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
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.
J. López-Castiblanco, L. López-Kleine, Yesid Cuesta-Astroz· Journal of Investigative Med...· 0 citations
The integrated Plant single-cell Database (iPscDB), an integrated and multifunctional platform that facilitates the integration and analysis of plant single-cell data, is presented, offering a practical and accessible resource for researchers with diverse technical backgrounds.
Peng Lu, Jing-Jing Jin, Jie-Meng Tao et al.· Nucleic Acids Research· 0 citations
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 developme...
This study introduces CellART, a unified framework designed to extract single-cell information across diverse high-resolution spatial transcriptomics platforms, including VisiumHD, Xenium, MERFISH, and Stereo-seq, and highlights its ability to fully leverage high-resolution ST data.
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 biolog...
Zlatka Fischer, Katharina Imkeller, Marcel H. Schulz et al.· Trends in Genetics· 2 citations
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