Vision, challenges and opportunities for a Plant Cell Atlas

Exploring the untapped potential of single-cell and spatial omics in plant biology

Advances in single-cell and spatial omics technologies have revolutionised biology by revealing the diverse molecular states of individual cells and their spatial organization within tissues. The field of plant biology has widely adopted single-cell transcriptome and chromatin accessibility profiling and spatial transcriptomics, which extend traditional cell biology and genomics analyses and provide unique opportunities to reveal molecular and cellular dynamics of tissues. Using these technologies, comprehensive cell atlases have been generated in several model plant species, providing valuable platforms for discovery and tool development. Other emerging technologies related to single-cell and spatial omics, such as multiomics, lineage tracing, molecular recording, and high-content genetic and chemical perturbation phenotyping, offer immense potential for deepening our understanding of plant biology yet remain underutilised due to unique technical challenges and resource availability. Overcoming plant-specific barriers, such as cell wall complexity and limited antibody resources, alongside community-driven efforts in developing more complete reference atlases and computational tools, will accelerate progress. The synergy between technological innovation and targeted biological questions is poised to drive significant discoveries, advancing plant science. This review highlights the current applications of single-cell and spatial omics technologies in plant research and introduces emerging approaches with the potential to transform the field.

The promising role of proteomes and metabolomes in defining the single-cell landscapes of plants

The plant community has a strong track record of RNA sequencing technology deployment, which combined with the recent advent of spatial platforms (e.g. 10× genomics) has resulted in an explosion of single-cell and nuclei datasets that can be positioned in an in situ context within tissues (e.g. a cell atlas). In the genomics era, application of proteomics technologies in the plant sciences has always trailed behind that of RNA sequencing technologies, largely due in part to upfront cost, ease-of-use, and access to expertise. Conversely, the use of early analytical tools for characterizing small molecules (metabolites) from plant systems predates nucleic acid sequencing and proteomics analysis, as the search for plant-based natural products has played a significant role in improving human health throughout history. As the plant sciences field now aims to fully define cell states, cell-specific regulatory networks, metabolic asymmetry and phenotypes, the measurement of proteins and metabolites at the single-cell level will be paramount. As a result of these efforts, the plant community will unlock exciting opportunities to accelerate discovery and drive toward meaningful translational outcomes.

20 years of the Bio-Analytic Resource for Plant Biology

The Bio-Analytic Resource for Plant Biology ('the BAR', at https://bar.utoronto.ca) is celebrating its 20th year in operation in 2025. The BAR encompasses and provides visualization tools for large 'omics data sets from plants. The BAR covers data from Arabidopsis, tomato, wheat, barley and 29 other plant species (with data for 2 others to be released soon). These data include nucleotide and protein sequence data, gene expression data, protein-protein and protein-DNA interactions, protein structures, subcellular localizations, and polymorphisms. The data are stored in more than 200 relational databases holding 186 GB of data and are presented to the researchers via web apps. These web apps provide data analysis and visualization tools. Some of the most popular tools are eFP ('electronic fluorescent pictograph') Browsers, ePlants and ThaleMine (an Arabidopsis-specific instance of InterMine). The BAR was designated a Global Core Biodata Resource in 2023. Like other GCBRs, the BAR has excellent operational stability, provides access without login requirement, and provides an API for researchers to be able to access BAR data programmatically. We present in this update a new overarching search tool called Gaia that permits easy access to all BAR data, powered by machine learning and artificial intelligence.

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.

Harnessing Single-Cell and Spatial Transcriptomics for Crop Improvement

Single-cell and spatial transcriptomics technologies have significantly advanced our understanding of the molecular mechanisms underlying crop biology. This review presents an update on the application of these technologies in crop improvement. The heterogeneity of different cell populations within a tissue plays a crucial role in the coordinated response of an organism to its environment. Single-cell transcriptomics enables the dissection of this heterogeneity, offering insights into the cell-specific transcriptomic responses of plants to various environmental stimuli. Spatial transcriptomics technologies complement single-cell approaches by preserving the spatial context of gene expression profiles, allowing for the in situ localization of transcripts. Together, single-cell and spatial transcriptomics facilitate the discovery of novel genes and gene regulatory networks that can be targeted for genetic manipulation and breeding strategies aimed at enhancing crop yield, quality, and resilience. This review highlights significant findings from recent studies, discusses the expanding roles of these technologies, and explores future opportunities for their application in crop improvement.

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

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

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.

Single-cell transcriptomics reveals heterogeneity in plant responses to the environment: a focus on biotic and abiotic interactions

Biotic and abiotic environmental cues are major factors influencing plant growth and productivity. Interactions with biotic (e.g. symbionts and pathogens) and abiotic (e.g. changes in temperature, water, or nutrient availability) factors trigger signaling and downstream transcriptome adjustments in plants. While bulk RNA-sequencing technologies have traditionally been used to profile these transcriptional changes, tissue homogenization may mask heterogeneity of responses resulting from the cellular complexity of organs. Thus, whether different cell types respond equally to environmental fluctuations, or whether subsets of the responses are cell-type specific, are long-lasting questions in plant biology. The recent breakthrough of single-cell transcriptomics in plant research offers an unprecedented view of cellular responses under changing environmental conditions. In this review, we discuss the contribution of single-cell transcriptomics to the understanding of cell-type-specific plant responses to biotic and abiotic environmental interactions. Besides major biological findings, we present some technical challenges coupled to single-cell studies of plant-environment interactions, proposing possible solutions and exciting paths for future research.

Pleiotropy, a feature or a bug? Toward co-ordinating plant growth, development, and environmental responses through engineering plant hormone signaling

The advent of gene editing technologies such as CRISPR has simplified co-ordinating trait development. However, identifying candidate genes remains a challenge due to complex gene networks and pathways. These networks exhibit pleiotropy, complicating the determination of specific gene and pathway functions. In this review, we explore how systems biology and single-cell sequencing technologies can aid in identifying candidate genes for co-ordinating specifics of plant growth and development within specific temporal and tissue contexts. Exploring sequence-function space of these candidate genes and pathway modules with synthetic biology allows us to test hypotheses and define genotype-phenotype relationships through reductionist approaches. Collectively, these techniques hold the potential to advance breeding and genetic engineering strategies while also addressing genetic diversity issues critical for adaptation and trait development.

MAMS: matrix and analysis metadata standards to facilitate harmonization and reproducibility of single-cell data

Many datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While biospecimen and experimental information is often captured, detailed metadata standards related to data matrices and analysis workflows are currently lacking. To address this, we develop the matrix and analysis metadata standards (MAMS) to serve as a resource for data centers, repositories, and tool developers. We define metadata fields for matrices and parameters commonly utilized in analytical workflows and developed the rmams package to extract MAMS from single-cell objects. Overall, MAMS promotes the harmonization, integration, and reproducibility of single-cell data across platforms.

AraLeTA: An Arabidopsis leaf expression atlas across diurnal and developmental scales

Mature plant leaves are a composite of distinct cell types, including epidermal, mesophyll, and vascular cells. Notably, the proportion of these cells and the relative transcript concentrations within different cell types may change over time. While gene expression data at a single-cell level can provide cell-type-specific expression values, it is often too expensive to obtain these data for high-resolution time series. Although bulk RNA-seq can be performed in a high-resolution time series, RNA-seq using whole leaves measures average gene expression values across all cell types in each sample. In this study, we combined single-cell RNA-seq data with time-series data from whole leaves to assemble an atlas of cell-type-specific changes in gene expression over time for Arabidopsis (Arabidopsis thaliana). We inferred how the relative transcript concentrations of different cell types vary across diurnal and developmental timescales. Importantly, this analysis revealed 3 subgroups of mesophyll cells with distinct temporal profiles of expression. Finally, we developed tissue-specific gene networks that form a community resource: an Arabidopsis Leaf Time-dependent Atlas (AraLeTa). This allows users to extract gene networks that are confirmed by transcription factor-binding data and specific to certain cell types at certain times of day and at certain developmental stages. AraLeTa is available at https://regulatorynet.shinyapps.io/araleta/.

Novel synthetic inducible promoters controlling gene expression during water-deficit stress with green tissue specificity in transgenic poplar

Synthetic promoters may be designed using short cis-regulatory elements (CREs) and core promoter sequences for specific purposes. We identified novel conserved DNA motifs from the promoter sequences of leaf palisade and vascular cell type-specific expressed genes in water-deficit stressed poplar (Populus tremula × Populus alba), collected through low-input RNA-seq analysis using laser capture microdissection. Hexamerized sequences of four conserved 20-base motifs were inserted into each synthetic promoter construct. Two of these synthetic promoters (Syn2 and Syn3) induced GFP in transformed poplar mesophyll protoplasts incubated in 0.5 M mannitol solution. To identify effect of length and sequence from a valuable 20 base motif, 5' and 3' regions from a basic sequence (GTTAACTTCAGGGCCTGTGG) of Syn3 were hexamerized to generate two shorter synthetic promoters, Syn3-10b-1 (5': GTTAACTTCA) and Syn3-10b-2 (3': GGGCCTGTGG). These promoters' activities were compared with Syn3 in plants. Syn3 and Syn3-10b-1 were specifically induced in transient agroinfiltrated Nicotiana benthamiana leaves in water cessation for 3 days. In stable transgenic poplar, Syn3 presented as a constitutive promoter but had the highest activity in leaves. Syn3-10b-1 had stronger induction in green tissues under water-deficit stress conditions than mock control. Therefore, a synthetic promoter containing the 5' sequence of Syn3 endowed both tissue-specificity and water-deficit inducibility in transgenic poplar, whereas the 3' sequence did not. Consequently, we have added two new synthetic promoters to the poplar engineering toolkit: Syn3-10b-1, a green tissue-specific and water-deficit stress-induced promoter, and Syn3, a green tissue-preferential constitutive promoter.

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.

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.

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

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

Dictionary learning for integrative, multimodal and scalable single-cell analysis

Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce 'bridge integration', a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a 'dictionary', which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit ( http://www.satijalab.org/seurat ), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities.

Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023-10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.

scPlantDB: a comprehensive database for exploring cell types and markers of plant cell atlases

Recent advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled the comprehensive profiling of gene expression patterns at the single-cell level, offering unprecedented insights into cellular diversity and heterogeneity within plant tissues. In this study, we present a systematic approach to construct a plant single-cell database, scPlantDB, which is publicly available at https://biobigdata.nju.edu.cn/scplantdb. We integrated single-cell transcriptomic profiles from 67 high-quality datasets across 17 plant species, comprising approximately 2.5 million cells. The data underwent rigorous collection, manual curation, strict quality control and standardized processing from public databases. scPlantDB offers interactive visualization of gene expression at the single-cell level, facilitating the exploration of both single-dataset and multiple-dataset analyses. It enables systematic comparison and functional annotation of markers across diverse cell types and species while providing tools to identify and compare cell types based on these markers. In summary, scPlantDB serves as a comprehensive database for investigating cell types and markers within plant cell atlases. It is a valuable resource for the plant research community.

How to explore what is hidden? A review of techniques for vascular tissue expression profile analysis

The evolution of plants to efficiently transport water and assimilates over long distances is a major evolutionary success that facilitated their growth and colonization of land. Vascular tissues, namely xylem and phloem, are characterized by high specialization, cell heterogeneity, and diverse cell components. During differentiation and maturation, these tissues undergo an irreversible sequence of events, leading to complete protoplast degradation in xylem or partial degradation in phloem, enabling their undisturbed conductive function. Due to the unique nature of vascular tissue, and the poorly understood processes involved in xylem and phloem development, studying the molecular basis of tissue differentiation is challenging. In this review, we focus on methods crucial for gene expression research in conductive tissues, emphasizing the importance of initial anatomical analysis and appropriate material selection. We trace the expansion of molecular techniques in vascular gene expression studies and discuss the application of single-cell RNA sequencing, a high-throughput technique that has revolutionized transcriptomic analysis. We explore how single-cell RNA sequencing will enhance our knowledge of gene expression in conductive tissues.

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.

Spatially resolved transcriptomic analysis of the germinating barley grain

Seeds are a vital source of calories for humans and a unique stage in the life cycle of flowering plants. During seed germination, the embryo undergoes major developmental transitions to become a seedling. Studying gene expression in individual seed cell types has been challenging due to the lack of spatial information or low throughput of existing methods. To overcome these limitations, a spatial transcriptomics workflow was developed for germinating barley grain. This approach enabled high-throughput analysis of spatial gene expression, revealing specific spatial expression patterns of various functional gene categories at a sub-tissue level. This study revealed over 14 000 genes differentially regulated during the first 24 h after imbibition. Individual genes, such as the aquaporin gene family, starch degradation, cell wall modification, transport processes, ribosomal proteins and transcription factors, were found to have specific spatial expression patterns over time. Using spatial autocorrelation algorithms, we identified auxin transport genes that had increasingly focused expression within subdomains of the embryo over time, suggesting their role in establishing the embryo axis. Overall, our study provides an unprecedented spatially resolved cellular map for barley germination and identifies specific functional genomics targets to better understand cellular restricted processes during germination. The data can be viewed at https://spatial.latrobe.edu.au/.

Perspectives on Computation in Plants

Plants thrive in virtually all natural and human-adapted environments and are becoming popular models for developing robotics systems because of their strategies of morphological and behavioral adaptation. Such adaptation and high plasticity offer new approaches for designing, modeling, and controlling artificial systems acting in unstructured scenarios. At the same time, the development of artifacts based on their working principles reveals how plants promote innovative approaches for preservation and management plans and opens new applications for engineering-driven plant science. Environmentally mediated growth patterns (e.g., tropisms) are clear examples of adaptive behaviors displayed through morphological phenotyping. Plants also create networks with other plants through subterranean roots-fungi symbiosis and use these networks to exchange resources or warning signals. This article discusses the functional behaviors of plants and shows the close similarities with a perceptron-like model that could act as a behavior-based control model in plants. We begin by analyzing communication rules and growth behaviors of plants; we then show how we translated plant behaviors into algorithmic solutions for bioinspired robot controllers; and finally, we discuss how those solutions can be extended to embrace original approaches to networking and robotics control architectures.

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.

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

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

Plant Single-Cell/Nucleus RNA-seq Workflow

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

Plant synthetic biology innovations for biofuels and bioproducts

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

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

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

Gene regulatory networks shape developmental plasticity of root cell types under water extremes in rice

Understanding how roots modulate development under varied irrigation or rainfall is crucial for development of climate-resilient crops. We established a toolbox of tagged rice lines to profile translating mRNAs and chromatin accessibility within specific cell populations. We used these to study roots in a range of environments: plates in the lab, controlled greenhouse stress and recovery conditions, and outdoors in a paddy. Integration of chromatin and mRNA data resolves regulatory networks of the following: cycle genes in proliferating cells that attenuate DNA synthesis under submergence; genes involved in auxin signaling, the circadian clock, and small RNA regulation in ground tissue; and suberin biosynthesis, iron transporters, and nitrogen assimilation in endodermal/exodermal cells modulated with water availability. By applying a systems approach, we identify known and candidate driver transcription factors of water-deficit responses and xylem development plasticity. Collectively, this resource will facilitate genetic improvements in root systems for optimal climate resilience.

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.

An Efficient and Universal Protoplast Isolation Protocol Suitable for Transient Gene Expression Analysis and Single-Cell RNA Sequencing

The recent advent of single-cell RNA sequencing (scRNA-seq) has enabled access to the developmental landscape of a complex organ by monitoring the differentiation trajectory of every specialized cell type at the single-cell level. A main challenge in this endeavor is dissociating plant cells from the rigid cell walls and some species are recalcitrant to such cellular isolation. Here, we describe the establishment of a simple and efficient protocol for protoplast preparation in Chirita pumila, which includes two consecutive digestion processes with different enzymatic buffers. Using this protocol, we generated viable cell suspensions suitable for an array of expression analyses, including scRNA-seq. The universal application of this protocol was further tested by successfully isolating high-quality protoplasts from multiple organs (petals, fruits, tuberous roots, and gynophores) from representative species on the key branches of the angiosperm lineage. This work provides a robust method in plant science, overcoming barriers to isolating protoplasts in diverse plant species and opens a new avenue to study cell type specification, tissue function, and organ diversification in plants.

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.

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.

Biological Parts for Plant Biodesign to Enhance Land-Based Carbon Dioxide Removal

A grand challenge facing society is climate change caused mainly by rising CO2 concentration in Earth's atmosphere. Terrestrial plants are linchpins in global carbon cycling, with a unique capability of capturing CO2 via photosynthesis and translocating captured carbon to stems, roots, and soils for long-term storage. However, many researchers postulate that existing land plants cannot meet the ambitious requirement for CO2 removal to mitigate climate change in the future due to low photosynthetic efficiency, limited carbon allocation for long-term storage, and low suitability for the bioeconomy. To address these limitations, there is an urgent need for genetic improvement of existing plants or construction of novel plant systems through biosystems design (or biodesign). Here, we summarize validated biological parts (e.g., protein-encoding genes and noncoding RNAs) for biological engineering of carbon dioxide removal (CDR) traits in terrestrial plants to accelerate land-based decarbonization in bioenergy plantations and agricultural settings and promote a vibrant bioeconomy. Specifically, we first summarize the framework of plant-based CDR (e.g., CO2 capture, translocation, storage, and conversion to value-added products). Then, we highlight some representative biological parts, with experimental evidence, in this framework. Finally, we discuss challenges and strategies for the identification and curation of biological parts for CDR engineering in plants.