Decoding plant complexity through spatially resolved multi-omics approaches

Spatial omics has rapidly emerged as a transformative method for decoding the molecular landscape of plant biology by integrating transcriptomic, proteomic, and metabolomic information with precise spatial context. In the current study, recent advances in spatial transcriptomics, proteomics, and metabolomics with high-resolution mapping of gene expression, protein localisation, and metabolite distribution across complex plant organs were quantitatively analysed. The analysis revealed a rapidly expanding research landscape, characterised by strong growth in publications, high international collaboration, and increasing emphasis on integrating multi-omics approaches to address plant development, stress responses, and nutritional traits. These technologies have unlocked opportunities to chart cellular heterogeneity, uncover regulatory networks, and associate molecular signatures with phenotypes relevant to growth and stress adaptation. Applications span diverse areas, including mapping gene expression in tissues and organs, decoding biotic and abiotic stress responses, and identifying metabolic pathways governing nutritional traits. Spatial omics also supports studies on translational regulation, providing insight into how mRNA translation shapes plant development under varying conditions. Epigenetic signatures, such as DNA methylation, histone modification, and chromatin organization are increasingly explored through spatially resolved approaches, revealing regulatory circuits at subcellular scales. Emerging techniques, including expansion microscopy, extend resolution to nanoscale dimensions, while spatial profiling of host-pathogen interactions clarifies infection dynamics and defence strategies within intact tissues. Despite rapid progress, challenges remain in plant spatial omics due to rigid cell walls, tissue heterogeneity, and the difficulty of achieving single-cell resolution in complex tissues. Computational hurdles, including data integration, quality control, and spatiotemporal modelling, further limit broad adoption. Future efforts must therefore refine tissue-compatible protocols, develop advanced bioinformatics pipelines, and integrate spatial omics with multi-omics and phenomics to link molecular patterns with traits. As these constraints are addressed, spatial omics will transition from descriptive studies to predictive breeding, enabling precise trait improvement, enhanced nutritional value, and sustainable crop productivity under environmental change.