Bastiaan O. R. Bargmann

Cannabis sativa ( Cannabis ) is a high‐value medicinal crop whose functional genomics and metabolic engineering remain constrained by limited genetic tools. The species is regarded as recalcitrant to stable transformation, and existing transient expression systems exhibit low efficiency or are restricted to hemp cultivars. Here, we report two Agrobacterium ‐mediated agroinfiltration protocols enabling rapid, robust, and reproducible transient gene expression in drug‐type, high‐THC Cannabis tissues. Using the RUBY betalain reporter system, we demonstrate transformation efficiencies of up to 100% in mature leaf disks and 81%–100% in whole seedlings. By independently comparing Agrobacterium strains and vector configurations, we demonstrate that transformation efficiency and transgene expression are influenced by distinct experimental variables, with EHA105 providing the highest transformation efficiency and p35S ‐based vectors producing the strongest reporter expression. Complementing these genetic tools, we developed a UPLC‐PDA method enabling simultaneous detection and quantification of cannabinoids and cannflavins. While not intended to replace mass spectrometry‐based analyses, the method provides an accessible and cost‐effective platform for comparative metabolite profiling during functional genomics and metabolic engineering studies. Together, these advances establish an integrated workflow linking high‐efficiency transient gene expression with streamlined biochemical analysis, providing a practical foundation for gene function studies, pathway interrogation, and biotechnology‐driven trait engineering in Cannabis .

Plant developmental biology lacks cell-based experimental systems comparable to the organoids and live-imaging platforms that have transformed mechanistic discovery in animal research. To address this gap, we present a robust, trackable protoplast regeneration platform in Arabidopsis thaliana that enables high-resolution, time-resolved analysis from the single-cell stage through microcolony formation and early regenerative development. Protoplasts are embedded in thin alginate matrices containing fluorescent fiducial beads, maintaining physical separation and allowing repeated return to the same cells over days to weeks. This design supports long-term imaging using epifluorescence, confocal and lattice light-sheet microscopy, enabling visualization of cell-cycle re-entry, asymmetric division, organelle dynamics, dedifferentiation, redifferentiation and regenerative competence. Fluorescent reporters for nuclei, membranes, microtubules, Golgi and hormone signaling further permit observation of subcellular organization and signaling heterogeneity during early reprogramming. Together, this platform provides an accessible, scalable system for studying plant cellular plasticity at single-cell resolution and offers a foundation for plant organoid-like models. By enabling high-resolution study of regeneration from single cells, this method expands the experimental toolkit and supports efforts to overcome species- and genotype-dependent barriers to transformation and plant biotechnology.

Transcription factors (TFs) are key regulators of expression of numerous genes. In wheat, many gene networks remain unresolved with the identification of TF target genes being particularly problematic. Methods developed for this purpose are not yet adequate. For example, ChIP-seq requires the use of transgenic plants which is still a lengthy process in wheat, while in vitro techniques such as DAP-seq are not effective on all families of TFs. To overcome these limitations, we have adapted the Arabidopsis thaliana TARGET system (Transient Assay Reporting Genome-wide Effects of Transcription factors) developed by Bargmann et al., 20131, to wheat. This in vivo system uses transformed protoplasts for the rapid, genome-wide identification of the direct targets of TFs. Please note: A correction has been made in the Materials section: the MaMg solution requires 150 µl of 1M MgCl2 for a final concentration of 15mM (and not 75 µl as stated in versions 1 and 2 of this protocol).

Plant development requires coordinated execution of gene regulatory programs across diverse cell types. While classical genetic and genomic approaches have revealed many of the genes required for plant growth and patterning, these methods often average signals across heterogeneous tissues, thereby obscuring how regulatory programs operate within individual cells. Therefore, resolving gene expression at the cellular level is essential for understanding how developmental decisions are made, integrated, and propagated during organ growth. The Arabidopsis thaliana root, with its simple anatomy and invariant cell lineages, provides an ideal system for addressing these questions. Recent advances in single-cell and single-nucleus transcriptomics have enabled construction of comprehensive cellular atlases that capture gene expression dynamics across cell identities and developmental trajectories. In this Expert View article, we highlight recent conceptual and technical developments that illustrate how single-cell atlases have transformed studies of root development. We emphasize how these atlases serve as community resources to inform the interpretation of new datasets, including those generated from mutants and in response to perturbation, as well as provide a platform for meta-analysis to initiate new studies. Using auxin signaling as a meta-analysis case study, we demonstrate how legacy transcriptomic data can be reinterpreted within a cell lineage-resolved framework. Finally, we highlight how spatial transcriptomics and rigorous data-sharing practices will extend cellular atlases across tissues and species, thereby enabling increasingly precise strategies for understanding and engineering plant growth and resilience.

Single somatic plant cells are capable of dedifferentiation and reversion to totipotency. However, understanding of dedifferentiation remains incomplete, limiting efforts to reprogram plant cell identities with engineered properties. Here, we describe the transcriptional dynamics of 125,091 Arabidopsis thaliana whole seedling protoplasts along a time course of dedifferentiation and varied chemical perturbations using single-cell RNA sequencing. We observed widespread cell-type-specific transcriptional reprogramming concomitant with cell wall digestion, including a guard cell-specific somatic embryogenic program initiated by ectopic LEC2 expression. Contrasting cell states between chemical perturbations across real time revealed that phytohormones antagonize cell aging, promote retention of transcriptional diversity, and selectively suppress wound responses to prioritize acquisition of totipotency. By dissecting time-resolved transcriptional dynamics, we discovered that dedifferentiation is non-uniform, accompanied by the emergence of heterogeneous stem cell-like identities, and associated with prevalent chromatin remodeling at the onset of dedifferentiation. Modeling dedifferentiation trajectories of diverse somatic cell types uncovered extensive bias in stem potential, including the identification of epithem-derived stem-like cells, variable dedifferentiation rates, and diversity in early reprogramming outcomes as a major source of dedifferentiation heterogeneity, despite discovery of a shared regulatory program underlying early dedifferentiation. Taken together, these data showcase how initial somatic cell identity, phytohormone signaling, chromatin remodeling, and reprogramming heterogeneity combine to coordinate the early transcriptional events defining cellular dedifferentiation.

Single-cell genomics is rapidly reshaping plant biology, yet broader adoption is limited by plant-specific technical constraints, fragmented tools, and inconsistent analytical practices. Here we report outcomes from the 2025 Summer Workshop for Plant Single-Cell Analysis, which convened researchers to define community needs and design shared solutions. Participants identified five priority challenge areas: (i) improving data quality through imputation, simulation, and deep generative modeling; (ii) developing automated, phylogenetically aware cell type annotation frameworks; (iii) reconstructing developmental trajectories and gene regulatory networks from single-cell and single-nucleus profiles; (iv) creating visualization approaches that embed transcriptional states into anatomically grounded plant organ contexts; and (v) using artificial intelligence (AI) agents and foundation models to orchestrate end-to-end single-cell workflows. In response, we established PlantSCHub, a community-curated web portal that aggregates protocols, datasets, and tutorials to support reproducible plant single-cell analysis. We outline conceptual roadmaps for cross-species integration, inference of multimodal trajectory and gene regulatory networks (GRNs), spatially anchored visualization, and AI scientist agents that dynamically coordinate analytical tools and literature. We discuss both scRNA-seq and scATAC-seq that are important for regulatory inference and cross-species analysis. Together, these efforts aim to transform isolated plant single-cell studies into an interoperable, evolving ecosystem that accelerates discovery and crop improvement.

Transcription factors (TFs) orchestrate gene expression programs by binding regulatory DNA sequences and modulating transcription of target genes. Identifying TF–target gene relationships is fundamental to understanding plant development, stress responses, and metabolic regulation. However, determining which genes a TF regulates remains technically challenging. This review provides a decision-oriented framework, that integrates experimental and computational plant TF–target identification. Placing emphasis on plant-specific constraints and practical method selection to guide researchers from initial TF discovery through comprehensive network characterization. We compare biochemical approaches (EMSA, Y1H), genome-wide mapping methods (ChIP-seq, DAP-seq, CUT&Tag), expression profiling techniques (RNA-seq on mutants and overexpression lines), and computational prediction tools (GENIE3, PTFSpot, ConnecTF). Critical trade-offs are discussed, between binding potential and functional regulation, throughput and resolution, and between different model and non-model plant systems. Finally, we highlight emerging technologies including high-throughput enhancer screening, single-cell approaches, and machine learning-based prediction platforms that promise to accelerate functional characterization of plant TFs and their regulatory networks.

Reporter-gene activation studies are essential for dissecting gene regulatory mechanisms, yet traditional whole-plant assays are often low-throughput and difficult to quantify. This chapter presents a streamlined method for analyzing reporter-gene activity using transiently transformed Arabidopsis thaliana mesophyll protoplasts. We describe the use of the pBeaconRFP vector for positive-fluorescent selection, enabling the isolation of successfully transformed cells via flow cytometry and fluorescence-activated cell sorting. Additionally, we introduce the pEvTV Gateway-compatible vector for flexible reporter-gene construct delivery and an automated pipeline for reproducible flow-cytometric data analysis. These methods facilitate rapid, robust, and scalable quantification of transcriptional responses, exemplified by the activation of the DR5 auxin-responsive reporter by gain-of-function Auxin Response Factor expression. The protocols are adaptable to other tissues and species, offering a versatile platform for high-throughput functional genomics.

There is a growing need for scalable and high-throughput model systems to study plant-pathogen interactions and fungal secondary metabolite production. This research addresses that need by exploring the use of callus culture as a proxy for whole-plant infection dynamics and mycotoxin biosynthesis. Both callus and intact Arabidopsis thaliana plants were inoculated with the fungal pathogen, Fusarium graminearum. Disease progression in plants was evaluated using a visual Fusarium-Arabidopsis Disease (FAD) rating system, while ergosterol, a marker of fungal biomass, was quantified in callus. The mycotoxin, deoxynivalenol, (DON) was measured in both plants and callus. Three A. thaliana accessions with known differences in susceptibility were assessed at 7, 14, and 21 days post-inoculation (DPI). In all greenhouse experiments, inoculated plants exhibited symptoms and signs of infection including mycelial growth, drying, and constriction. The three A. thaliana accessions varied significantly in mean FAD ratings (P = 0.03), when controlling for timepoint and experiment. DON was detected in flower and seed samples from infected A. thaliana plants. Inoculated calli exhibited symptoms of infection, and DON was measured in some but not all samples. Inconsistent DON detection may reflect detoxification by other fungi or the plants themselves. Mean ergosterol concentrations varied significantly across callus of different accessions (P = 0.02), when controlling for timepoint and experiment. These findings highlight distinctions in disease markers based on accession-specific susceptibility and time post-inoculation. Collectively, the results support the potential of callus culture as a simplified yet informative system for studying plant-pathogen interactions and developing phytosensors for fungal pathogen detection in the future.