Rice Genomics and Genetics 2025, Vol.16, No.4, 199-210 http://cropscipublisher.com/index.php/rgg 207 environmental stress, experience alone is not enough. The more we understand miRNA and the target genes they regulate, the more precisely we can apply our efforts. However, on the other hand, these advancements are also inseparable from a broader context: single-cell technology is gradually integrating with other omics methods (such as spatial transcriptomics, proteomics, and metabolomics) into a complete system. Although data analysis is still not an easy task, especially when there are significant cell differences and complex regulation, the current tools are far more powerful than ever before. In the future, if these methods can be widely applied to crop breeding, the process of studying in the fields should become faster and more accurate. Acknowledgments We are grateful to Mr. Xu for critically reading the manuscript and providing valuable feedback that improved the clarity of the text. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Bawa G., Liu Z., Yu X., Qin A., and Sun X., 2022, Single-cell RNA sequencing for plant research: insights and possible benefits, International Journal of Molecular Sciences, 23(9): 4497. https://doi.org/10.3390/ijms23094497 Bridges K., and Miller-Jensen K., 2022, Mapping and validation of scRNA-Seq-Derived cell-cell communication networks in the tumor microenvironment, Frontiers in Immunology, 13: 885267. https://doi.org/10.3389/fimmu.2022.885267 Cao R., Zhao S., Jiao G., Duan Y., Ma L., Dong N., Lu F., Zhu M., Shao G., Hu S., Sheng Z., Zhang J., Tang S., Wei X., and Hu P., 2022, OPAQUE3, encoding a transmembrane bZIP transcription factor, regulates endosperm storage protein and starch biosynthesis in rice, Plant Communications, 3(6): 100463. https://doi.org/10.1016/j.xplc.2022.100463 Chen Z.F., and Zhang D.P., 2024, Genome editing and rice improvement: the role of CRISPR/Cas9 in developing superior yield traits, Genomics and Applied Biology, 15(4): 182-190. https://doi.org/10.5376/gab.2024.15.0014 Chen H., Wang T., Gong Z., Lu H., Chen Y., Deng F., and Ren W., 2022, Low light conditions alter genome-wide profiles of circular RNAs in rice grains during grain filling, Plants, 11(9): 1272. https://doi.org/10.3390/plants11091272 Chen L., Dautle M., Gao R., Zhang S., and Chen Y., 2025, Inferring gene regulatory networks from time-series scRNA-seq data via GRANGER causal recurrent autoencoders, Briefings in Bioinformatics, 26(2): bbaf089. https://doi.org/10.1093/bib/bbaf089 Denyer T.X., Klesen S., Scacchi E., Nieselt K., and Timmermans M., 2019, Spatiotemporal developmental trajectories in the Arabidopsis root revealed using high-throughput single-cell RNA sequencing, Developmental Cell, 48(6): 840-852. https://doi.org/10.1016/j.devcel.2019.02.022 Durbha S., Siromani N., Jaldhani V., Krishnakanth T., Thuraga V., Neeraja C., Subrahmanyam D., and Sundaram R., 2024, Dynamics of starch formation and gene expression during grain filling and its possible influence on grain quality, Scientific Reports, 14: 6743. https://doi.org/10.1038/s41598-024-57010-4 Fan X., He Z., Guo J., Bu D., Han D., Qu X., Li Q., Cheng S., Han A., and Guo J., 2025, Leveraging TME features and multi-omics data with an advanced deep learning framework for improved cancer survival prediction, Scientific Reports, 15: 14282. https://doi.org/10.1038/s41598-025-98565-0 Fei H., Yang Z., Lu Q., Wen X., Zhang Y., Zhang A., and Lu C., 2021, OsSWEET14 cooperates with OsSWEET11 to contribute to grain filling in rice, Plant Science, 306: 110851. https://doi.org/10.1016/j.plantsci.2021.110851 Gao Z., Su Y., Tang J., Jin H., Ding Y., Cao R., Wei P., and Zheng C., 2025, AttentionGRN: a functional and directed graph transformer for gene regulatory network reconstruction from scRNA-seq data, Briefings in Bioinformatics, 26(2): bbaf118. https://doi.org/10.1093/bib/bbaf118 Hu L., Tu B., Yang W., Yuan H., Li J., Guo L., Zheng L., Chen W., Zhu X., Wang Y., Qin P., Ma B., and Li S., 2020, Mitochondria-associated pyruvate kinase complexes regulate grain filling in rice, Plant Physiology, 183(3): 1073-1087. https://doi.org/10.1104/pp.20.00279 Huang Y.M., 2024, Genomic insights into grain size and weight: the GS2 gene’s role in rice yield improvement, Plant Gene and Trait, 15(3): 141-151. http://dx.doi.org/10.5376/pgt.2024.15.0015
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