Jul 2026· Journal of Experimental Botany· 1 citation
Medicine
TL;DR
The current technological landscape of plant single-cell studies is summarized, key experimental and analytical challenges are discussed, and emerging strategies for validating single-cell discoveries are reviewed, highlighting their potential to accelerate the discovery of cell type-specific regulatory mechanisms underlying growth, stress resilience, and biomass production.
Abstract
Single-cell RNA sequencing (scRNA-seq) has transformed transcriptomic studies by enabling gene expression profiling at the resolution of individual cells within and across a broad range of tissue types, revealing cellular heterogeneity that is obscured in bulk tissue transcriptomes. Over the past decade, improvements in microfluidics and library preparation have drastically increased throughput, allowing tens of thousands of cells to be assayed in a single experiment. Although initially developed in animal systems, scRNA-seq has rapidly emerged as a powerful and widely adopted approach in plant biology. Beyond transcriptomics, the integration of single-cell data with chromatin accessibility, proteomics, metabolomics, and spatial omics is enabling a system-level understanding of plant gene regulation and cellular organization. Network-based analytical frameworks further support the reconstruction of gene regulatory networks and the interpretation of complex single-cell data. In this review, we summarize the current technological landscape of plant single-cell studies, discuss key experimental and analytical challenges, and review emerging strategies for validating single-cell discoveries. We also discuss future directions in applying single-cell technologies to woody perennials plants and bioenergy-relevant crops, emphasizing their potential to accelerate the discovery of cell type-specific regulatory mechanisms underlying growth, stress resilience, and biomass production.
It is argued that careful experimental design, robust data integration, and a clear understanding of cell identity are essential to obtain the full potential of plant single-cell and spatial omics in basic research and crop improvement.
L. De Veylder, Maite Saura-Sánchez, B. De Rybel et al.· The Plant Cell· 0 citations
Cellular diversity in multicellular organisms arises from the functional specialization of individual cells and the influence of both the local tissue microenvironment and external stimuli. Understanding this heterogeneity requires accurate characterization of cell types and the molecular dynamics that define them. In this context, transcriptomic technologies at the single-cell level have become central tools, as they provide a comprehensive view of gene expression and reveal functional molecular patterns. Recent advances have dramatically expanded the number of detectable transcripts and improved data resolution, shifting from bulk measurements that averaged signals across tissues to single‑cell approaches capable of quantifying gene expression at cellular resolution. This finer resolution enables detailed investigation of cellular functions, interactions, and transitions, and supports the development of multiscale computational models. Within this landscape, biological network-based approaches, particularly gene regulatory networks, have emerged as powerful tools for interpreting the functional organization of gene circuits. These methods facilitate the identification of biomarkers, regulatory factors, and key pathways, deepening our understanding of gene regulation and cellular identity through high‑resolution transcriptomic data. Transferring this knowledge to clinical practice is what we here refer to as precision health. This manuscript explores the current landscape of single-cell RNA sequencing (scRNA-seq), highlighting key studies that have leveraged this technology to advance biological understanding for clinical purposes through the construction of gene regulatory networks (GRNs) from single-cell transcriptomic data. Furthermore, it examines how these insights could contribute to clinical applications and, ultimately, the advancement of precision health. Finally, it discusses the key challenges in data analysis and practical applications within this rapidly evolving field.
J. López-Castiblanco, L. López-Kleine, Yesid Cuesta-Astroz· Journal of Investigative Med...· 0 citations
The rapid advancement of single-cell technologies has significantly enhanced our ability to investigate cellular heterogeneity within plant tissues. However, deciphering these intricate cellular landscapes requires processing high-dimensional gene expression matrices and integrating diverse datasets to enable accurate marker selection, cell identification, and other complex computational operations. These processes typically require broad programming expertise, posing a challenge for researchers with a limited computational background. To address this, we present integrated Plant single-cell Database (iPscDB), an integrated and multifunctional platform that facilitates the integration and analysis of plant single-cell data. iPscDB combines 4 688 428 cells and 288 139 curated cell markers derived from 946 experiments across 38 plant species. Wherever raw data were available, datasets were reprocessed through a single uniform pipeline, and both integration quality and automated cell-type annotation were benchmarked quantitatively. The platform introduces a Marker Confidence Level scheme that grades each cell-type marker by the strength and independence of its supporting evidence (from manually curated classic markers to database-derived associations), allowing users to judge marker reliability directly. The platform also provides a user-friendly online analysis pipeline and modules capable of processing raw FASTQ files or Cell Ranger-processed files. Users can configure parameters via an intuitive interface and utilize an integrated image editor to customize visualization outputs. Additionally, iPscDB supports various analyses, including cross-species gene expression, electronic Single-Cell Pictograph, and developmental trajectory. By streamlining the complex workflows of single-cell transcriptomics, iPscDB offers a practical and accessible resource for researchers with diverse technical backgrounds. iPscDB is accessible at https://www.tobaccodb.org/ipscdb/homePage.
Peng Lu, Jingjing Jin, Jie-Meng Tao et al.· Nucleic Acids Research· 0 citations
The biological complexity of plants arises from highly coordinated cellular activities. We propose that a "cellular spatiotemporal dogma" governs the zygote's programmed development into a complete plant and its adaptation to various environmental stresses, representing the set of principles describing how gene expression, cell identity, and function are coordinated across physical spatial contexts and temporal developmental progressions. Historically, our understanding of this dogma remains limited because traditional bulk tissue sequencing provides only a homogenized average of gene expression, making it challenging to isolate and resolve functionally significant but rare cell populations, such as the root quiescent center. However, the "resolution revolution" driven by single-cell and spatially resolved omics has dismantled these barriers to reveal cellular niches: local microenvironments where neighboring cells interact and coordinate function. Here, we firstly synthesize the current landscape of single-cell and spatial multi-omics technologies, highlighting how they bypass botanical barriers like the cell wall. Next, we detail how these technologies decode plant cellular characteristics, from defining novel cell subtypes to reconstructing dynamic trajectories and identifying pan-cell populations that remain ultra-conserved across hundreds of millions of years of evolution. Finally, we discuss the paradigm shift toward Large Foundation Models (FMs), which are computational paradigms that conceptualize biological data as a structured language to enable predictive modeling. Despite challenges such as data scarcity and phylogenetic bias, the integration of high-resolution omics with AI-driven intelligence is paving the way for a "Virtual Plant Cell, " a comprehensive digital model capable of simulating and predicting cellular responses to genetic and environmental perturbations, offering a transformative foundation for smart breeding and climate-resilient agriculture.
Understanding how different cell types assemble into tissues and organs, as well as how they interact to transmit and receive biological signals, is essential for advancing biomedical and biological research. Recent advancements in spatial transcriptomics (ST) technologies have opened new avenues for investigating biological systems by achieving subcellular spatial resolution. Since cells are the fundamental units of life, extracting single-cell information from high-resolution ST data is crucial. However, existing ST platforms often capture sparse transcript counts per spot or measure only a limited number of genes, complicating the extraction of comprehensive single-cell information. In this study, we introduce CellART, a unified framework designed to extract single-cell information across diverse high-resolution ST platforms, including VisiumHD, Xenium, MERFISH, and Stereo-seq. By leveraging multimodal data, such as staining images, spatial transcriptomics data, and single-cell RNA sequencing references, CellART simultaneously performs cell segmentation and cell type annotation through a seamless integration of deep learning and probabilistic modeling. We demonstrate the efficiency, generalizability, and robustness of CellART across various high-resolution spatial transcriptomics platforms, capable of processing datasets containing millions of spots. Comprehensive experiments validate the biological relevance and accuracy of the recovered cellular information within spatial configurations. Notably, we highlight the utility of CellART in breast and colorectal cancer datasets, showcasing its ability to fully leverage high-resolution ST data. By enhancing cellular resolution, CellART facilitates the identification of transient cancer cell states and immune cell subtypes. Furthermore, CellART enables investigations into cancer-immune cell communication, uncovering both established interactions and novel ligand-receptor pairs. The outputs of CellART are compatible with widely used community tools, facilitating a variety of downstream analyses.
Yu-Heng Chen, Yu-Yao Liu, Zhi-Wei Wang et al.· bioRxiv· 1 citation
Cell-cell communication (CCC) is involved in regulating cellular behavior in tissues. Spatial transcriptomics adds local context to gene expression, enabling more biologically grounded CCC inference than single-cell RNA-seq alone. Rapid method development has yielded diverse CCC methods, each addressing distinct biological questions through varied analytical frameworks. We review 33 recent methods and organize them into three categories: inference of communication networks at cell-type or single-cell resolution, modeling of microenvironment-driven transcriptional variability and regulatory modules, and estimation of spatially informed signaling gene co-associations and higher-order interaction structures. We offer a structured guide for method selection aligned with researchers' analytical goals, highlighting strengths, limitations, and key technical features to support the informed application of CCC inference methods in spatial transcriptomic research.
Zlatka Fischer, Katharina Imkeller, Marcel H. Schulz et al.· Trends in Genetics· 0 citations
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