Cold damage is one of the most important environmental factors influencing crop growth, development, and production. In this study, we generated a pair of near-isogenic lines (NILs), Towada and ZL31, and Towada showed more cold sensitivity than ZL31 in the rice seedling stage. To explore the transcriptional regulation mechanism and the reason for phenotypic divergence of the two lines in response to cold stress, an in-depth comparative transcriptome study under cold stress was carried out. Our analysis uncovered that rapid and high-amplitude transcriptional reprogramming occurred in the early stage of cold treatment. GO enrichment and KEGG pathway analysis indicated that genes of the response to stress, environmental adaptation, signal transduction, metabolism, photosynthesis, and the MAPK signaling pathway might form the main part of the engine for transcriptional reprogramming in response to cold stress. Furthermore, we identified four core genes, OsWRKY24, OsCAT2, OsJAZ9, and OsRR6, that were potential candidates affecting the cold sensitivity of Towada and ZL31. Genome re-sequencing analysis between the two lines revealed that only OsWRKY24 contained sequence variations which may change its transcript abundance. Our study not only provides novel insights into the cold-related transcriptional reprogramming process, but also highlights the potential candidates involved in cold stress.

Abiotic stresses—drought, salinity, extreme temperature, flooding, and heavy-metal toxicity—constrain rice (Oryza sativa L.) yield worldwide, and the cellular programmes underlying them are unevenly distributed across cell types that bulk-tissue assays average together. This review examines, from a methodological standpoint, what single-cell and spatial omics technologies can and cannot establish about rice abiotic stress biology. We first define the modality space: single-cell omics measures RNA, chromatin accessibility, DNA methylation, protein, or metabolite features at the resolution of individual cells or nuclei, whereas spatial omics measures such features while retaining tissue coordinates; the two are complementary rather than interchangeable. We then treat each platform class—droplet-based scRNA-seq, combinatorial-indexing approaches including SPLiT-seq, nuclei-based snRNA-seq and multiome, sequencing-based and imaging-based spatial transcriptomics—under a common template covering measurement principle, the questions each can answer, applicability to rice tissues, dominant biases, and the inferences each cannot support. To make evidence strength comparable across a heterogeneous literature, we apply a four-tier scheme throughout: Tier A, direct rice cell-resolved or spatial evidence with functional or field validation; Tier B, robust rice functional and localization evidence without single-cell data; Tier C, cell-resolved evidence without causal validation; and Tier D, cross-species analogy or reasoned proposal. Applying this scheme shows that the genes with genuine breeding traction in rice—SUB1A, OsHKT1;5, OsHMA3, OsNRAMP5, DRO1—rest on Tier B evidence from classical genetics and field testing, whereas the most cell-resolved rice evidence concentrates in root outer layers and barrier formation at Tier C, and heat and cold stress, despite dominating yield loss, lack rice cell-resolved data almost entirely. We extend the discussion beyond transcriptomics to single-cell DNA methylome profiling, spatial proteomics and metabolomics, and three-dimensional analysis of thick plant tissues, in each case distinguishing demonstrated plant capability from mammalian-only capability, and we assess the expanding role of artificial intelligence in annotation, segmentation, batch correction, integration, and perturbation prediction alongside its documented failure modes. Rice, maize, and wheat are compared to identify transferable methodology. Cell-resolved omics has to date improved biological interpretation and candidate prioritization; demonstrating an incremental breeding advantage from it remains an unmet requirement.