The cellular heterogeneity associated with plant form and function is often overlooked in traditional bulk-tissue analyses. Emerging single-cell technologies provide new opportunities for dissecting this complexity at higher resolution. In this review, we summarize how single-cell multi-omics approaches, integrating transcriptomics, epigenomics, and spatial omics, can be used to characterize the regulatory landscapes associated with crop development, stress responses, and evolution. We discuss the application of these technologies across the crop life cycle, with a focus on identifying cell-type-specific programs related to key agronomic traits and tracing developmental trajectories. Furthermore, we describe how single-cell tools contribute to the analysis of plant responses to abiotic and biotic stresses and provide insights into the evolution of specialized cell types. We also discuss current challenges, including technical difficulties in protoplast isolation, the computational integration of multi-modal data, and scalability across diverse species. Finally, we outline potential future directions for combining machine learning and spatial transcriptomics to connect cellular-level observations with tissue-level functions, thereby supporting advances in functional genomics, precision breeding, and crop improvement.
Wang et al. (Mon,) studied this question.