Single-cell and spatial multi-omics technologies in plants: exploring transcriptomics, proteomics, metabolomics, and emerging integrations

Plant life depends on the precise spatiotemporal coordination of gene expression, protein abundance, and metabolic activity. Although single-cell sequencing has transformed the resolution of cellular heterogeneity, tissue dissociation inevitably disrupts the positional context required to understand cell–cell communication and local regulatory environments. Spatial omics technologies address this limitation by mapping molecular states directly onto native tissue architecture. Here, we review the evolution from plant single-cell omics to spatially resolved profiling, with emphasis on plant-specific constraints such as rigid cell walls, extensive vacuolization, organellar RNA interference, and metabolite-driven autofluorescence. We further compare routinely deployable approaches with emerging proof-of-concept methods across transcriptomic, proteomic, and metabolomic layers, highlighting key considerations for platform selection in challenging plant tissues. Finally, we discuss the major barriers to multi-omics integration—including resolution mismatch, image registration, and temporal asynchrony—and outline a roadmap toward predictive, spatiotemporally resolved plant 'digital twins', with particular relevance for crop improvement in tropical species.