and by silencing candidate genes in the corolla. In conclusion, the high-resolution spatiotemporal atlas of gene expression provided by scRNA-Seq reveals the molecular features underlying cell-type-specific metabolism in a plant.

Single-cell genomics revolutionizes plant development studies across scales

Understanding the development of tissues, organs and entire organisms through the lens of single-cell genomics has revolutionized developmental biology. Although single-cell transcriptomics has been pioneered in animal systems, from an experimental perspective, plant development holds some distinct advantages: cells do not migrate in relation to one another, and new organ formation (of leaves, roots, flowers, etc.) continues post-embryonically from persistent stem cell populations known as meristems. For a time, plant studies lagged behind animal or cell culture-based, single-cell approaches, largely owing to the difficulty in dissociating plant cells from their rigid cell walls. Recent intensive development of single-cell and single-nucleus isolation techniques across plant species has opened up a wide range of experimental approaches. This has produced a rapidly expanding diversity of information across tissue types and species, concomitant with the creative development of methods. In this brief Spotlight, we highlight some of the technical developments and how they have led to profiling single-cell genomics in various plant organs. We also emphasize the contribution of single-cell genomics in revealing developmental trajectories among different cell types within plant organs. Furthermore, we present efforts toward comparative analysis of tissues and organs at a single-cell level. Single-cell genomics is beginning to generate comprehensive information relating to how plant organs emerge from stem cell populations.

Protoplast Dissociation and Transcriptome Analysis Provides Insights to Salt Stress Response in Cotton

As one of the pioneer crops widely planted in saline-alkaline areas, Gossypium provides daily necessities, including natural fiber, vegetable proteins, and edible oils. However, cotton fiber yield and quality are highly influenced by salt stress. Therefore, elucidating the molecular mechanisms of cotton in response to salinity stress is importance to breed new cultivars with high tolerance. In this study, we first developed a method for single-cell RNA-seq based on isolating protoplast from cotton root tips; then, we studied the impact of salinity stress on gene expression profiling and their dynamic changes using the developed high-efficiency method for protoplast dissociation suitable for single-cell RNA-seq. A total of 3391 and 2826 differentially expressed genes (DEGs) were identified in salt-treated samples before and after protoplast dissociation, respectively, which were enriched into several molecular components, including response to stimulus, response to stress, and cellular macromolecule metabolic process by gene ontology (GO) analysis. Plant hormone signal transduction, phenylpropanoid biosynthesis, and MAPK signaling pathway were found to be enriched via Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Twenty-two and nine salinity-responsive DEGs participated in plant hormone signaling and MAPK signaling in roots, before and after protoplast dissociation, respectively; six upregulated DEGs were involved in ABA signaling transduction, namely, Ga04G2111, Ga07G0142, Ga09G2061, Ga10G0262, Ga01G0063, and Ga08G1915 which indicates their potential functions on plants adapting to salt stress. Additionally, 384 and 257 transcription factors (TFs) were differentially expressed in salt-stress roots before and after protoplast dissociation, respectively, of which significantly up-regulated TFs mainly belonged to the AP2/ERF-ERF family, which implied their potential roles responding to salt stress. These results not only provide novel insights to reveal the regulatory networks in plant’s root response to salt stress, but also lay the solid foundation for further exploration on cellular heterogeneity by single-cell transcriptome sequencing.

Genetic and molecular mechanisms underlying root architecture and function under heat stress-A hidden story

Heat stress events are resulting in a significant negative impact on global food production. The dynamics of cellular, molecular and physiological homoeostasis in aboveground parts under heat stress are extensively deciphered. However, root responses to higher soil/air temperature or stress signalling from shoot to root are limited. Therefore, this review presents a holistic view of root physio-morphological and molecular responses to adapt under hotter environments. Heat stress reprogrammes root cellular machinery, including crosstalk between genes, phytohormones, reactive oxygen species (ROS) and antioxidants. Spatio-temporal regulation and long-distance transport of phytohormones, such as auxin, cytokinin and abscisic acid (ABA) determine the root growth and development under heat stress. ABA cardinally integrates a signalling pathway involving heat shock factors, heat shock proteins and ROS to govern heat stress responses. Additionally, epigenetic modifications by transposable elements, DNA methylation and acetylation also regulate root growth under heat stress. Exogenous application of chemical compounds or biological agents such as ascorbic acid, metal ion chelators, fungi and bacteria can alleviate heat stress-induced reduction in root biomass. Future research should focus on the systemic effect of heat stress from shoot to root with more detailed investigations to decipher the molecular cues underlying the roots architecture and function.

A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants

In all multicellular organisms, transcriptional networks orchestrate organ development. The Arabidopsis root, with its simple structure and indeterminate growth, is an ideal model for investigating the spatiotemporal transcriptional signatures underlying developmental trajectories. To map gene expression dynamics across root cell types and developmental time, we built a comprehensive, organ-scale atlas at single-cell resolution. In addition to estimating developmental progressions in pseudotime, we employed the mathematical concept of optimal transport to infer developmental trajectories and identify their underlying regulators. To demonstrate the utility of the atlas to interpret new datasets, we profiled mutants for two key transcriptional regulators at single-cell resolution, shortroot and scarecrow. We report transcriptomic and in vivo evidence for tissue trans-differentiation underlying a mixed cell identity phenotype in scarecrow. Our results support the atlas as a rich community resource for unraveling the transcriptional programs that specify and maintain cell identity to regulate spatiotemporal organ development.

A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants

In all multicellular organisms, transcriptional networks orchestrate organ development. The Arabidopsis root, with its simple structure and indeterminate growth, is an ideal model for investigating the spatiotemporal transcriptional signatures underlying developmental trajectories. To map gene expression dynamics across root cell types and developmental time, we built a comprehensive, organ-scale atlas at single-cell resolution. In addition to estimating developmental progressions in pseudotime, we employed the mathematical concept of optimal transport to infer developmental trajectories and identify their underlying regulators. To demonstrate the utility of the atlas to interpret new datasets, we profiled mutants for two key transcriptional regulators at single-cell resolution, shortroot and scarecrow. We report transcriptomic and in vivo evidence for tissue trans-differentiation underlying a mixed cell identity phenotype in scarecrow. Our results support the atlas as a rich community resource for unraveling the transcriptional programs that specify and maintain cell identity to regulate spatiotemporal organ development.

Single-cell genomics in plants: current state, future directions, and hurdles to overcome

Single-cell genomics has the potential to revolutionize the study of plant development and tissue-specific responses to environmental stimuli by revealing heretofore unknown players and gene regulatory processes. Here, I focus on the current state of single-cell genomics in plants, emerging technologies and applications, in addition to outlining possible future directions for experiments. I describe approaches to enable cheaper and larger experiments and technologies to measure multiple types of molecules to better model and understand cell types and their different states and trajectories throughout development. Lastly, I discuss the inherent limitations of single-cell studies and the technological hurdles that need to be overcome to widely apply single-cell genomics in crops to generate the greatest possible knowledge gain.

Organizing your space: The potential for integrating spatial transcriptomics and 3D imaging data in plants

Plant cells communicate information for the regulation of development and responses to external stresses. A key form of this communication is transcriptional regulation, accomplished via complex gene networks operating both locally and systemically. To fully understand how genes are regulated across plant tissues and organs, high resolution, multi-dimensional spatial transcriptional data must be acquired and placed within a cellular and organismal context. Spatial transcriptomics (ST) typically provides a two-dimensional spatial analysis of gene expression of tissue sections that can be stacked to render three-dimensional data. For example, X-ray and light-sheet microscopy provide sub-micron scale volumetric imaging of cellular morphology of tissues, organs, or potentially entire organisms. Linking these technologies could substantially advance transcriptomics in plant biology and other fields. Here, we review advances in ST and 3D microscopy approaches and describe how these technologies could be combined to provide high resolution, spatially organized plant tissue transcript mapping.

Shoot and root single cell sequencing reveals tissue- and daytime-specific transcriptome profiles

Although several large-scale single-cell RNA sequencing (scRNAseq) studies addressing the root of Arabidopsis (Arabidopsis thaliana) have been published, there is still need for a de novo reference map for both root and especially above-ground cell types. As the plants' transcriptome substantially changes throughout the day, shaped by the circadian clock, we performed scRNAseq on both Arabidopsis root and above-ground tissues at defined times of the day. For the root scRNAseq analysis, we used tissue-specific reporter lines grown on plates and harvested at the end of the day (ED). In addition, we submitted above-ground tissues from plants grown on soil at ED and end of the night to scRNAseq, which allowed us to identify common cell types/markers between root and shoot and uncover transcriptome changes to above-ground tissues depending on the time of the day. The dataset was also exploited beyond the traditional scRNAseq analysis to investigate non-annotated and di-cistronic transcripts. We experimentally confirmed the predicted presence of some of these transcripts and also addressed the potential function of a previously unidentified marker gene for dividing cells. In summary, this work provides insights into the spatial control of gene expression from nearly 70,000 cells of Arabidopsis for below- and whole above-ground tissue at single-cell resolution at defined time points.

Single-cell transcriptomics sheds light on the identity and metabolism of developing leaf cells

As the main photosynthetic instruments of vascular plants, leaves are crucial and complex plant organs. A strict organization of leaf mesophyll and epidermal cell layers orchestrates photosynthesis and gas exchange. In addition, water and nutrients for leaf growth are transported through the vascular tissue. To establish the single-cell transcriptomic landscape of these different leaf tissues, we performed high-throughput transcriptome sequencing of individual cells isolated from young leaves of Arabidopsis (Arabidopsis thaliana) seedlings grown in two different environmental conditions. The detection of approximately 19,000 different transcripts in over 1,800 high-quality leaf cells revealed 14 cell populations composing the young, differentiating leaf. Besides the cell populations comprising the core leaf tissues, we identified subpopulations with a distinct identity or metabolic activity. In addition, we proposed cell-type-specific markers for each of these populations. Finally, an intuitive web tool allows for browsing the presented dataset. Our data present insights on how the different cell populations constituting a developing leaf are connected via developmental, metabolic, or stress-related trajectories.

Cis-regulatory sequences in plants: Their importance, discovery, and future challenges

The identification and characterization of cis-regulatory DNA sequences and how they function to coordinate responses to developmental and environmental cues is of paramount importance to plant biology. Key to these regulatory processes are cis-regulatory modules (CRMs), which include enhancers and silencers. Despite the extraordinary advances in high-quality sequence assemblies and genome annotations, the identification and understanding of CRMs, and how they regulate gene expression, lag significantly behind. This is especially true for their distinguishing characteristics and activity states. Here, we review the current knowledge on CRMs and breakthrough technologies enabling identification, characterization, and validation of CRMs; we compare the genomic distributions of CRMs with respect to their target genes between different plant species, and discuss the role of transposable elements harboring CRMs in the evolution of gene expression. This is an exciting time to study cis-regulomes in plants; however, significant existing challenges need to be overcome to fully understand and appreciate the role of CRMs in plant biology and in crop improvement.

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.