Dynamics of Gene Expression in Single Root Cells of Arabidopsis thaliana

Distinct identities of leaf phloem cells revealed by single cell transcriptomics

The leaf vasculature plays a key role in solute translocation. Veins consist of at least seven distinct cell types, with specific roles in transport, metabolism, and signaling. Little is known about leaf vascular cells, in particular the phloem parenchyma (PP). PP effluxes sucrose into the apoplasm as a basis for phloem loading, yet PP has been characterized only microscopically. Here, we enriched vascular cells from Arabidopsis leaves to generate a single-cell transcriptome atlas of leaf vasculature. We identified at least 19 cell clusters, encompassing epidermis, guard cells, hydathodes, mesophyll, and all vascular cell types, and used metabolic pathway analysis to define their roles. Clusters comprising PP cells were enriched for transporters, including SWEET11 and SWEET12 sucrose and UmamiT amino acid efflux carriers. We provide evidence that PP development occurs independently from ALTERED PHLOEM DEVELOPMENT, a transcription factor required for phloem differentiation. PP cells have a unique pattern of amino acid metabolism activity distinct from companion cells (CCs), explaining differential distribution/metabolism of amino acids in veins. The kinship relation of the vascular clusters is strikingly similar to the vein morphology, except for a clear separation of CC from the other vascular cells including PP. In summary, our single-cell RNA-sequencing analysis provides a wide range of information into the leaf vasculature and the role and relationship of the leaf cell types.

Unraveling Root Development Through Single-Cell Omics and Reconstruction of Gene Regulatory Networks

Over the last decades, research on postembryonic root development has been facilitated by "omics" technologies. Among these technologies, microarrays first, and RNA sequencing (RNA-seq) later, have provided transcriptional information on the underlying molecular processes establishing the basis of System Biology studies in roots. Cell fate specification and development have been widely studied in the primary root, which involved the identification of many cell type transcriptomes and the reconstruction of gene regulatory networks (GRN). The study of lateral root (LR) development has not been an exception. However, the molecular mechanisms regulating cell fate specification during LR formation remain largely unexplored. Recently, single-cell RNA-seq (scRNA-seq) studies have addressed the specification of tissues from stem cells in the primary root. scRNA-seq studies are anticipated to be a useful approach to decipher cell fate specification and patterning during LR formation. In this review, we address the different scRNA-seq strategies used both in plants and animals and how we could take advantage of scRNA-seq to unravel new regulatory mechanisms and reconstruct GRN. In addition, we discuss how to integrate scRNA-seq results with previous RNA-seq datasets and GRN. We also address relevant findings obtained through single-cell based studies and how LR developmental studies could be facilitated by scRNA-seq approaches and subsequent GRN inference. The use of single-cell approaches to investigate LR formation could help to decipher fundamental biological mechanisms such as cell memory, synchronization, polarization, or pluripotency.

Single-cell transcriptome atlas and chromatin accessibility landscape reveal differentiation trajectories in the rice root

Root development relies on the establishment of meristematic tissues that give rise to distinct cell types that differentiate across defined temporal and spatial gradients. Dissection of the developmental trajectories and the transcriptional networks that underlie them could aid understanding of the function of the root apical meristem in both dicots and monocots. Here, we present a single-cell RNA (scRNA) sequencing and chromatin accessibility survey of rice radicles. By temporal profiling of individual root tip cells we reconstruct continuous developmental trajectories of epidermal cells and ground tissues, and elucidate regulatory networks underlying cell fate determination in these cell lineages. We further identify characteristic processes, transcriptome profiles, and marker genes for these cell types and reveal conserved and divergent root developmental pathways between dicots and monocots. Finally, we demonstrate the potential of the platform for functional genetic studies by using spatiotemporal modeling to identify a rice root meristematic mutant from a cell-specific gene cohort.

A new era for plant science: spatial single-cell transcriptomics

To achieve a complete understanding of how organisms function, there is a need to study their fundamental unit, the cell, in its spatial context. In recent years, we have seen fast-paced technological progress to study the transcriptional content of single cells and their spatial relationships. This review highlights modern advancements in single-cell RNA-sequencing, provides an overview of the technologies that led the plant field toward spatial transcriptomics, and describes the available spatial transcriptomics approaches providing examples of their application to plant tissues. In addition, it discusses the integration of these methods to study plant tissues. Taken together, we propose a central role of spatial transcriptomics approaches in plant science.

A single-cell analysis of the Arabidopsis vegetative shoot apex

The shoot apical meristem allows for reiterative formation of new aerial structures throughout the life cycle of a plant. We use single-cell RNA sequencing to define the cellular taxonomy of the Arabidopsis vegetative shoot apex at the transcriptome level. We find that the shoot apex is composed of highly heterogeneous cells, which can be partitioned into 7 broad populations with 23 transcriptionally distinct cell clusters. We delineate cell-cycle continuums and developmental trajectories of epidermal cells, vascular tissue, and leaf mesophyll cells and infer transcription factors and gene expression signatures associated with cell fate decisions. Integrative analysis of shoot and root apical cell populations further reveals common and distinct features of epidermal and vascular tissues. Our results, thus, offer a valuable resource for investigating the basic principles underlying cell division and differentiation in plants at single-cell resolution.

Targeted permeabilization of the cell wall and extraction of charged molecules from single cells in intact plant clusters using a focused electric field

The extraction of cellular contents from plant cells covered with cell walls remains a challenge, as it is physically hindered by the cell wall. We present a new microfluidic approach that leverages an intense pulsed electric field for permeabilizing the cell wall and a focused DC electric field for extracting the cellular contents selectively from a few targeted cells in a cluster of intact plant cells. We coupled the approach with on-chip fluorescence quantification of extracted molecules leveraging isotachophoresis as well as off-chip reverse transcription-quantitative polymerase chain reaction detecting extracted mRNA molecules. Our approach offers a workflow of about 5 min, isolating a cluster of intact plant cells, permeabilizing the cell wall, selectively extracting cytosolic molecules from a few targeted cells in the cluster, and outputting them to off-chip analyses without any enzymatic reactions. We anticipate that this approach will create a new opportunity to explore plant biology through less biased data realized by the rapid extraction of molecules from intact plant clusters.

Single-nucleus RNA and ATAC sequencing reveals the impact of chromatin accessibility on gene expression in Arabidopsis roots at the single-cell level

Similar to other complex organisms, plants consist of diverse and specialized cell types. The gain of unique biological functions of these different cell types is the consequence of the establishment of cell-type-specific transcriptional programs. As a necessary step in gaining a deeper understanding of the regulatory mechanisms controlling plant gene expression, we report the use of single-nucleus RNA sequencing (sNucRNA-seq) and single-nucleus assay for transposase accessible chromatin sequencing (sNucATAC-seq) technologies on Arabidopsis roots. The comparison of our single-nucleus transcriptomes to the published protoplast transcriptomes validated the use of nuclei as biological entities to establish plant cell-type-specific transcriptomes. Furthermore, our sNucRNA-seq results uncovered the transcriptomes of additional cell subtypes not identified by single-cell RNA-seq. Similar to our transcriptomic approach, the sNucATAC-seq approach led to the distribution of the Arabidopsis nuclei into distinct clusters, suggesting the differential accessibility of chromatin between groups of cells according to their identity. To reveal the impact of chromatin accessibility on gene expression, we integrated sNucRNA-seq and sNucATAC-seq data and demonstrated that cell-type-specific marker genes display cell-type-specific patterns of chromatin accessibility. Our data suggest that the differential chromatin accessibility is a critical mechanism to regulate gene activity at the cell-type level.

Transcriptional landscape of rice roots at the single-cell resolution

There are two main types of root systems in flowering plants, namely taproot systems of dicots and fibrous root systems found in monocots. Despite this fundamental split, our current knowledge of cellular and molecular mechanism driving root development is mainly based on studies of the dicot model Arabidopsis. However, the world major crops are monocots and little is known about the transcriptional programs underlying cell-type specification in this clade. Here, we report the transcriptomes of more than 20 000 single cells derived from root tips of two agronomically important rice cultivars. Using combined computational and experimental analyses we were able to robustly identify most of the major cell types and define novel cell-type-specific marker genes for both cultivars. Importantly, we found divergent cell types associated with specific regulatory programs, including phytohormone biosynthesis, signaling, and response, which were well conserved between the two rice cultivars. In addition, we detected substantial differences between the cell-type transcript profiles of Arabidopsis and rice. These species-specific features emphasize the importance of analyzing tissues across diverse model species, including rice. Taken together, our study provides insight into the transcriptomic landscape of major cell types of rice root tip at single-cell resolution and opens new avenues to study cell-type specification, function, and evolution in plants.

Single-cell profiling lights different cell trajectories in plants

The molecular mechanism of the maintenance and differentiation of plant stem cells is an eternal theme in studies on plant growth and development. Recent advances in single-cell RNA sequencing (scRNA-seq) methods have completely changed the understanding of cell heterogeneity and cell function, allowing research precision to identify the differentiation trajectory of stem cells maintained and differentiated at the cellular level. This review aimed to mainly discuss the novel insights provided by scRNA-seq for the maintenance and initiation of plant stem cells, cell differentiation, cell response to environmental changes, and improvement strategies for scRNA-seq. In addition, it highlighted additional perspectives beyond scRNA-seq, such as spatial transcriptomes, epigenomes, and single-cell multiomics, for a renewed understanding of stem cell maintenance and cell differentiation, thus providing potential targets and theoretical foundations for crop improvement.

FlsnRNA-seq: protoplasting-free full-length single-nucleus RNA profiling in plants

The broad application of single-cell RNA profiling in plants has been hindered by the prerequisite of protoplasting that requires digesting the cell walls from different types of plant tissues. Here, we present a protoplasting-free approach, flsnRNA-seq, for large-scale full-length RNA profiling at a single-nucleus level in plants using isolated nuclei. Combined with 10x Genomics and Nanopore long-read sequencing, we validate the robustness of this approach in Arabidopsis root cells and the developing endosperm. Sequencing results demonstrate that it allows for uncovering alternative splicing and polyadenylation-related RNA isoform information at the single-cell level, which facilitates characterizing cell identities.

Space: the final frontier — achieving single-cell, spatially resolved transcriptomics in plants

Single-cell RNA-seq is a tool that generates a high resolution of transcriptional data that can be used to understand regulatory networks in biological systems. In plants, several methods have been established for transcriptional analysis in tissue sections, cell types, and/or single cells. These methods typically require cell sorting, transgenic plants, protoplasting, or other damaging or laborious processes. Additionally, the majority of these technologies lose most or all spatial resolution during implementation. Those that offer a high spatial resolution for RNA lack breadth in the number of transcripts characterized. Here, we briefly review the evolution of spatial transcriptomics methods and we highlight recent advances and current challenges in sequencing, imaging, and computational aspects toward achieving 3D spatial transcriptomics of plant tissues with a resolution approaching single cells. We also provide a perspective on the potential opportunities to advance this novel methodology in plants.

DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

Machine learning in plant science and plant breeding

Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to integrate various data sets. Concurrently, machine learning has rapidly evolved and is now widely applied in science in general and in plant genotyping and phenotyping in particular. Here, we review the application of machine learning in the context of plant science and plant breeding. We focus on analyses at different phenotype levels, from biochemical to yield, and in connecting genotypes to these. In this way, we illustrate how machine learning offers a suite of methods that enable researchers to find meaningful patterns in relevant plant data.

Gramene 2021: harnessing the power of comparative genomics and pathways for plant research

Gramene (http://www.gramene.org), a knowledgebase founded on comparative functional analyses of genomic and pathway data for model plants and major crops, supports agricultural researchers worldwide. The resource is committed to open access and reproducible science based on the FAIR data principles. Since the last NAR update, we made nine releases; doubled the genome portal's content; expanded curated genes, pathways and expression sets; and implemented the Domain Informational Vocabulary Extraction (DIVE) algorithm for extracting gene function information from publications. The current release, #63 (October 2020), hosts 93 reference genomes-over 3.9 million genes in 122 947 families with orthologous and paralogous classifications. Plant Reactome portrays pathway networks using a combination of manual biocuration in rice (320 reference pathways) and orthology-based projections to 106 species. The Reactome platform facilitates comparison between reference and projected pathways, gene expression analyses and overlays of gene-gene interactions. Gramene integrates ontology-based protein structure-function annotation; information on genetic, epigenetic, expression, and phenotypic diversity; and gene functional annotations extracted from plant-focused journals using DIVE. We train plant researchers in biocuration of genes and pathways; host curated maize gene structures as tracks in the maize genome browser; and integrate curated rice genes and pathways in the Plant Reactome.

Gramene 2021: harnessing the power of comparative genomics and pathways for plant research

Gramene (http://www.gramene.org), a knowledgebase founded on comparative functional analyses of genomic and pathway data for model plants and major crops, supports agricultural researchers worldwide. The resource is committed to open access and reproducible science based on the FAIR data principles. Since the last NAR update, we made nine releases; doubled the genome portal's content; expanded curated genes, pathways and expression sets; and implemented the Domain Informational Vocabulary Extraction (DIVE) algorithm for extracting gene function information from publications. The current release, #63 (October 2020), hosts 93 reference genomes-over 3.9 million genes in 122 947 families with orthologous and paralogous classifications. Plant Reactome portrays pathway networks using a combination of manual biocuration in rice (320 reference pathways) and orthology-based projections to 106 species. The Reactome platform facilitates comparison between reference and projected pathways, gene expression analyses and overlays of gene-gene interactions. Gramene integrates ontology-based protein structure-function annotation; information on genetic, epigenetic, expression, and phenotypic diversity; and gene functional annotations extracted from plant-focused journals using DIVE. We train plant researchers in biocuration of genes and pathways; host curated maize gene structures as tracks in the maize genome browser; and integrate curated rice genes and pathways in the Plant Reactome.

Single-Cell Transcriptome Analysis in Plants: Advances and Challenges

The rapid and enthusiastic adoption of single-cell RNA sequencing (scRNA-seq) has demonstrated that this technology is far more than just another way to perform transcriptome analysis. It is not an exaggeration to say that the advent of scRNA-seq is revolutionizing the details of whole-transcriptome snapshots from a tissue to a cell. With this disruptive technology, it is now possible to mine heterogeneity between tissue types and within cells like never before. This enables more rapid identification of rare and novel cell types, simultaneous characterization of multiple different cell types and states, more accurate and integrated understanding of their roles in life processes, and more. However, we are only at the beginning of unlocking the full potential of scRNA-seq applications. This is particularly true for plant sciences, where single-cell transcriptome profiling is in its early stage and has many exciting challenges to overcome. In this review, we compare and evaluate recent pioneering studies using the Arabidopsis root model, which has established new paradigms for scRNA-seq studies in plants. We also explore several new and promising single-cell analysis tools that are available to those wishing to study plant development and physiology at unprecedented resolution and scale. In addition, we propose some future directions on the use of scRNA-seq technology to tackle some of the critical challenges in plant research and breeding.

Guidelines for Setting Up a mRNA Sequencing Experiment and Best Practices for Bioinformatic Data Analysis

RNA-sequencing, commonly referred to as RNA-seq, is the most recently developed method for the analysis of transcriptomes. It uses high-throughput next-generation sequencing technologies and has revolutionized our understanding of the complexity and dynamics of whole transcriptomes.In this chapter, we recall the key developments in transcriptome analysis and dissect the different steps of the general workflow that can be run by users to design and perform a mRNA-seq experiment as well as to process mRNA-seq data obtained by the Illumina technology. The chapter proposes guidelines for completing a mRNA-seq study properly and makes available recommendations for best practices based on recent literature and on the latest developments in technology and algorithms. We also remark the large number of choices available (especially for bioinformatic data analysis) in front of which the scientist may be in trouble.In the last part of the chapter we discuss the new frontiers of single-cell RNA-seq and isoform sequencing by long read technology.

Flow Cytometry and Sorting in Arabidopsis

Flow cytometry and sorting represents a valuable and mature experimental platform for the analysis of cellular populations. Applications involving higher plants started to emerge around 40 years ago and are now widely employed both to provide unique information regarding basic and applied questions in the biosciences and to advance agricultural productivity in practical ways. Further development of this platform is being actively pursued, and this promises additional progress in our understanding of the interactions of cells within complex tissues and organs. Higher plants offer unique challenges in terms of flow cytometric analysis, first since their organs and tissues are, almost without exception, three-dimensional assemblies of different cell types held together by tough cell walls, and, second, because individual plant cells are generally larger than those of mammals.This chapter, which updates work last reviewed in 2014 [Galbraith DW (2014) Flow cytometry and sorting in Arabidopsis. In: Sanchez Serrano JJ, Salinas J (eds) Arabidopsis Protocols, 3rd ed. Methods in molecular biology, vol 1062. Humana Press, Totowa, pp 509-537], describes the application of techniques of flow cytometry and sorting to the model plant species Arabidopsis thaliana, in particular emphasizing (a) fluorescence labeling in vivo of specific cell types and of subcellular components, (b) analysis using both conventional cytometers and spectral analyzers, (c) fluorescence-activated sorting of protoplasts and nuclei, and (d) transcriptome analyses using sorted protoplasts and nuclei, focusing on population analyses at the level of single protoplasts and nuclei. Since this is an update, details of new experimental methods are emphasized.

Conserved and nuanced hierarchy of gene regulatory response to hypoxia

A dynamic assembly of nuclear and cytoplasmic processes regulate gene activity. Hypoxic stress and the associated energy crisis activate a plurality of regulatory mechanisms including modulation of chromatin structure, transcriptional activation and post-transcriptional processes. Temporal control of genes is associated with specific chromatin modifications and transcription factors. Genome-scale technologies that resolve transcript subpopulations in the nucleus and cytoplasm indicate post-transcriptional processes enable cells to conserve energy, prepare for prolonged stress and accelerate recovery. Moreover, the harboring of gene transcripts associated with growth in the nucleus and macromolecular RNA-protein complexes contributes to the preferential translation of stress-responsive gene transcripts during hypoxia. We discuss evidence of evolutionary variation in integration of nuclear and cytoplasmic processes that may contribute to variations in flooding resilience.

Identification of Gene Regulatory Networks from Single-Cell Expression Data

Single-cell RNAseq is an emerging technology that allows the quantification of gene expression in individual cells. In plants, single-cell sequencing technology has been applied to generate root cell expression maps under many experimental conditions. DAP-seq and ATAC-seq have also been used to generate genome-scale maps of protein-DNA interactions and open chromatin regions in plants. In this protocol, we describe a multistep computational pipeline for the integration of single-cell RNAseq data with DAP-seq and ATAC-seq data to predict regulatory networks and key regulatory genes. Our approach utilizes machine learning methods including feature selection and stability selection to identify candidate regulatory genes. The network generated by this pipeline can be used to provide a putative annotation of gene regulatory modules and to identify candidate transcription factors that could play a key role in specific cell types.

Long-distance stress and developmental signals associated with abscisic acid signaling in environmental responses

Flowering plants consist of highly differentiated organs, including roots, leaves, shoots and flowers, which have specific roles: root system for water and nutrient uptake, leaves for photosynthesis and gas exchange and reproductive organs for seed production. The communication between organs through the vascular system, by which water, nutrient and signaling molecules are transported, is essential for coordinated growth and development of the whole plant, particularly under adverse conditions. Here, we highlight recent progress in understanding how signaling pathways of plant hormones are associated with long-distance stress and developmental signals, with particular focus on environmental stress responses. In addition to the root-to-shoot peptide signal that induces abscisic acid accumulation in leaves under drought stress conditions, we summarize the diverse stress-responsive peptide signals reported to date to play a role in environmental responses.

Plant roots employ cell-layer-specific programs to respond to pathogenic and beneficial microbes

Plant roots are built of concentric cell layers that are thought to respond to microbial infections by employing specific, genetically defined programs. Yet, the functional impact of this radial organization remains elusive, particularly due to the lack of genome-wide techniques for monitoring expression at a cell-layer resolution. Here, cell-type-specific expression of tagged ribosomes enabled the isolation of ribosome-bound mRNA to obtain cell-layer translatomes (TRAP-seq, translating ribosome affinity purification and RNA sequencing). After inoculation with the vascular pathogen Verticillium longisporum, pathogenic oomycete Phytophthora parasitica, or mutualistic endophyte Serendipita indica, root cell-layer responses reflected the fundamentally different colonization strategies of these microbes. Notably, V. longisporum specifically suppressed the endodermal barrier, which restricts fungal progression, allowing microbial access to the root central cylinder. Moreover, localized biosynthesis of antimicrobial compounds and ethylene differed in response to pathogens and mutualists. These examples highlight the power of this resource to gain insights into root-microbe interactions and to develop strategies in crop improvement.

Isolation of Plant Root Nuclei for Single Cell RNA Sequencing

The characterization of the transcriptional similarities and differences existing between plant cells and cell types is important to better understand the biology of each cell composing the plant, to reveal new molecular mechanisms controlling gene activity, and to ultimately implement meaningful strategies to enhance plant cell biology. To gain a deeper understanding of the regulation of plant gene activity, the individual transcriptome of each plant cell needs to be established. Until recently, single cell approaches were mostly limited to bulk transcriptomic studies on selected cell types. Accessing specific cell types required the development of labor-intensive strategies. Recently, single cell sequencing strategies were successfully applied on isolated Arabidopsis thaliana root protoplasts. However, this strategy relies on the successful isolation of viable protoplasts upon the optimization of the enzymatic cocktails required to digest the cell wall and on the compatibility of fragile plant protoplasts with the use of microfluidic systems to generate single cell transcriptomic libraries. To overcome these difficulties, we present a simple and fast alternative strategy: the isolation and use of plant nuclei to access meaningful transcriptomic information from plant cells. This protocol was specifically developed to enable the use of the plant nuclei with 10× Genomics' Chromium technology partitions technology. Briefly, the plant nuclei are released from the root by chopping into a nuclei isolation buffer before purification by filtration then nuclei sorting. Upon sorting, the nuclei are resuspended in a low divalent ion buffer compatible with the Chromium technology in order to create single nuclei ribonucleic acid-sequencing libraries (sNucRNA-seq). © 2020 Wiley Periodicals LLC. Basic Protocol 1: Arabidopsis seed sterilization and planting Basic Protocol 2: Nuclei isolation from Arabidopsis roots Basic Protocol 3: Fluorescent-activated nuclei sorting (FANS) purification Support Protocol: Estimation of nuclei density using Countess II automated cell counter Alternate Protocol 1: Proper growth conditions for Medicago truncatula and Sorghum bicolor Alternate Protocol 2: Estimation of nuclei density using sNucRNA-seq technology.

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

Discovering transcription factor (TF) targets is necessary for the study of regulatory pathways, but it is hampered in plants by the lack of highly efficient predictive technology. This study is the first to establish a simple system for predicting TF targets in rice (Oryza sativa) leaf cells based on 10 × Genomics' single-cell RNA sequencing method. We effectively utilized the transient expression system to create the differential expression of a TF (OsNAC78) in each cell and sequenced all single cell transcriptomes. In total, 35 candidate targets having strong correlations with OsNAC78 expression were captured using expression profiles. Likewise, 78 potential differentially expressed genes were identified between clusters having the lowest and highest expression levels of OsNAC78. A gene overlapping analysis identified 19 genes as final candidate targets, and various assays indicated that Os01g0934800 and Os01g0949900 were OsNAC78 targets. Additionally, the cell profiles showed extremely similar expression trajectories between OsNAC78 and the two targets. The data presented here provide a high-resolution insight into predicting TF targets and offer a new application for single-cell RNA sequencing in plants.

Expression Partitioning of Duplicate Genes at Single Cell Resolution in Arabidopsis Roots

Gene duplication is a key evolutionary phenomenon, prevalent in all organisms but particularly so in plants, where whole genome duplication (WGD; polyploidy) is a major force in genome evolution. Much effort has been expended in attempting to understand the evolution of duplicate genes, addressing such questions as why some paralog pairs rapidly return to single copy status whereas, in other pairs, both paralogs are retained and may diverge in expression pattern or function. The effect of a gene - its site of expression and thus the initial locus of its function - occurs at the level of a cell comprising a single cell type at a given state of the cell's development. Using Arabidopsis thaliana single cell transcriptomic data we categorized patterns of expression for 11,470 duplicate gene pairs across 36 cell clusters comprising nine cell types and their developmental states. Among these 11,470 pairs, 10,187 (88.8%) had at least one copy expressed in at least one of the 36 cell clusters. Pairs produced by WGD more often had both paralogs expressed in root cells than did pairs produced by small scale duplications. Three quarters of gene pairs expressed in the 36 cell clusters (7,608/10,187) showed extreme expression bias in at least one cluster, including 352 cases of reciprocal bias, a pattern consistent with expression subfunctionalization. More than twice as many pairs showed reciprocal expression bias between cell states than between cell types or between roots and leaves. A group of 33 gene pairs with reciprocal expression bias showed evidence of concerted divergence of gene networks in stele vs. epidermis. Pairs with both paralogs expressed without bias were less likely to have paralogs with divergent mutant phenotypes; such bias-free pairs showed evidence of preservation by maintenance of dosage balance. Overall, we found considerable evidence of shifts in gene expression following duplication, including in >80% of pairs encoding 7,653 genes expressed ubiquitously in all root cell types and states for which we inferred the polarity of change.

scAPAtrap: identification and quantification of alternative polyadenylation sites from single-cell RNA-seq data

Alternative polyadenylation (APA) generates diverse mRNA isoforms, which contributes to transcriptome diversity and gene expression regulation by affecting mRNA stability, translation and localization in cells. The rapid development of 3' tag-based single-cell RNA-sequencing (scRNA-seq) technologies, such as CEL-seq and 10x Genomics, has led to the emergence of computational methods for identifying APA sites and profiling APA dynamics at single-cell resolution. However, existing methods fail to detect the precise location of poly(A) sites or sites with low read coverage. Moreover, they rely on priori genome annotation and can only detect poly(A) sites located within or near annotated genes. Here we proposed a tool called scAPAtrap for detecting poly(A) sites at the whole genome level in individual cells from 3' tag-based scRNA-seq data. scAPAtrap incorporates peak identification and poly(A) read anchoring, enabling the identification of the precise location of poly(A) sites, even for sites with low read coverage. Moreover, scAPAtrap can identify poly(A) sites without using priori genome annotation, which helps locate novel poly(A) sites in previously overlooked regions and improve genome annotation. We compared scAPAtrap with two latest methods, scAPA and Sierra, using scRNA-seq data from different experimental technologies and species. Results show that scAPAtrap identified poly(A) sites with higher accuracy and sensitivity than competing methods and could be used to explore APA dynamics among cell types or the heterogeneous APA isoform expression in individual cells. scAPAtrap is available at https://github.com/BMILAB/scAPAtrap.

Network-based approaches for understanding gene regulation and function in plants

Expression reprogramming directed by transcription factors is a primary gene regulation underlying most aspects of the biology of any organism. Our views of how gene regulation is coordinated are dramatically changing thanks to the advent and constant improvement of high-throughput profiling and transcriptional network inference methods: from activities of individual genes to functional interactions across genes. These technical and analytical advances can reveal the topology of transcriptional networks in which hundreds of genes are hierarchically regulated by multiple transcription factors at systems level. Here we review the state of the art of experimental and computational methods used in plant biology research to obtain large-scale datasets and model transcriptional networks. Examples of direct use of these network models and perspectives on their limitations and future directions are also discussed.

Transcriptional networks regulating root vascular development

Vascular development involves multiple processes, including the establishment of vascular stem cells (e.g. procambium/cambium cells), stem cell divisions, and cell specification. A number of key transcription factors regulating vascular development have been identified, and the molecular mechanisms underlying these regulators have been keenly investigated. These studies uncovered that transcriptional regulation and phytohormone signaling have central roles in proceeding vascular developmental processes. Recent research approaches contributed to identify key transcription factors and their downstream genes, which enhanced our understanding of vascular development. This review discusses some research approaches and emerging molecular mechanisms that mediate the activation of transcriptional networks regulating root vascular development.

Single-Cell Genomics and Epigenomics: Technologies and Applications in Plants

The development of genomics and epigenomics has allowed rapid advances in our understanding of plant biology. However, conventional bulk analysis dilutes cell-specific information by providing only average information, thereby limiting the resolution of genomic and functional genomic studies. Recent advances in single-cell sequencing technology concerning genomics and epigenomics open new avenues to dissect cell heterogeneity in multiple biological processes. Recent applications of these approaches to plants have provided exciting insights into diverse biological questions. We highlight the methodologies underlying the current techniques of single-cell genomics and epigenomics before covering their recent applications, potential significance, and future perspectives in plant biology.

Vascular transcription factors guide plant epidermal responses to limiting phosphate conditions

Optimal plant growth is hampered by deficiency of the essential macronutrient phosphate in most soils. Plant roots can, however, increase their root hair density to efficiently forage the soil for this immobile nutrient. By generating and exploiting a high-resolution single-cell gene expression atlas of Arabidopsis roots, we show an enrichment of TARGET OF MONOPTEROS 5/LONESOME HIGHWAY (TMO5/LHW) target gene responses in root hair cells. The TMO5/LHW heterodimer triggers biosynthesis of mobile cytokinin in vascular cells and increases root hair density during low-phosphate conditions by modifying both the length and cell fate of epidermal cells. Moreover, root hair responses in phosphate-deprived conditions are TMO5- and cytokinin-dependent. Cytokinin signaling links root hair responses in the epidermis to perception of phosphate depletion in vascular cells.

Gene regulatory network and its constituent transcription factors that control nitrogen-deficiency responses in rice

Increase in the nitrogen (N)-use efficiency and optimization of N response in crop species are urgently needed. Although transcription factor-based genetic engineering is a promising approach for achieving these goals, transcription factors that play key roles in the response to N deficiency have not been studied extensively. Here, we performed RNA-seq analysis of root samples of 20 Asian rice (Oryza sativa) accessions with differential nutrient uptake. Data obtained from plants exposed to N-replete and N-deficient conditions were subjected to coexpression analysis and machine learning-based pathway inference to dissect the gene regulatory network required for the response to N deficiency. Four transcription factors, including members of the G2-like and bZIP families, were predicted to function as key regulators of gene transcription within the network in response to N deficiency. Cotransfection assays validated inferred novel regulatory pathways, and further analyses using genome-edited knockout lines suggested that these transcription factors are important for N-deficiency responses in planta. Many of the N deficiency-responsive genes, including those encoding key regulators within the network, were coordinately regulated by transcription factors belonging to different families. Transcription factors identified in this study could be valuable for the modification of N response and metabolism.

Regulation of Cell Type-Specific Immunity Networks in Arabidopsis Roots

While root diseases are among the most devastating stresses in global crop production, our understanding of root immunity is still limited relative to our knowledge of immune responses in leaves. Considering that root performance is based on the concerted functions of its different cell types, we undertook a cell type-specific transcriptome analysis to identify gene networks activated in epidermis, cortex, and pericycle cells of Arabidopsis (Arabidopsis thaliana) roots challenged with two immunity elicitors, the bacterial flagellin-derived flg22 and the endogenous Pep1 peptide. Our analyses revealed distinct immunity gene networks in each cell type. To further substantiate our understanding of regulatory patterns underlying these cell type-specific immunity networks, we developed a tool to analyze paired transcription factor binding motifs in the promoters of cell type-specific genes. Our study points toward a connection between cell identity and cell type-specific immunity networks that might guide cell types in launching immune response according to the functional capabilities of each cell type.

Fructans Are Differentially Distributed in Root Tissues of Asparagus

Inulin- and neoseries-type fructans [fructooligosaccharides (FOS) and fructopolysaccharides] accumulate in storage roots of asparagus (Asparagus officinalis L.), which continue to grow throughout the lifespan of this perennial plant. However, little is known about the storage of fructans at the spatial level in planta, and the degree of control by the plant is largely uncertain. We have utilized mass spectrometry imaging (MSI) to resolve FOS distribution patterns in asparagus roots (inner, middle, and outer tissues). Fructan and proteome profiling were further applied to validate the differential abundance of various fructan structures and to correlate observed tissue-specific metabolite patterns with the abundance of related fructan biosynthesis enzymes. Our data revealed an increased abundance of FOS with higher degree of polymerization (DP > 5) and of fructopolysaccharides (DP11 to DP17) towards the inner root tissues. Three isoforms of fructan:fructan 6G-fructosyltransferase (6G-FFT), forming 6G-kestose with a β (2-6) linkage using sucrose as receptor and 1-kestose as donor, were similarly detected in all three root tissues. In contrast, one ß-fructofuranosidase, which likely exhibits fructan:fructan 1-fructosyltransferase (1-FFT) activity, showed very high abundance in the inner tissues and lower levels in the outer tissues. We concluded a tight induction of the biosynthesis of fructans with DP > 5, following a gradient from the outer root cortex to the inner vascular tissues, which also correlates with high levels of sucrose metabolism in inner tissues, observed in our study.

Global Dynamic Molecular Profiling of Stomatal Lineage Cell Development by Single-Cell RNA Sequencing

The regulation of stomatal lineage cell development has been extensively investigated. However, a comprehensive characterization of this biological process based on single-cell transcriptome analysis has not yet been reported. In this study, we performed RNA sequencing on 12 844 individual cells from the cotyledons of 5-day-old Arabidopsis seedlings. We identified 11 cell clusters corresponding mostly to cells at specific stomatal developmental stages using a series of marker genes. Comparative analysis of genes with the highest variable expression among these cell clusters revealed transcriptional networks that regulate development from meristemoid mother cells to guard mother cells. Examination of the developmental dynamics of marker genes via pseudo-time analysis revealed potential interactions between these genes. Collectively, our study opens the door for understanding how the identified novel marker genes participate in the regulation of stomatal lineage cell development.

Prediction of condition-specific regulatory genes using machine learning

Recent advances in genomic technologies have generated data on large-scale protein-DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5-25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.

PscB: A Browser to Explore Plant Single Cell RNA-Sequencing Data Sets

The Plant Single Cell RNA-Sequencing Browser, with its comprehensive visualization tools, provides a resource to explore expression information in scRNA-Seq data.

Combining GAL4 GFP enhancer trap with split luciferase to measure spatiotemporal promoter activity in Arabidopsis

In multicellular organisms different types of tissues have distinct gene expression profiles associated with specific function or structure of the cell. Quantification of gene expression in whole organs or whole organisms can give misleading information about levels or dynamics of expression in specific cell types. Tissue- or cell-specific analysis of gene expression has potential to enhance our understanding of gene regulation and interactions of cell signalling networks. The Arabidopsis circadian oscillator is a gene network which orchestrates rhythmic expression across the day/night cycle. There is heterogeneity between cell and tissue types of the composition and behaviour of the oscillator. In order to better understand the spatial and temporal patterns of gene expression, flexible tools are required. By combining a Gateway®-compatible split luciferase construct with a GAL4 GFP enhancer trap system, we describe a tissue-specific split luciferase assay for non-invasive detection of spatiotemporal gene expression in Arabidopsis. We demonstrate the utility of this enhancer trap-compatible split luciferase assay (ETSLA) system to investigate tissue-specific dynamics of circadian gene expression. We confirm spatial heterogeneity of circadian gene expression in Arabidopsis leaves and describe the resources available to investigate any gene of interest.

The promise of single-cell genomics in plants

Single-cell genomic approaches have the potential to revolutionize the study of plant systems. Here, we highlight newly developed techniques to analyze transcriptomes at single-cell resolution. We focus on the rigorous standards necessary to generate and compare these data sets introducing analysis methods that can be applied to interpret their results. Lastly, we discuss the inherent limitations of single-cell studies and address future directions for plant single-cell genomics.

Dimensionality reduction by UMAP to visualize physical and genetic interactions

Dimensionality reduction is often used to visualize complex expression profiling data. Here, we use the Uniform Manifold Approximation and Projection (UMAP) method on published transcript profiles of 1484 single gene deletions of Saccharomyces cerevisiae. Proximity in low-dimensional UMAP space identifies groups of genes that correspond to protein complexes and pathways, and finds novel protein interactions, even within well-characterized complexes. This approach is more sensitive than previous methods and should be broadly useful as additional transcriptome datasets become available for other organisms.

Biological and Cellular Functions of the Microdomain-Associated FWL/CNR Protein Family in Plants

Membrane microdomains/nanodomains are sub-compartments of the plasma membrane enriched in sphingolipids and characterized by their unique protein composition. They play important roles in regulating plant development and plant-microbe interactions including mutualistic symbiotic interactions. Several protein families are associated with the microdomain fraction of biological membranes such as flotillins, prohibitins, and remorins. More recently, GmFWL1, a FWL/CNR protein exclusively expressed in the soybean nodule, was functionally characterized as a new microdomain-associated protein. Interestingly, GmFWL1 is homologous to the tomato FW2-2 protein, a major regulator of tomato fruit development. In this review, we summarize the knowledge gained about the biological, cellular, and physiological functions of members of the FWL/CNR family across various plant species. The role of the FWL/CNR proteins is also discussed within the scope of their evolution and transcriptional regulation.

Growth models from a brassinosteroid perspective

Plant growth relies on interconnected hormonal pathways, their corresponding transcriptional networks and mechanical signals. This work reviews recent brassinosteroid (BR) studies and integrates them with current growth models derived from research in roots. The relevance of spatiotemporal BR signaling in the longitudinal and radial root axes and its multifaceted interaction with auxin, the impact of BR on final cell size determination and its interplay with microtubules and the cell wall are discussed. Also highlighted are emerging variations of canonical BR signaling that could function in developmental-specific context.

Single-Cell Transcriptomics: A High-Resolution Avenue for Plant Functional Genomics

Plant function is the result of the concerted action of single cells in different tissues. Advances in RNA-seq technologies and tissue processing allow us now to capture transcriptional changes at single-cell resolution. The incredible potential of single-cell RNA-seq lies in the novel ability to study and exploit regulatory processes in complex tissues based on the behaviour of single cells. Importantly, the independence from reporter lines allows the analysis of any given tissue in any plant. While there are challenges associated with the handling and analysis of complex datasets, the opportunities are unique to generate knowledge of tissue functions in unprecedented detail and to facilitate the application of such information by mapping cellular functions and interactions in a plant cell atlas.

Time-Series Single-Cell RNA-Seq Data Reveal Auxin Fluctuation during Endocycle

Appropriate cell cycle regulation is crucial for achieving coordinated development and cell differentiation in multicellular organisms. In Arabidopsis, endoreduplication is often observed in terminally differentiated cells and several reports have shown its molecular mechanisms. Auxin is a key factor for the mode transition from mitotic cell cycle to endocycle; however, it remains unclear if and how auxin maintains the endocycle mode. In this study, we reanalyzed root single-cell transcriptome data and reconstructed cell cycle trajectories of the mitotic cell cycle and endocycle. With progression of the endocycle, genes involved in auxin synthesis, influx and efflux were induced at the specific cell phase, suggesting that auxin concentration fluctuated dynamically. Such induction of auxin-related genes was not observed in the mitotic cell cycle, suggesting that the auxin fluctuation plays some roles in maintaining the endocycle stage. In addition, the expression level of CYCB1;1, which is required for cell division in the M phase, coincided with the expected amount of auxin and cell division. Our analysis also provided a set of genes expressed in specific phases of the cell cycle. Taking these findings together, reconstruction of single-cell transcriptome data enables us to identify properties of the cell cycle more accurately.

Just passing through: The auxin gradient of the root meristem

The root meristem-one of the plant's centers of continuous growth-is a conveyer belt in which cells of different identities are pushed through gradients along the root's longitudinal axis. An auxin gradient has long been implicated in controlling the progression of cell states in the root meristem. Recent work has shown that a PLETHORA (PLT) protein transcription factor gradient, which is under a delayed auxin response, has a dose-dependent effect on the differentiation state of cells. The direct effect of auxin concentration on differential transcriptional outputs remains unclear. Genomic and other analyses of regulatory sequences show that auxin responses are likely controlled by combinatorial inputs from transcription factors outside the core auxin signaling pathway. The passage through the meristem exposes cells to varying positional signals that could help them interpret auxin inputs independent of gradient effects. One open question is whether cells process information from the changes in the gradient over time as they move through the auxin gradient.

Visualization of Protein Coding, Long Noncoding, and Nuclear RNAs by Fluorescence in Situ Hybridization in Sections of Shoot Apical Meristems and Developing Flowers

In addition to transcriptional regulation, gene expression is further modulated through mRNA spatiotemporal distribution, by RNA movement between cells, and by RNA localization within cells. Here, we have adapted RNA fluorescence in situ hybridization (FISH) to explore RNA localization in Arabidopsis (Arabidopsis thaliana). We show that RNA FISH on sectioned material can be applied to investigate the tissue and subcellular localization of meristem and flower development genes, cell cycle transcripts, and plant long noncoding RNAs. We also developed double RNA FISH to dissect the coexpression of different mRNAs at the shoot apex and nuclear-cytoplasmic separation of cell cycle gene transcripts in dividing cells. By coupling RNA FISH with fluorescence immunocytochemistry, we further demonstrate that a gene's mRNA and protein may be simultaneously detected, for example revealing uniform distribution of PIN-FORMED1 (PIN1) mRNA and polar localization of PIN1 protein in the same cells. Therefore, our method enables the visualization of gene expression at both transcriptional and translational levels with subcellular spatial resolution, opening up the possibility of systematically tracking the dynamics of RNA molecules and their cognate proteins in plant cells.

A single-cell view of tissue regeneration in plants

With the development of advanced molecular-genetic and computational technologies it becomes possible to tackle individual cells within a regenerating tissue, to define morphogenetic and cellular changes in space and time by live imaging, to acquire transcriptome status with single-cell RNA sequencing (ScRNA-seq), and to delineate the candidate mechanisms by iterative computational and experimental approaches. Here, we review recent findings and current knowledge on tissue regeneration in plants, focusing on four evolutionarily conserved scenarios that a cell may embark on to facilitate the regeneration of a plant tissue structure lost by injury, namely cell death, division, dedifferentiation, and transdifferentiation. Understanding of these scenarios at single-cell resolution, singularly and in combination, could provide an unprecedented view of tissue regeneration in plants.