Integrated single cell transcriptomics analysis for unraveling heterogeneity and plasticity of root cells for...

The plant root system architecture (RSA) functions in anchorage, acquisition of water and mineral nutrients, and exhibits pronounced phenotypic plasticity in response to the spatiotemporal heterogeneity of the soil environment. Resolving the regulatory networks that underpin root development is therefore a prerequisite for improving stress tolerance and yield. Single-cell RNA sequencing (scRNA-seq) resolves transcriptional landscapes at cellular resolution, discriminating root zonation, lineage trajectories and cell-type-restricted responses to environmental signals. Coupling scRNA-seq to epigenomic, proteomic and metabolomic profiling of the same cell populations links chromatin state to transcript, protein and metabolite output and therefore exposes the regulatory layers that govern root development and plasticity. Machine-learning models trained on single-cell matrices assist cell-type annotation, gene regulatory network inference and prioritization of candidate loci for precision breeding, although their output remains contingent on reference datasets that are still sparse for crop species. This review examines what scRNA-seq, spatial transcriptomics and machine learning have so far established about root cellular heterogeneity and regulatory architecture. This delimits the technical constraints that presently bound their application to crop improvement including protoplasting bias, transcript dropout and incomplete state of crop reference atlases to support sustainable and regenerative agriculture.