Towards Building a Plant Cell Atlas

Unveiling the spatiotemporal strategies of plants in response to biotic and abiotic stresses:A comprehensive review

Plant functions are governed by complex regulatory mechanisms that operate across diverse cell types in various tissues. However, the challenge of dissecting plant tissues has hindered the widespread application of single-cell technologies in plant research. Recent advancements in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have propelled the field forward. scRNA-seq enables the examination of gene expression at the single-cell level, while ST preserves the spatial context of cellular organization. While previous reviews have discussed the breakthroughs of scRNA-seq and ST in plants, none have comprehensively addressed the use of these technologies to study plant responses to environmental stress at the cellular level. This review provides an in-depth analysis of the development, advantages, and limitations of single-cell and spatial transcriptomics, highlighting their critical role in unraveling plant strategies for coping with biotic and abiotic stresses. We also explore the challenges and future prospects of integrating scRNA-seq and ST in plant research. Understanding cell-specific responses and the complex interactions between cellular entities within the plant under stress is essential for advancing our knowledge of plant biology.

Arabidopsis research in 2030: Translating the computable plant

Plants are essential for human survival. Over the past three decades, work with the reference plant Arabidopsis thaliana has significantly advanced plant biology research. One key event was the sequencing of its genome 25 years ago, which fostered many subsequent research technologies and datasets. Arabidopsis has been instrumental in elucidating plant-specific aspects of biology, developing research tools, and translating findings to crop improvement. It not only serves as a model for understanding plant biology and but also biology in other fields, with discoveries in Arabidopsis also having led to applications in human health, including insights into immunity, protein degradation, and circadian rhythms. Arabidopsis research has also fostered the development of tools useful for the wider biological research community, such as optogenetic systems and auxin-based degrons. This 4th Multinational Arabidopsis Steering Committee Roadmap outlines future directions, with emphasis on computational approaches, research support, translation to crops, conference accessibility, coordinated research efforts, climate change mitigation, sustainable production, and fundamental research. Arabidopsis will remain a nexus for discovery, innovation, and application, driving advances in both plant and human biology to the year 2030, and beyond.

Can a plant biologist fix a thermostat?

The shift to reductionist biology at the dawn of the genome era yielded a 'parts list' of plant genes and a nascent understanding of complex biological processes. Today, with the genomics era in full swing, advances in high-definition genomics enabled precise temporal and spatial analyses of biological systems down to the single-cell level. These insights, coupled with artificial intelligence-driven in silico design, are propelling the development of the first synthetic plants. By integrating reductionist and systems approaches, researchers are not only reimagining plants as sources of food, fiber, and fuel but also as 'environmental thermostats' capable of mitigating the impacts of a changing climate.

Considerations for building and using integrated single-cell atlases

The rapid adoption of single-cell technologies has created an opportunity to build single-cell 'atlases' integrating diverse datasets across many laboratories. Such atlases can serve as a reference for analyzing and interpreting current and future data. However, it has become apparent that atlasing approaches differ, and the impact of these differences are often unclear. Here we review the current atlasing literature and present considerations for building and using atlases. Importantly, we find that no one-size-fits-all protocol for atlas building exists, but rather we discuss context-specific considerations and workflows, including atlas conceptualization, data collection, curation and integration, atlas evaluation and atlas sharing. We further highlight the benefits of integrated atlases for analyses of new datasets and deriving biological insights beyond what is possible from individual datasets. Our overview of current practices and associated recommendations will improve the quality of atlases to come, facilitating the shift to a unified, reference-based understanding of single-cell biology.

Building a FAIR data ecosystem for incorporating single-cell transcriptomics data into agricultural genome to phenome research

Introduction

The agriculture genomics community has numerous data submission standards available, but the standards for describing and storing single-cell (SC, e.g., scRNA- seq) data are comparatively underdeveloped.

Methods

To bridge this gap, we leveraged recent advancements in human genomics infrastructure, such as the integration of the Human Cell Atlas Data Portal with Terra, a secure, scalable, open-source platform for biomedical researchers to access data, run analysis tools, and collaborate. In parallel, the Single Cell Expression Atlas at EMBL-EBI offers a comprehensive data ingestion portal for high-throughput sequencing datasets, including plants, protists, and animals (including humans). Developing data tools connecting these resources would offer significant advantages to the agricultural genomics community. The FAANG data portal at EMBL-EBI emphasizes delivering rich metadata and highly accurate and reliable annotation of farmed animals but is not computationally linked to either of these resources.

Results

Herein, we describe a pilot-scale project that determines whether the current FAANG metadata standards for livestock can be used to ingest scRNA-seq datasets into Terra in a manner consistent with HCA Data Portal standards. Importantly, rich scRNA-seq metadata can now be brokered through the FAANG data portal using a semi-automated process, thereby avoiding the need for substantial expert curation. We have further extended the functionality of this tool so that validated and ingested SC files within the HCA Data Portal are transferred to Terra for further analysis. In addition, we verified data ingestion into Terra, hosted on Azure, and demonstrated the use of a workflow to analyze the first ingested porcine scRNA-seq dataset. Additionally, we have also developed prototype tools to visualize the output of scRNA-seq analyses on genome browsers to compare gene expression patterns across tissues and cell populations. This JBrowse tool now features distinct tracks, showcasing PBMC scRNA-seq alongside two bulk RNA-seq experiments.

Discussion

We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem based on Findable, Accessible, Interoperable, and Reusable (FAIR) SC principles to facilitate SC-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.

Leveraging multi-omics tools to comprehend responses and tolerance mechanisms of heavy metals in crop plants

Extreme anthropogenic activities and current farming techniques exacerbate the effects of water and soil impurity by hazardous heavy metals (HMs), severely reducing agricultural output and threatening food safety. In the upcoming years, plants that undergo exposure to HM might cause a considerable decline in the development as well as production. Hence, plants have developed sophisticated defensive systems to evade or withstand the harmful consequences of HM. These mechanisms comprise the uptake as well as storage of HMs in organelles, their immobilization via chemical formation by organic chelates, and their removal using many ion channels, transporters, signaling networks, and TFs, amid other approaches. Among various cutting-edge methodologies, omics, most notably genomics, transcriptomics, proteomics, metabolomics, miRNAomics, phenomics, and epigenomics have become game-changing approaches, revealing information about the genes, proteins, critical metabolites as well as microRNAs that govern HM responses and resistance systems. With the help of integrated omics approaches, we will be able to fully understand the molecular processes behind plant defense, enabling the development of more effective crop protection techniques in the face of climate change. Therefore, this review comprehensively presented omics advancements that will allow resilient and sustainable crop plants to flourish in areas contaminated with HMs.

Large language models in plant biology

Large language models (LLMs), such as ChatGPT, have taken the world by storm. However, LLMs are not limited to human language and can be used to analyze sequential data, such as DNA, protein, and gene expression. The resulting foundation models can be repurposed to identify the complex patterns within the data, resulting in powerful, multipurpose prediction tools able to predict the state of cellular systems. This review outlines the different types of LLMs and showcases their recent uses in biology. Since LLMs have not yet been embraced by the plant community, we also cover how these models can be deployed for the plant kingdom.

Three-dimensional reconstruction and multiomics analysis reveal a unique pattern of embryogenesis in Ginkgo biloba

Ginkgo (Ginkgo biloba L.) is one of the earliest extant species in seed plant phylogeny. Embryo development patterns can provide fundamental evidence for the origin, evolution, and adaptation of seeds. However, the architectural and morphological dynamics during embryogenesis in G. biloba remain elusive. Herein, we obtained over 2,200 visual slices from 3 stages of embryo development using micro-computed tomography imaging with improved staining methods. Based on 3-dimensional (3D) spatiotemporal pattern analysis, we found that a shoot apical meristem with 7 highly differentiated leaf primordia, including apical and axillary leaf buds, is present in mature Ginkgo embryos. 3D rendering from the front, top, and side views showed 2 separate transport systems of tracheids located in the hypocotyl and cotyledon, representing a unique pattern of embryogenesis. Furthermore, the morphological dynamic analysis of secretory cavities indicated their strong association with cotyledons during development. In addition, we identified genes GbLBD25a (lateral organ boundaries domain 25a), GbCESA2a (cellulose synthase 2a), GbMYB74c (myeloblastosis 74c), GbPIN2 (PIN-FORMED 2) associated with vascular development regulation, and GbWRKY1 (WRKYGOK 1), GbbHLH12a (basic helix-loop-helix 12a), and GbJAZ4 (jasmonate zim-domain 4) potentially involved in the formation of secretory cavities. Moreover, we found that flavonoid accumulation in mature embryos could enhance postgerminative growth and seedling establishment in harsh environments. Our 3D spatial reconstruction technique combined with multiomics analysis opens avenues for investigating developmental architecture and molecular mechanisms during embryogenesis and lays the foundation for evolutionary studies of embryo development and maturation.

Astronaut omics and the impact of space on the human body at scale

Future multi-year crewed planetary missions will motivate advances in aerospace nutrition and telehealth. On Earth, the Human Cell Atlas project aims to spatially map all cell types in the human body. Here, we propose that a parallel Human Cell Space Atlas could serve as an openly available, global resource for space life science research. As humanity becomes increasingly spacefaring, high-resolution omics on orbit could permit an advent of precision spaceflight healthcare. Alongside the scientific potential, we consider the complex ethical, cultural, and legal challenges intrinsic to the human space omics discipline, and how philosophical frameworks may benefit from international perspectives.

Recent advances in exploring transcriptional regulatory landscape of crops

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

Comprehensive integration of single-cell transcriptomic data illuminates the regulatory network architecture of plant cell fate specification

Plant morphogenesis relies on precise gene expression programs at the proper time and position which is orchestrated by transcription factors (TFs) in intricate regulatory networks in a cell-type specific manner. Here we introduced a comprehensive single-cell transcriptomic atlas of Arabidopsis seedlings. This atlas is the result of meticulous integration of 63 previously published scRNA-seq datasets, addressing batch effects and conserving biological variance. This integration spans a broad spectrum of tissues, including both below- and above-ground parts. Utilizing a rigorous approach for cell type annotation, we identified 47 distinct cell types or states, largely expanding our current view of plant cell compositions. We systematically constructed cell-type specific gene regulatory networks and uncovered key regulators that act in a coordinated manner to control cell-type specific gene expression. Taken together, our study not only offers extensive plant cell atlas exploration that serves as a valuable resource, but also provides molecular insights into gene-regulatory programs that varies from different cell types.

A century journey of organelles research in the plant endomembrane system

We are entering an exciting century in the study of the plant organelles in the endomembrane system. Over the past century, especially within the past 50 years, tremendous advancements have been made in the complex plant cell to generate a much clearer and informative picture of plant organelles, including the molecular/morphological features, dynamic/spatial behavior, and physiological functions. Importantly, all these discoveries and achievements in the identification and characterization of organelles in the endomembrane system would not have been possible without: (1) the innovations and timely applications of various state-of-art cell biology tools and technologies for organelle biology research; (2) the continuous efforts in developing and characterizing new organelle markers by the plant biology community; and (3) the landmark studies on the identification and characterization of the elusive organelles. While molecular aspects and results for individual organelles have been extensively reviewed, the development of the techniques for organelle research in plant cell biology is less appreciated. As one of the ASPB Centennial Reviews on "organelle biology," here we aim to take a journey across a century of organelle biology research in plants by highlighting the important tools (or landmark technologies) and key scientists that contributed to visualize organelles. We then highlight the landmark studies leading to the identification and characterization of individual organelles in the plant endomembrane systems.

Single-cell transcriptomics is revolutionizing the improvement of plant biotechnology research: recent advances and future opportunities

Single-cell approaches are a promising way to obtain high-resolution transcriptomics data and have the potential to revolutionize the study of plant growth and development. Recent years have seen the advent of unprecedented technological advances in the field of plant biology to study the transcriptional information of individual cells by single-cell RNA sequencing (scRNA-seq). This review focuses on the modern advancements of single-cell transcriptomics in plants over the past few years. In addition, it also offers a new insight of how these emerging methods will expedite advance research in plant biotechnology in the near future. Lastly, the various technological hurdles and inherent limitations of single-cell technology that need to be conquered to develop such outstanding possible knowledge gain is critically analyzed and discussed.

Spatiotemporal metabolic responses to water deficit stress in distinct leaf cell-types of poplar

The impact of water-deficit (WD) stress on plant metabolism has been predominantly studied at the whole tissue level. However, plant tissues are made of several distinct cell types with unique and differentiated functions, which limits whole tissue 'omics'-based studies to determine only an averaged molecular signature arising from multiple cell types. Advancements in spatial omics technologies provide an opportunity to understand the molecular mechanisms underlying plant responses to WD stress at distinct cell-type levels. Here, we studied the spatiotemporal metabolic responses of two poplar (Populus tremula× P. alba) leaf cell types -palisade and vascular cells- to WD stress using matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI). We identified unique WD stress-mediated metabolic shifts in each leaf cell type when exposed to early and prolonged WD stresses and recovery from stress. During water-limited conditions, flavonoids and phenolic metabolites were exclusively accumulated in leaf palisade cells. However, vascular cells mainly accumulated sugars and fatty acids during stress and recovery conditions, respectively, highlighting the functional divergence of leaf cell types in response to WD stress. By comparing our MALDI-MSI metabolic data with whole leaf tissue gas chromatography-mass spectrometry (GC-MS)-based metabolic profile, we identified only a few metabolites including monosaccharides, hexose phosphates, and palmitic acid that showed a similar accumulation trend at both cell-type and whole leaf tissue levels. Overall, this work highlights the potential of the MSI approach to complement the whole tissue-based metabolomics techniques and provides a novel spatiotemporal understanding of plant metabolic responses to WD stress. This will help engineer specific metabolic pathways at a cellular level in strategic perennial trees like poplars to help withstand future aberrations in environmental conditions and to increase bioenergy sustainability.

Plant biotechnology research with single-cell transcriptome: recent advancements and prospects

KEY MESSAGE

Single-cell transcriptomic techniques have emerged as powerful tools in plant biology, offering high-resolution insights into gene expression at the individual cell level. This review highlights the rapid expansion of single-cell technologies in plants, their potential in understanding plant development, and their role in advancing plant biotechnology research. Single-cell techniques have emerged as powerful tools to enhance our understanding of biological systems, providing high-resolution transcriptomic analysis at the single-cell level. In plant biology, the adoption of single-cell transcriptomics has seen rapid expansion of available technologies and applications. This review article focuses on the latest advancements in the field of single-cell transcriptomic in plants and discusses the potential role of these approaches in plant development and expediting plant biotechnology research in the near future. Furthermore, inherent challenges and limitations of single-cell technology are critically examined to overcome them and enhance our knowledge and understanding.

AI to enable plant cell metabolic engineering

Plant metabolic engineering must take into consideration the heterogeneous cell types that play a role in metabolite production; cells do not participate equally. We posit that artificial intelligence (AI) developed for biomedical purposes can be applied to plant cell characterization to accelerate the development of metabolic engineering strategies in plants.

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

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

A Single-Nucleus Resolution Atlas of Transcriptome and Chromatin Accessibility for Peanut (Arachis Hypogaea L.) Leaves

The peanut is an important worldwide cash-crop for edible oil and protein. However, the kinetic mechanisms that determine gene expression and chromatin accessibility during leaf development in peanut represented allotetraploid leguminous crops are poorly understood at single-cell resolution. Here, a single-nucleus atlas of peanut leaves is developed by simultaneously profiling the transcriptome and chromatin accessibility in the same individual-cell using fluorescence-activated sorted single-nuclei. In total, 5930 cells with 50 890 expressed genes are classified into 18 cell-clusters, and 5315 chromatin fragments are enriched with 26 083 target genes in the chromatin accessible landscape. The developmental trajectory analysis reveals the involvement of the ethylene-AP2 module in leaf cell differentiation, and cell-cycle analysis demonstrated that genome replication featured in distinct cell-types with circadian rhythms transcription factors (TFs). Furthermore, dual-omics illustrates that the fatty acid pathway modulates epidermal-guard cells differentiation and providescritical TFs interaction networks for understanding mesophyll development, and the cytokinin module (LHY/LOG) that regulates vascular growth. Additionally, an AT-hook protein AhAHL11 is identified that promotes leaf area expansion by modulating the auxin content increase. In summary, the simultaneous profiling of transcription and chromatin accessibility landscapes using snRNA/ATAC-seq provides novel biological insights into the dynamic processes of peanut leaf cell development at the cellular level.

Application of single-cell multi-omics approaches in horticulture research

Cell heterogeneity shapes the morphology and function of various tissues and organs in multicellular organisms. Elucidation of the differences among cells and the mechanism of intercellular regulation is essential for an in-depth understanding of the developmental process. In recent years, the rapid development of high-throughput single-cell transcriptome sequencing technologies has influenced the study of plant developmental biology. Additionally, the accuracy and sensitivity of tools used to study the epigenome and metabolome have significantly increased, thus enabling multi-omics analysis at single-cell resolution. Here, we summarize the currently available single-cell multi-omics approaches and their recent applications in plant research, review the single-cell based studies in fruit, vegetable, and ornamental crops, and discuss the potential of such approaches in future horticulture research.

scPlant: A versatile framework for single-cell transcriptomic data analysis in plants

Single-cell transcriptomics has been fully embraced in plant biological research and is revolutionizing our understanding of plant growth, development, and responses to external stimuli. However, single-cell transcriptomic data analysis in plants is not trivial, given that there is currently no end-to-end solution and that integration of various bioinformatics tools involves a large number of required dependencies. Here, we present scPlant, a versatile framework for exploring plant single-cell atlases with minimum input data provided by users. The scPlant pipeline is implemented with numerous functions for diverse analytical tasks, ranging from basic data processing to advanced demands such as cell-type annotation and deconvolution, trajectory inference, cross-species data integration, and cell-type-specific gene regulatory network construction. In addition, a variety of visualization tools are bundled in a built-in Shiny application, enabling exploration of single-cell transcriptomic data on the fly.

Cell signaling in the shoot apical meristem

Distinct from animals, plants maintain organogenesis from specialized tissues termed meristems throughout life. In the shoot apex, the shoot apical meristem (SAM) produces all aerial organs, such as leaves, from its periphery. For this, the SAM needs to precisely balance stem cell renewal and differentiation, which is achieved through dynamic zonation of the SAM, and cell signaling within functional domains is key for SAM functions. The WUSCHEL-CLAVATA feedback loop plays a key role in SAM homeostasis, and recent studies have uncovered new components, expanding our understanding of the spatial expression and signaling mechanism. Advances in polar auxin transport and signaling have contributed to knowledge of the multifaceted roles of auxin in the SAM and organogenesis. Finally, single-cell techniques have expanded our understanding of the cellular functions within the shoot apex at single-cell resolution. In this review, we summarize the most up-to-date understanding of cell signaling in the SAM and focus on the multiple levels of regulation of SAM formation and maintenance.

The maturation and aging trajectory of Marchantia polymorpha at single-cell resolution

Bryophytes represent a sister to the rest of land plants. Despite their evolutionary importance and relatively simple body plan, a comprehensive understanding of the cell types and transcriptional states that underpin the temporal development of bryophytes has not been achieved. Using time-resolved single-cell RNA sequencing, we define the cellular taxonomy of Marchantia polymorpha across asexual reproduction phases. We identify two maturation and aging trajectories of the main plant body of M. polymorpha at single-cell resolution: the gradual maturation of tissues and organs along the tip-to-base axis of the midvein and the progressive decline of meristem activities in the tip along the chronological axis. Specifically, we observe that the latter aging axis is temporally correlated with the formation of clonal propagules, suggesting an ancient strategy to optimize allocation of resources to producing offspring. Our work thus provides insights into the cellular heterogeneity that underpins the temporal development and aging of bryophytes.

Analysis of meristems and plant regeneration at single-cell resolution

Rapid development of high-throughput single-cell RNA sequencing (scRNA-seq) technologies offers exciting opportunities to reveal new and rare cell types, previously hidden cell states, and continuous developmental trajectories. In this review, we first illustrate the ways in which scRNA-seq enables researchers to distinguish between distinct plant cell populations, delineate cell cycle continuums, and infer continuous differentiation trajectories of diverse cell types in shoots, roots, and floral and vascular meristems with unprecedented resolution. We then highlight the emerging power of scRNA-seq to dissect cell heterogeneity in regenerating tissues and uncover the cellular basis of cell reprogramming and stem cell commitment during plant regeneration. We conclude by discussing related outstanding questions in the field.

Proximity Labeling in Plants

Proteins are workhorses in the cell; they form stable and more often dynamic, transient protein-protein interactions, assemblies, and networks and have an intimate interplay with DNA and RNA. These network interactions underlie fundamental biological processes and play essential roles in cellular function. The proximity-dependent biotinylation labeling approach combined with mass spectrometry (PL-MS) has recently emerged as a powerful technique to dissect the complex cellular network at the molecular level. In PL-MS, by fusing a genetically encoded proximity-labeling (PL) enzyme to a protein or a localization signal peptide, the enzyme is targeted to a protein complex of interest or to an organelle, allowing labeling of proximity proteins within a zoom radius. These biotinylated proteins can then be captured by streptavidin beads and identified and quantified by mass spectrometry. Recently engineered PL enzymes such as TurboID have a much-improved enzymatic activity, enabling spatiotemporal mapping with a dramatically increased signal-to-noise ratio. PL-MS has revolutionized the way we perform proteomics by overcoming several hurdles imposed by traditional technology, such as biochemical fractionation and affinity purification mass spectrometry. In this review, we focus on biotin ligase-based PL-MS applications that have been, or are likely to be, adopted by the plant field. We discuss the experimental designs and review the different choices for engineered biotin ligases, enrichment, and quantification strategies. Lastly, we review the validation and discuss future perspectives.

The protein-protein interaction landscape of transcription factors during gynoecium development in Arabidopsis

Flowers are composed of organs whose identity is defined by the combinatorial activity of transcription factors (TFs). The interactions between MADS-box TFs and protein complex formation have been schematized in the floral quartet model of flower development. The gynoecium is the flower's female reproductive part, crucial for fruit and seed production and, hence, for reproductive success. After the establishment of carpel identity, many tissues arise to form a mature gynoecium. TFs have been described as regulators of gynoecium development, and some interactions and complexes have been identified. However, broad knowledge about the interactions among these TFs and their participation during development remains scarce. In this study, we used a systems biology approach to understand the formation of a complex reproductive unit-as the gynoecium-by mapping binary interactions between well-characterized TFs. We analyzed almost 4500 combinations and detected more than 250 protein-protein interactions (PPIs), resulting in a process-specific interaction map. Topological analyses suggest hidden functions and novel roles for many TFs. In addition, we observed a close relationship between TFs involved in auxin and cytokinin-signaling pathways and other TFs. Furthermore, we analyzed the network by combining PPI data, expression, and genetic data, which helped us to dissect it into several dynamic spatio-temporal subnetworks related to gynoecium development processes. Finally, we generated an extended PPI network that predicts new players in gynoecium development. Taken together, all these results serve as a valuable resource for the plant community.

Toward a data infrastructure for the Plant Cell Atlas

We review how a data infrastructure for the Plant Cell Atlas might be built using existing infrastructure and platforms. The Human Cell Atlas has developed an extensive infrastructure for human and mouse single cell data, while the European Bioinformatics Institute has developed a Single Cell Expression Atlas, that currently houses several plant data sets. We discuss issues related to appropriate ontologies for describing a plant single cell experiment. We imagine how such an infrastructure will enable biologists and data scientists to glean new insights into plant biology in the coming decades, as long as such data are made accessible to the community in an open manner.

Using quantitative methods to understand leaf epidermal development

As the interface between plants and the environment, the leaf epidermis provides the first layer of protection against drought, ultraviolet light, and pathogen attack. This cell layer comprises highly coordinated and specialised cells such as stomata, pavement cells and trichomes. While much has been learned from the genetic dissection of stomatal, trichome and pavement cell formation, emerging methods in quantitative measurements that monitor cellular or tissue dynamics will allow us to further investigate cell state transitions and fate determination in leaf epidermal development. In this review, we introduce the formation of epidermal cell types in Arabidopsis and provide examples of quantitative tools to describe phenotypes in leaf research. We further focus on cellular factors involved in triggering cell fates and their quantitative measurements in mechanistic studies and biological patterning. A comprehensive understanding of how a functional leaf epidermis develops will advance the breeding of crops with improved stress tolerance.

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.

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.

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.

Sample-multiplexing approaches for single-cell sequencing

Single-cell sequencing is widely used in biological and medical studies. However, its application with multiple samples is hindered by inefficient sample processing, high experimental costs, ambiguous identification of true single cells, and technical batch effects. Here, we introduce sample-multiplexing approaches for single-cell sequencing in transcriptomics, epigenomics, genomics, and multiomics. In single-cell transcriptomics, sample multiplexing uses variants of native or artificial features as sample markers, enabling sample pooling and decoding. Such features include: (1) natural genetic variation, (2) nucleotide-barcode anchoring on cellular or nuclear membranes, (3) nucleotide-barcode internalization to the cytoplasm or nucleus, (4) vector-based barcode expression in cells, and (5) nucleotide-barcode incorporation during library construction. Other single-cell omics methods are based on similar concepts, particularly single-cell combinatorial indexing. These methods overcome current challenges, while enabling super-loading of single cells. Finally, selection guidelines are presented that can accelerate technological application.

Wolffia, a minimalist plant and synthetic biology chassis

A highly simplified species for genome engineering would facilitate rational design of a synthetic plant. A candidate species is the aquatic, non-grass monocot wolffia (Wolffia australiana) in the Lemnaceae family. Commonly known as watermeal, wolffia is a rootless ball of several thousand cells the size of a pinhead and the fastest growing plant known on Earth. Its extreme morphological reduction is coupled to transposon-mediated streamlining of its transcriptome, which represents a core set of nonredundant protein coding genes. Despite its body plan and transcriptome being highly specialized for continuous growth, wolffia retains cell types relevant to higher plants. Systems level studies with this species could enable the creation of a defined biological chassis for synthetic plant construction.

The single-cell stereo-seq reveals region-specific cell subtypes and transcriptome profiling in Arabidopsis leaves

Understanding the complex functions of plant leaves requires a thorough characterization of discrete cell features. Although single-cell gene expression profiling technologies have been developed, their application in characterizing cell subtypes has not been achieved yet. Here, we present scStereo-seq (single-cell spatial enhanced resolution omics sequencing) that enabled us to show the bona fide single-cell spatial transcriptome profiles of Arabidopsis leaves. Subtle but significant transcriptomic differences between upper and lower epidermal cells have been successfully distinguished. Furthermore, we discovered cell-type-specific gene expression gradients from the main vein to the leaf edge, which led to the finding of distinct spatial developmental trajectories of vascular cells and guard cells. Our study showcases the importance of physical locations of individual cells for exerting complex biological functions in plants and demonstrates that scStereo-seq is a powerful tool to integrate single-cell location and transcriptome information for plant biology study.

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.

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.

Gynoecium and fruit development in Arabidopsis

Flowering plants produce flowers and one of the most complex floral structures is the pistil or the gynoecium. All the floral organs differentiate from the floral meristem. Various reviews exist on molecular mechanisms controlling reproductive development, but most focus on a short time window and there has been no recent review on the complete developmental time frame of gynoecium and fruit formation. Here, we highlight recent discoveries, including the players, interactions and mechanisms that govern gynoecium and fruit development in Arabidopsis. We also present the currently known gene regulatory networks from gynoecium initiation until fruit maturation.

Single-cell RNA sequencing technologies and applications: A brief overview

Single-cell RNA sequencing (scRNA-seq) technology has become the state-of-the-art approach for unravelling the heterogeneity and complexity of RNA transcripts within individual cells, as well as revealing the composition of different cell types and functions within highly organized tissues/organs/organisms. Since its first discovery in 2009, studies based on scRNA-seq provide massive information across different fields making exciting new discoveries in better understanding the composition and interaction of cells within humans, model animals and plants. In this review, we provide a concise overview about the scRNA-seq technology, experimental and computational procedures for transforming the biological and molecular processes into computational and statistical data. We also provide an explanation of the key technological steps in implementing the technology. We highlight a few examples on how scRNA-seq can provide unique information for better understanding health and diseases. One important application of the scRNA-seq technology is to build a better and high-resolution catalogue of cells in all living organism, commonly known as atlas, which is key resource to better understand and provide a solution in treating diseases. While great promises have been demonstrated with the technology in all areas, we further highlight a few remaining challenges to be overcome and its great potentials in transforming current protocols in disease diagnosis and treatment.

Advances in enzyme-mediated proximity labeling and its potential for plant research

Cellular processes rely on the intimate interplay of different molecules, including DNA, RNA, proteins, and metabolites. Obtaining and integrating data on their abundance and dynamics at high temporal and spatial resolution are essential for our understanding of plant growth and development. In the past decade, enzymatic proximity labeling (PL) has emerged as a powerful tool to study local protein and nucleotide ensembles, discover protein-protein and protein-nucleotide interactions, and resolve questions about protein localization and membrane topology. An ever-growing number and continuous improvement of enzymes and methods keep broadening the spectrum of possible applications for PL and make it more accessible to different organisms, including plants. While initial PL experiments in plants required high expression levels and long labeling times, recently developed faster enzymes now enable PL of proteins on a cell type-specific level, even with low-abundant baits, and in different plant species. Moreover, expanding the use of PL for additional purposes, such as identification of locus-specific gene regulators or high-resolution electron microscopy may now be in reach. In this review, we give an overview of currently available PL enzymes and their applications in mammalian cell culture and plants. We discuss the challenges and limitations of PL methods and highlight open questions and possible future directions for PL in plants.

To the proteome and beyond: advances in single-cell omics profiling for plant systems

Recent advances in single-cell proteomics for animal systems could be adapted for plants to increase our understanding of plant development, response to stimuli, and cell-to-cell signaling.

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.

Mass Spectrometry Imaging Disclosed Spatial Distribution of Defense-Related Metabolites in Triticum spp

Fusarium Head Blight is the most common fungal disease that strongly affects Triticum spp., reducing crop yield and leading to the accumulation of toxic metabolites. Several studies have investigated the plant metabolic response to counteract mycotoxins accumulation. However, information on the precise location where the defense mechanism is taking place is scarce. Therefore, this study aimed to investigate the specific tissue distribution of defense metabolites in two Triticum species and use this information to postulate on the metabolites' functional role, unlocking the "location-to-function" paradigm. To address this challenge, transversal cross-sections were obtained from the middle of the grains. They were analyzed using an atmospheric-pressure (AP) SMALDI MSI source (AP-SMALDI5 AF, TransMIT GmbH, Giessen, Germany) coupled to a Q Exactive HF (Thermo Fisher Scientific GmbH, Bremen, Germany) orbital trapping mass spectrometer. Our result revealed the capability of (AP)-SMALDI MSI instrumentation to finely investigate the spatial distribution of wheat defense metabolites, such as hydroxycinnamic acid amides, oxylipins, linoleic and α-linoleic acids, galactolipids, and glycerolipids.

The Systems and Synthetic Biology of Auxin

Auxin biology as a field has been at the forefront of advances in delineating the structures, dynamics, and control of plant growth networks. Advances have been enabled by combining the complementary fields of top-down, holistic systems biology and bottom-up, build-to-understand synthetic biology. Continued collaboration between these approaches will facilitate our understanding of and ability to engineer auxin's control of plant growth, development, and physiology. There is a need for the application of similar complementary approaches to improving equity and justice through analysis and redesign of the human systems in which this research is undertaken.

Arabidopsis bioinformatics: tools and strategies

The sequencing of the Arabidopsis thaliana genome 21 years ago ushered in the genomics era for plant research. Since then, an incredible variety of bioinformatic tools permit easy access to large repositories of genomic, transcriptomic, proteomic, epigenomic and other '-omic' data. In this review, we cover some more recent tools (and highlight the 'classics') for exploring such data in order to help formulate quality, testable hypotheses, often without having to generate new experimental data. We cover tools for examining gene expression and co-expression patterns, undertaking promoter analyses and gene set enrichment analyses, and exploring protein-protein and protein-DNA interactions. We will touch on tools that integrate different data sets at the end of the article.

Plant PhysioSpace: a robust tool to compare stress response across plant species

Generalization of transcriptomics results can be achieved by comparison across experiments. This generalization is based on integration of interrelated transcriptomics studies into a compendium. Such a focus on the bigger picture enables both characterizations of the fate of an organism and distinction between generic and specific responses. Numerous methods for analyzing transcriptomics datasets exist. Yet, most of these methods focus on gene-wise dimension reduction to obtain marker genes and gene sets for, for example, pathway analysis. Relying only on isolated biological modules might result in missing important confounders and relevant contexts. We developed a method called Plant PhysioSpace, which enables researchers to compute experimental conditions across species and platforms without a priori reducing the reference information to specific gene sets. Plant PhysioSpace extracts physiologically relevant signatures from a reference dataset (i.e. a collection of public datasets) by integrating and transforming heterogeneous reference gene expression data into a set of physiology-specific patterns. New experimental data can be mapped to these patterns, resulting in similarity scores between the acquired data and the extracted compendium. Because of its robustness against platform bias and noise, Plant PhysioSpace can function as an inter-species or cross-platform similarity measure. We have demonstrated its success in translating stress responses between different species and platforms, including single-cell technologies. We have also implemented two R packages, one software and one data package, and a Shiny web application to facilitate access to our method and precomputed models.

Single-nucleus RNA sequencing of plant tissues using a nanowell-based system

Single-cell genomics provides unprecedented potential for research on plant development and environmental responses. Here, we introduce a generic procedure for plant nucleus isolation combined with nanowell-based library preparation. Our method enables the transcriptome analysis of thousands of individual plant nuclei. It serves as an alternative to the use of protoplast isolation, which is currently a standard methodology for plant single-cell genomics, although it can be challenging for some plant tissues. We show the applicability of our nucleus isolation method by using different plant materials from different species. The potential of our single-nucleus RNA sequencing method is shown through the characterization of transcriptomes of seedlings and developing flowers from Arabidopsis thaliana. We evaluated the transcriptome dynamics during the early stages of anther development, identified stage-specific activities of transcription factors regulating this process, and predicted potential target genes of these transcription factors. Our nucleus isolation procedure can be applied in different plant species and tissues, thus expanding the toolkit for plant single-cell genomics experiments.

Single-nucleus RNA sequencing of plant tissues using a nanowell-based system

Single-cell genomics provides unprecedented potential for research on plant development and environmental responses. Here, we introduce a generic procedure for plant nucleus isolation combined with nanowell-based library preparation. Our method enables the transcriptome analysis of thousands of individual plant nuclei. It serves as an alternative to the use of protoplast isolation, which is currently a standard methodology for plant single-cell genomics, although it can be challenging for some plant tissues. We show the applicability of our nucleus isolation method by using different plant materials from different species. The potential of our single-nucleus RNA sequencing method is shown through the characterization of transcriptomes of seedlings and developing flowers from Arabidopsis thaliana. We evaluated the transcriptome dynamics during the early stages of anther development, identified stage-specific activities of transcription factors regulating this process, and predicted potential target genes of these transcription factors. Our nucleus isolation procedure can be applied in different plant species and tissues, thus expanding the toolkit for plant single-cell genomics experiments.

Single-cell transcriptome atlas of the leaf and root of rice seedlings

As a multicellular organism, rice flourishes relying on gene expression diversity among cells of various functions. However, cellular-resolution transcriptome features are yet to be fully recognized, let alone cell-specific transcriptional responses to environmental stimuli. In this study, we apply single-cell RNA sequencing to both shoot and root of rice seedlings growing in Kimura B nutrient solution or exposed to various abiotic stresses and characterize transcriptomes for a total of 237,431 individual cells. We identify 15 and 9 cell types in the leaf and root, respectively, and observe that common transcriptome features are often shared between leaves and roots in the same tissue layer, except for endodermis or epidermis. Abiotic stress stimuli alter gene expression largely in a cell type-specific manner, but for a given cell type, different stresses often trigger transcriptional regulation of roughly the same set of genes. Besides, we detect proportional changes in cell populations in response to abiotic stress and investigate the underlying molecular mechanisms through single-cell reconstruction of the developmental trajectory. Collectively, our study represents a benchmark-setting data resource of single-cell transcriptome atlas for rice seedlings and an illustration of exploiting such resources to drive discoveries in plant biology.

Effects of Sample Size on Plant Single-Cell RNA Profiling

Single-cell RNA (scRNA) profiling or scRNA-sequencing (scRNA-seq) makes it possible to parallelly investigate diverse molecular features of multiple types of cells in a given plant tissue and discover cell developmental processes. In this study, we evaluated the effects of sample size (i.e., cell number) on the outcome of single-cell transcriptome analysis by sampling different numbers of cells from a pool of ~57,000 Arabidopsis thaliana root cells integrated from five published studies. Our results indicated that the most significant principal components could be achieved when 20,000–30,000 cells were sampled, a relatively high reliability of cell clustering could be achieved by using ~20,000 cells with little further improvement by using more cells, 96% of the differentially expressed genes could be successfully identified with no more than 20,000 cells, and a relatively stable pseudotime could be estimated in the subsample with 5000 cells. Finally, our results provide a general guide for optimizing sample size to be used in plant scRNA-seq studies.