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.
Abstract
Recent advances in single-cell and spatial omics technologies are transforming plant research by enabling cell-resolved analyses of gene expression, proteomics, chromatin organization, and metabolism at the level of individual cells and at spatial resolution. These approaches have revealed extensive cellular heterogeneity, rare and transient cell states, and previously unrecognized developmental and physiological programs. However, they also introduce conceptual, technical, and computational challenges related to data quality and integration, and biological interpretation. In this review, we summarize the emerging opportunities and key bottlenecks in plant single-cell and spatial biology. We discuss how embedding single-cell data within evolutionary developmental frameworks can accelerate the discovery of functionally relevant genes, and how spatial transcriptomics may enable comparative study designs, the study of cell-to-cell communication, and multi-organism spatial analyses. We also explore how integrating multiple datatypes and gene regulatory network inference can move the field from descriptive atlases toward mechanistic insights. Finally, we highlight the importance of visualization strategies across spatial, temporal, environmental, and evolutionary scales. Overall, we argue 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.
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.
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
Spatial omics technologies are providing new opportunities for plant biology by enabling molecular profiling within structurally intact tissues, revealing spatially organised cell states, developmental gradients, and regulatory interactions. While spatial transcriptomics has driven early advances, the field is rapidly expanding toward integrated spatial multi-omics by combining single-cell and spatial transcriptomic, epigenomic, proteomic, and metabolomic data. These approaches offer new opportunities to study development, physiology, and plant biotic and abiotic interactions in spatially preserved cellular contexts. However, despite rapid adoption, the field remains constrained by plant-specific challenges when applying technologies largely developed for animal systems. Compared with animal systems, plant tissues pose additional challenges due to rigid cell walls, and diverse chemistries, complicating sample preparation, cell and subcellular segmentation, signal detection, and data integration. As a result, many studies rely on bespoke protocols and analysis pipelines that are often difficult to reproduce or generalise. Here, we provide a practical, solution-oriented synthesis of current bottlenecks across experimental and computational pipelines, highlight emerging strategies to overcome these limitations, and propose a roadmap for community-driven protocol sharing, benchmarking, and integration across spatial and multi-omics modalities. Addressing these challenges will be essential to establish spatial omics as a routine and scalable tool for plant biology.
Min-Yao Jhu, Max Minne, Zi-Liang Luo et al.· The Plant Cell· 0 citations
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
Abstract Cell–cell communication (CCC) is essential for maintaining tissue organization and driving biological progression, yet its inference from transcriptomic data has long been limited by the absence of spatial context. Advances in spatial transcriptomics (ST) now enable mechanistically grounded analyses of CCC by preserving the physical organization of cells and their microenvironments. In this review, we examine recent methodological developments in CCC inference from ST data, focusing on how statistical, optimal transport, and deep learning frameworks incorporate spatial information to model ligand–receptor (LR) interactions and downstream signaling. We also summarize key mechanism-driven components shared across spatial and non-spatial CCC approaches. In addition, we discuss how tissue heterogeneity and spatial architecture can introduce context-dependent biases, particularly for permutation-based inference, and outline mechanistic considerations such as LR biochemistry, signal transduction, and condition-specific communication. We further highlight databases that curate intercellular conduction and intracellular signaling processes. By integrating spatial constraints with biochemical and computational principles, this review offers an integrated assessment of the opportunities and limitations of current approaches. We conclude by identifying key methodological challenges and future directions for developing robust, scalable, and mechanistically interpretable CCC inference as ST technologies continue to advance.
Yue-Song Wu, Hao-Hao Su, Yue-Hua Cui· Briefings in Bioinformatics· 0 citations
This review synthesizes recent progress across diverse plant species and tissues, showing that gene expression is not only cell-type specific but also tightly organized by position within organs and developmental niches, establishing spatial gene expression as a fundamental organizing principle of plant development and physiology.
Yiqing Wang, Zhengzhi Tan, Nicole A Freeman et al.· Plant Communications· 0 citations
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