Single-Cell and Spatial Transcriptomics in Plants

Single-cell and spatial transcriptomics (ST) have revolutionized plant biology by enabling the high-resolution analysis of cellular heterogeneity, developmental trajectories, and spatially patterned gene expression. Single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) generate transcriptional profiles at the individual-cell resolution, supporting the identification of discrete cell states, lineage relationships, and regulatory modules across diverse tissues. ST complements these dissociative techniques by preserving the positional context within intact organs and resolving spatially restricted expression domains, intercellular communication networks, and multicellular patterning processes that cannot be inferred from single-cell data alone. Despite rapid progress, major challenges constrain plant applications, including species-specific barriers to tissue dissociation, low transcript recovery from lignified or highly vacuolated cells, spatial resolution limits relative to plant cell size, and a lack of computational tools adapted to complex plant anatomy. To advance the field, continued innovation is essential in key areas, including optimized sample preparation protocols for diverse plant species, nucleus-based workflows to bypass protoplasting limitations, improved spatial-barcoding chemistries for higher-resolution mapping, and integrative multi-omic frameworks to link transcriptomics with other molecular layers. This chapter synthesizes the methodological breakthroughs that have expanded the application of single-cell and ST in plants, highlights key biological insights gained across model and crop species, and critically evaluates persistent technical and computational bottlenecks. Additionally, this review highlights emerging directions such as multimodal spatial profiling (combining transcriptomics with proteomics or metabolomics), comparative cross-species analyses to identify conserved cell-type regulatory programs, and predictive modeling of cell–cell communication networks. These advancements underscore the growing potential of single-cell and ST to drive mechanistic understanding of plant biology and accelerate precision crop improvement for global agricultural challenges.