Rice Genomics and Genetics 2025, Vol.16, No.4, 199-210 http://cropscipublisher.com/index.php/rgg 206 8 Integration with Other Omics and Validation Approaches 8.1 Combining scRNA-seq with spatial transcriptomics for localization of GRNs Single-cell RNA sequencing (scRNA-seq) enables us to see which genes each cell expresses, but it has a drawback - it "cannot reveal" the original location of these cells in the tissue. The spatial transcriptome (SRT) technology precisely fills this gap, as it can map gene expression to specific locations. Nowadays, many studies combine the use of the two methods. In this way, researchers can not only identify the regulatory networks (GRNS) in specific cell types, but also know the spatial layout of these networks in tissues. Some analytical tools, such as Seurat, SpaOTsc, Tangram and gimVI, have been able to match RNA-SEq data with spatial location information, estimate undetected gene expressions, and even draw possible spatial regulatory maps (Yan et al., 2024). These tools are very helpful for studying how cells interact and how the surrounding environment affects regulatory mechanisms (Williams et al., 2022). 8.2 Integration with proteomics and metabolomics for functional confirmation Just looking at the transcriptome data is not enough. After all, gene expression does not necessarily mean protein production. So, some researchers began to analyze scRNA-seq together with proteomic and metabolomic data. This multi-omics integration can provide a clearer view of exactly what is happening at the functional level of cells. Now there are some deep learning and graph models, such as MCNET and DEMOC, that can integrate transcriptome, proteome and epigenetic data, thereby more accurately identifying the key modules in the regulatory network (Zou et al., 2022; Tiwari and Trankatwar, 2023). More importantly, this approach can also verify whether the changes in mRNA are truly reflected in proteins and metabolites. This step is crucial for confirming the biological significance of the regulatory network (Fan et al., 2025). 8.3 Validation using gene editing and mutant analysis Although various analytical methods are powerful, paper inferences are not evidence after all. To confirm the true function of a certain gene or regulatory element, one still needs to conduct "hands-on experiments" for verification. Gene editing technologies like CRISPR/Cas9 and mutant analysis are all commonly used methods (Huang, 2024). By knocking out a gene, overexpressing it, or interfering with its regulatory region, we can observe how gene expression changes, whether proteins change, and whether the phenotypes of plants or cells also change (Rostom et al., 2017). These intervention experiments are like "practical tests" of previous computational predictions and are also an important part of transforming results from data into biological understanding (Bridges and Miller-Jensen, 2022). 9 Concluding Remarks Our understanding of the grain filling process of rice has actually undergone considerable changes in recent years. Especially after the emergence of single-cell RNA sequencing (scRNA-seq), it has enabled people to observe from a more detailed perspective how different each type of cell is in terms of gene regulation. This technology is not a solo effort. Together with other transcriptomics methods, it gradually unifies the regulatory relationships among genes, micrornas and targets during grain development. Take the miR1432-OsACOT pathway as an example. It was found to be related to fatty acid metabolism and hormone synthesis, and surprisingly, it affected the grouting speed and even the yield. The more such research there is, the clearer we can see the complex molecular mechanisms. In fact, under high-throughput sequencing, many new discoveries have been made: hundreds of mirnas have been found to be highly expressed only at a certain stage, and most of these mirnas are related to hormone balance, nutrient transport or starch accumulation. From this perspective, research at the single-cell level not only clarifies the regulatory map but also clarifies when and in which cells it takes effect, which is of great help to genetic improvement. These studies are not confined to the laboratory only. For instance, some research has been verified in the field - by suppressing miR1432 or modifying OsACOT, both the grain weight and total yield of rice were indeed increased. It is evident that to breed breeds that are more capable of filling, more nutritious, and more resistant to
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