Decoding plant cell heterogeneity and dynamics across responses, development, to evolution with single-cell technologies

Abstract

Single-cell technologies are redefining plant cell identity. Traditional classifications based on position, morphology, and a few marker genes yielded static, coarse cell categories. In contrast, single-cell and single-nucleus RNA sequencing reveal hidden cellular heterogeneity and reconstruct developmental trajectories in ostensibly well-characterized plant tissues including vasculature and mesophyll. Environmental cues such as pathogen attack, drought, and wounding generate transient, spatially restricted cell states that bulk profiling masks, and these dynamics are best resolved by integrating single-cell data with spatial transcriptomics and live imaging. Comparative single-cell analyses extend these insights across evolution, revealing conserved core cell-type groups, lineage-specific innovations, and rapid transcriptomic rewiring in particular cell types. Emerging computational strategies mitigate orthology issues caused by genome duplications, enabling robust cross-species atlas alignment. These advances demonstrate that plant cell identity is dynamic, context-dependent, and distributed along continuous spectra. We argue that future frameworks should balance discrete cell-type labels with flexible state-based descriptions and integrate multiomic and spatial information to capture the full plasticity of plant cells, from ephemeral stress responses to millennial evolutionary changes.