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

Dual and spatially resolved drought responses in the Arabidopsis leaf mesophyll revealed by single-cell transcriptomics

Drought stress imposes severe challenges on agriculture by impacting crop performance. Understanding drought responses in plants at a cellular level is a crucial first step toward engineering improved drought resilience. However, the molecular responses to drought are complex as they depend on multiple factors, including the severity of drought, the profiled organ, its developmental stage or even the cell types therein. Thus, deciphering the transcriptional responses to drought is especially challenging. In this study, we investigated tissue-specific responses to mild drought (MD) in young Arabidopsis thaliana (Arabidopsis) leaves using single-cell RNA sequencing (scRNA-seq). To preserve transcriptional integrity during cell isolation, we inhibited RNA synthesis using the transcription inhibitor actinomycin D, and demonstrated the benefits of transcriptome fixation for studying mild stress responses at a single-cell level. We present a curated and validated single-cell atlas, comprising 50 797 high-quality cells from almost all known cell types present in the leaf. All cell type annotations were validated with a new library of reporter lines. The curated data are available to the broad community in an intuitive tool and a browsable single-cell atlas (http://www.single-cell.be/plant/leaf-drought). We show that the mesophyll contains two spatially separated cell populations with distinct responses to drought: one enriched in canonical abscisic acid-related drought-responsive genes, and another one enriched in genes involved in iron starvation responses. Our study thus reveals a dual adaptive mechanism of the leaf mesophyll in response to MD and provides a valuable resource for future research on stress responses.

GAEDGRN: reconstruction of gene regulatory networks based on gravity-inspired graph autoencoders

Reconstructing high-resolution gene regulatory networks (GRNs) based on single-cell RNA sequencing data provides an opportunity to gain insight into disease pathogenesis. At present, there are a large number of GRN reconstruction methods based on graph neural networks, and they can obtain excellent performance in GRN inference by extracting network structure features. However, most of these methods fail to fully exploit the directional characteristics or even ignore them when extracting network structural features. To this end, a novel framework called GAEDGRN is proposed based on gravity-inspired graph autoencoder (GIGAE) to infer potential causal relationships between genes. Among them, GIGAE can help us capture the complex directed network topology in GRN. Additionally, due to the uneven distribution of the latent vectors generated by the graph autoencoder, a random walk-based method is used to regularize the latent vectors learnt by the encoder. Furthermore, considering that some genes in GRN usually have a significant impact on biological functions, GAEDGRN designs a gene importance score calculation method and pays attention to genes with high importance in the process of GRN reconstruction. Experimental results on seven cell types of three GRN types show that GAEDGRN achieves high accuracy and strong robustness. Moreover, a case study on human embryonic stem cells demonstrates that GAEDGRN can help identify important genes.

A single-cell and spatial wheat root atlas with cross-species annotations delineates conserved tissue-specific marker genes and regulators

Despite the broad use of single-cell/nucleus RNA sequencing in plant research, accurate cluster annotation in less-studied plant species remains a major challenge due to the lack of validated marker genes. Here, we generated a single-cell RNA sequencing atlas of soil-grown wheat roots and annotated cluster identities by transferring annotations from publicly available datasets in wheat, rice, maize, and Arabidopsis. The predictions from our orthology-based annotation approach were next validated using untargeted spatial transcriptomics. These results allowed us to predict evolutionarily conserved tissue-specific markers and generate cell type-specific gene regulatory networks for root tissues of wheat and the other species used in our analysis. In summary, we generated a single-cell and spatial transcriptomics resource for wheat root apical meristems, including numerous known and uncharacterized cell type-specific marker genes and developmental regulators. These data and analyses will facilitate future cell type annotation in non-model plant species.

CoTF-reg reveals cooperative transcription factors in oligodendrocyte gene regulation using single-cell multi-omics

Oligodendrocytes are the myelinating cells within the central nervous system, but the mechanisms by which transcription factors (TFs) cooperate for gene regulation in oligodendrocytes remain unclear. We introduce coTF-reg, an analytical framework that integrates scRNA-seq and scATAC-seq data to identify cooperative TFs co-regulating the target gene (TG). First, we identify co-binding TF pairs in the same oligodendrocyte-specific regulatory regions. Next, we train a deep learning model to predict each TG expression using the co-binding TFs' expressions. Shapley interaction scores reveal high interactions between co-binding TF pairs, such as SOX10-TCF12. Validation using oligodendrocyte eQTLs and their eGenes that are regulated by these cooperative TFs show potential regulatory roles for genetic variants. Experimental validation using ChIP-seq data confirms some cooperative TF pairs, such as SOX10-OLIG2. Prediction performance of our models is evaluated through holdout data and additional datasets, and an ablation study is also conducted. The results demonstrate stable and consistent performance.

Gene regulatory networks in abiotic stress responses via single-cell sequencing and spatial technologies: Advances and opportunities

Understanding intricate gene regulatory networks (GRNs) orchestrating responses to abiotic stresses is crucial for enhancing climate resilience in crop plants. Recent advancements in single-cell and spatial technologies have revolutionized our ability to dissect the GRNs at unprecedented resolution. Here, we explore the progress, challenges, and opportunities these state-of-the-art technologies offer in delineating the cellular intricacies of plant responses to abiotic stress. Using scRNA-seq, the transcriptome landscape of individual plant cells along with their lineages and regulatory interactions can be unraveled. Moreover, coupling scRNA-seq with spatial transcriptomics provides spatially resolved gene expression and insights into cell-to-cell interactions. In addition, the chromatin accessibility assays can discover the regulatory regions governing abiotic stress responses. An integrated multi-omics approach can facilitate discovery of cell-type-specific GRNs to reveal the key components that coordinate adaptive responses to different stresses. These potential regulatory factors can be harnessed for genetic engineering to enhance stress resilience in crop plants.

Single-cell transcriptomics: a new frontier in plant biotechnology research

Single-cell transcriptomic techniques have ushered in a new era in plant biology, enabling detailed analysis of gene expression at the resolution of individual cells. This review delves into the transformative impact of these technologies on our understanding of plant development and their far-reaching implications for plant biotechnology. We present a comprehensive overview of the latest advancements in single-cell transcriptomics, emphasizing their application in elucidating complex cellular processes and developmental pathways in plants. By dissecting the heterogeneity of cell populations, single-cell technologies offer unparalleled insights into the intricate regulatory networks governing plant growth, differentiation, and response to environmental stimuli. This review covers the spectrum of single-cell approaches, from pioneering techniques such as single-cell RNA sequencing (scRNA-seq) to emerging methodologies that enhance resolution and accuracy. In addition to showcasing the technological innovations, we address the challenges and limitations associated with single-cell transcriptomics in plants. These include issues related to sample preparation, cell isolation, data complexity, and computational analysis. We propose strategies to mitigate these challenges, such as optimizing protocols for protoplast isolation, improving computational tools for data integration, and developing robust pipelines for data interpretation. Furthermore, we explore the practical applications of single-cell transcriptomics in plant biotechnology. These applications span from improving crop traits through precise genetic modifications to enhancing our understanding of plant-microbe interactions. The review also touches on the potential for single-cell approaches to accelerate breeding programs and contribute to sustainable agriculture. This review concludes with a forward-looking perspective on the future impact of single-cell technologies in plant research. We foresee these tools becoming essential in plant biotechnology, spurring innovations that tackle global challenges in food security and environmental sustainability. This review serves as a valuable resource for researchers, providing a roadmap from sample preparation to data analysis and highlighting the transformative potential of single-cell transcriptomics in plant biotechnology.

Maize mutant screens: from classical methods to new CRISPR-based approaches

Mutations play a pivotal role in shaping the trajectory and outcomes of a species evolution and domestication. Maize (Zea mays) has been a major staple crop and model for genetic research for more than 100 yr. With the arrival of site-directed mutagenesis and genome editing (GE) driven by the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), maize mutational research is once again in the spotlight. If we combine the powerful physiological and genetic characteristics of maize with the already available and ever increasing toolbox of CRISPR-Cas, prospects for its future trait engineering are very promising. This review aimed to give an overview of the progression and learnings of maize screening studies analyzing forward genetics, natural variation and reverse genetics to focus on recent GE approaches. We will highlight how each strategy and resource has contributed to our understanding of maize natural and induced trait variability and how this information could be used to design the next generation of mutational screenings.

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.

Inference and prioritization of tissue-specific regulons in Arabidopsis and Oryza

A regulon refers to a group of genes regulated by a transcription factor binding to regulatory motifs to achieve specific biological functions. To infer tissue-specific gene regulons in Arabidopsis, we developed a novel pipeline named InferReg. InferReg utilizes a gene expression matrix that includes 3400 Arabidopsis transcriptomes to make initial predictions about the regulatory relationships between transcription factors (TFs) and target genes (TGs) using co-expression patterns. It further improves these anticipated interactions by integrating TF binding site enrichment analysis to eliminate false positives that are only supported by expression data. InferReg further trained a graph convolutional network with 133 transcription factors, supported by ChIP-seq, as positive samples, to learn the regulatory logic between TFs and TGs to improve the accuracy of the regulatory network. To evaluate the functionality of InferReg, we utilized it to discover tissue-specific regulons in 5 Arabidopsis tissues: flower, leaf, root, seed, and seedling. We ranked the activities of regulons for each tissue based on reliability using Borda ranking and compared them with existing databases. The results demonstrated that InferReg not only identified known tissue-specific regulons but also discovered new ones. By applying InferReg to rice expression data, we were able to identify rice tissue-specific regulons, showing that our approach can be applied more broadly. We used InferReg to successfully identify important regulons in various tissues of Arabidopsis and Oryza, which has improved our understanding of tissue-specific regulations and the roles of regulons in tissue differentiation and development.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42994-024-00176-2.

Voice from both sides: a molecular dialogue between transcriptional activators and repressors in seed-to-seedling transition and crop adaptation

High-quality seeds provide valuable nutrients to human society and ensure successful seedling establishment. During maturation, seeds accumulate storage compounds that are required to sustain seedling growth during germination. This review focuses on the epigenetic repression of the embryonic and seed maturation programs in seedlings. We begin with an extensive overview of mutants affecting these processes, illustrating the roles of core proteins and accessory components in the epigenetic machinery by comparing mutants at both phenotypic and molecular levels. We highlight how omics assays help uncover target-specific functional specialization and coordination among various epigenetic mechanisms. Furthermore, we provide an in-depth discussion on the Seed dormancy 4 (Sdr4) transcriptional corepressor family, comparing and contrasting their regulation of seed germination in the dicotyledonous species Arabidopsis and two monocotyledonous crops, rice and wheat. Finally, we compare the similarities in the activation and repression of the embryonic and seed maturation programs through a shared set of cis-regulatory elements and discuss the challenges in applying knowledge largely gained in model species to crops.

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/.

Single-cell multi-omics analysis reveals cooperative transcription factors for gene regulation in oligodendrocytes

Oligodendrocytes are the myelinating cells within the central nervous system. Many oligodendrocyte genes have been associated with brain disorders. However, how transcription factors (TFs) cooperate for gene regulation in oligodendrocytes remains largely uncharacterized. To address this, we integrated scRNA-seq and scATAC-seq data to identify the cooperative TFs that co-regulate the target gene (TG) expression in oligodendrocytes. First, we identified co- binding TF pairs whose binding sites overlapped in oligodendrocyte-specific regulatory regions. Second, we trained a deep learning model to predict the expression level of each TG using the expression levels of co-binding TFs. Third, using the trained models, we computed the TF importance and TF-TF interaction scores for predicting TG expression by the Shapley interaction scores. We found that the co-binding TF pairs involving known important TF pairs for oligodendrocyte differentiation, such as SOX10-TCF12, SOX10-MYRF, and SOX10-OLIG2, exhibited significantly higher Shapley scores than others (t-test, p-value < 1e-4). Furthermore, we identified 153 oligodendrocyte-associated eQTLs that reside in oligodendrocyte-specific enhancers or promoters where their eGenes (TGs) are regulated by cooperative TFs, suggesting potential novel regulatory roles from genetic variants. We also experimentally validated some identified TF pairs such as SOX10-OLIG2 and SOX10-NKX2.2 by co-enrichment analysis, using ChIP-seq data from rat peripheral nerve.

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.

Studying plant vascular development using single-cell approaches

Vascular cells form a highly complex and heterogeneous tissue. Its composition, function, shape, and arrangement vary with the developmental stage and between organs and species. Understanding the transcriptional regulation underpinning this complexity thus requires a high-resolution technique that is capable of capturing rapid events during vascular cell formation. Single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) approaches provide powerful tools to extract transcriptional information from these lowly abundant and dynamically changing cell types, which allows the reconstruction of developmental trajectories. Here, we summarize and reflect on recent studies using single-cell transcriptomics to study vascular cell types and discuss current and future implementations of sc/snRNA-seq approaches in the field of vascular development.

Best practices for the execution, analysis, and data storage of plant single-cell/nucleus transcriptomics

Single-cell and single-nucleus RNA-sequencing technologies capture the expression of plant genes at an unprecedented resolution. Therefore, these technologies are gaining traction in plant molecular and developmental biology for elucidating the transcriptional changes across cell types in a specific tissue or organ, upon treatments, in response to biotic and abiotic stresses, or between genotypes. Despite the rapidly accelerating use of these technologies, collective and standardized experimental and analytical procedures to support the acquisition of high-quality data sets are still missing. In this commentary, we discuss common challenges associated with the use of single-cell transcriptomics in plants and propose general guidelines to improve reproducibility, quality, comparability, and interpretation and to make the data readily available to the community in this fast-developing field of research.

Leaf growth - complex regulation of a seemingly simple process

Understanding the underlying mechanisms of plant development is crucial to successfully steer or manipulate plant growth in a targeted manner. Leaves, the primary sites of photosynthesis, are vital organs for many plant species, and leaf growth is controlled by a tight temporal and spatial regulatory network. In this review, we focus on the genetic networks governing leaf cell proliferation, one major contributor to final leaf size. First, we provide an overview of six regulator families of leaf growth in Arabidopsis: DA1, PEAPODs, KLU, GRFs, the SWI/SNF complexes, and DELLAs, together with their surrounding genetic networks. Next, we discuss their evolutionary conservation to highlight similarities and differences among species, because knowledge transfer between species remains a big challenge. Finally, we focus on the increase in knowledge of the interconnectedness between these genetic pathways, the function of the cell cycle machinery as their central convergence point, and other internal and environmental cues.

MINI-AC: inference of plant gene regulatory networks using bulk or single-cell accessible chromatin profiles

Gene regulatory networks (GRNs) represent the interactions between transcription factors (TF) and their target genes. Plant GRNs control transcriptional programs involved in growth, development, and stress responses, ultimately affecting diverse agricultural traits. While recent developments in accessible chromatin (AC) profiling technologies make it possible to identify context-specific regulatory DNA, learning the underlying GRNs remains a major challenge. We developed MINI-AC (Motif-Informed Network Inference based on Accessible Chromatin), a method that combines AC data from bulk or single-cell experiments with TF binding site (TFBS) information to learn GRNs in plants. We benchmarked MINI-AC using bulk AC datasets from different Arabidopsis thaliana tissues and showed that it outperforms other methods to identify correct TFBS. In maize, a crop with a complex genome and abundant distal AC regions, MINI-AC successfully inferred leaf GRNs with experimentally confirmed, both proximal and distal, TF-target gene interactions. Furthermore, we showed that both AC regions and footprints are valid alternatives to infer AC-based GRNs with MINI-AC. Finally, we combined MINI-AC predictions from bulk and single-cell AC datasets to identify general and cell-type specific maize leaf regulators. Focusing on C4 metabolism, we identified diverse regulatory interactions in specialized cell types for this photosynthetic pathway. MINI-AC represents a powerful tool for inferring accurate AC-derived GRNs in plants and identifying known and novel candidate regulators, improving our understanding of gene regulation in plants.

Exploring the identity of individual plant cells in space and time

In recent years, single-cell genomics, coupled to imaging techniques, have become the state-of-the-art approach for characterising biological systems. In plant sciences, a variety of tissues and species have been profiled, providing an enormous quantity of data on cell identity at an unprecedented resolution, but what biological insights can be gained from such data sets? Using recently published studies in plant sciences, we will highlight how single-cell technologies have enabled a better comprehension of tissue organisation, cell fate dynamics in development or in response to various stimuli, as well as identifying key transcriptional regulators of cell identity. We discuss the limitations and technical hurdles to overcome, as well as future directions, and the promising use of single-cell omics to understand, predict, and manipulate plant development and physiology.

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.

Repressive ZINC FINGER OF ARABIDOPSIS THALIANA proteins promote programmed cell death in the Arabidopsis columella root cap

Developmental programmed cell death (dPCD) controls a plethora of functions in plant growth and reproduction. In the root cap of Arabidopsis (Arabidopsis thaliana), dPCD functions to control organ size in balance with the continuous stem cell activity in the root meristem. Key regulators of root cap dPCD including SOMBRERO/ANAC033 (SMB) belong to the NAC family of transcription factors. Here, we identify the C2H2 zinc finger protein ZINC FINGER OF ARABIDOPSIS THALIANA 14 ZAT14 as part of the gene regulatory network of root cap dPCD acting downstream of SMB. Similar to SMB, ZAT14-inducible misexpression leads to extensive ectopic cell death. Both the canonical EAR motif and a conserved L-box motif of ZAT14 act as transcriptional repression motifs and are required to trigger cell death. While a single zat14 mutant does not show a cell death-related phenotype, a quintuple mutant knocking out 5 related ZAT paralogs shows a delayed onset of dPCD execution in the columella and the adjacent lateral root cap. While ZAT14 is co-expressed with established dPCD-associated genes, it does not activate their expression. Our results suggest that ZAT14 acts as a transcriptional repressor controlling a so far uncharacterized subsection of the dPCD gene regulatory network active in specific root cap tissues.

A pan-grass transcriptome reveals patterns of cellular divergence in crops

Different plant species within the grasses were parallel targets of domestication, giving rise to crops with distinct evolutionary histories and traits1. Key traits that distinguish these species are mediated by specialized cell types2. Here we compare the transcriptomes of root cells in three grass species-Zea mays, Sorghum bicolor and Setaria viridis. We show that single-cell and single-nucleus RNA sequencing provide complementary readouts of cell identity in dicots and monocots, warranting a combined analysis. Cell types were mapped across species to identify robust, orthologous marker genes. The comparative cellular analysis shows that the transcriptomes of some cell types diverged more rapidly than those of others-driven, in part, by recruitment of gene modules from other cell types. The data also show that a recent whole-genome duplication provides a rich source of new, highly localized gene expression domains that favour fast-evolving cell types. Together, the cell-by-cell comparative analysis shows how fine-scale cellular profiling can extract conserved modules from a pan transcriptome and provide insight on the evolution of cells that mediate key functions in crops.

STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data

MOTIVATION

Single-cell RNA-sequencing (scRNA-seq) technologies provide an opportunity to infer cell-specific gene regulatory networks (GRNs), which is an important challenge in systems biology. Although numerous methods have been developed for inferring GRNs from scRNA-seq data, it is still a challenge to deal with cellular heterogeneity.

RESULTS

To address this challenge, we developed an interpretable transformer-based method namely STGRNS for inferring GRNs from scRNA-seq data. In this algorithm, gene expression motif technique was proposed to convert gene pairs into contiguous sub-vectors, which can be used as input for the transformer encoder. By avoiding missing phase-specific regulations in a network, gene expression motif can improve the accuracy of GRN inference for different types of scRNA-seq data. To assess the performance of STGRNS, we implemented the comparative experiments with some popular methods on extensive benchmark datasets including 21 static and 27 time-series scRNA-seq dataset. All the results show that STGRNS is superior to other comparative methods. In addition, STGRNS was also proved to be more interpretable than "black box" deep learning methods, which are well-known for the difficulty to explain the predictions clearly.

AVAILABILITY AND IMPLEMENTATION

The source code and data are available at https://github.com/zhanglab-wbgcas/STGRNS.

Single-cell analysis opens a goldmine for plant functional studies

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

Characterization of Gene Regulatory Networks in Plants Using New Methods and Data Types

A major question in plant biology is to understand how plant growth, development, and environmental responses are controlled and coordinated by the activities of regulatory factors. Gene regulatory network (GRN) analyses require integrated approaches that combine experimental approaches with computational analyses. A wide range of experimental approaches and tools are now available, such as targeted perturbation of gene activities, quantitative and cell-type specific measurements of dynamic gene activities, and systematic analysis of the molecular 'hard-wiring' of the systems. At the computational level, different tools and databases are available to study regulatory sequences, including intuitive visualizations to explore data-driven gene regulatory networks in different plant species. Furthermore, advanced data integration approaches have recently been developed to efficiently leverage complementary regulatory data types and learn context-specific networks.