Rice Genomics and Genetics 2025, Vol.16, No.4, 199-210 http://cropscipublisher.com/index.php/rgg 199 Research Insight Open Access Cell-type Specific Gene Regulatory Networks during Rice Grain Filling Revealed by scRNA-seq Yanfu Wang, Danyan Ding Institute of Life Sciences, Jiyang College of Zhejiang A&F University, Zhuji, 311800, Zhejiang, China Corresponding email: danyan.ding@jicat.org Rice Genomics and Genetics, 2025, Vol.16, No.4 doi: 10.5376/rgg.2025.16.0017 Received: 20 May, 2025 Accepted: 02 Jul., 2025 Published: 20 Jul., 2025 Copyright © 2025 Wang and Ding, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Wang Y.F., and Ding D.Y., 2025, Cell-type specific gene regulatory networks during rice grain filling revealed by scRNA-seq, Rice Genomics and Genetics, 16(4): 199-210 (doi: 10.5376/rgg.2025.16.0017) Abstract Rice grain filling is a critical determinant of yield and quality, relying on tightly coordinated gene regulation across diverse cell types. In this study, we applied single-cell RNA sequencing (scRNA-seq) to dissect the transcriptomic landscape of individual cell populations within developing rice grains, enabling the construction of cell-type specific gene regulatory networks (GRNs). We identified and classified distinct cell types involved in grain filling, characterized their unique gene expression patterns, and traced dynamic transcriptional changes across developmental stages. Through GRN inference, we uncovered key transcription factors and regulatory hubs governing starch biosynthesis, nutrient transport, protein accumulation, and stress responses, as well as their integration with hormonal and metabolic pathways. A focused case study on endosperm-specific starch biosynthesis revealed candidate regulators validated by transgenic and CRISPR-based approaches. Integration with spatial transcriptomics, proteomics, and metabolomics further reinforced the functional significance of these networks. These findings provide a high-resolution view of cell-type specific transcriptional regulation during rice grain filling, offering novel targets and strategies for genetic improvement of grain yield and quality. Keywords Rice grain filling; Single-cell RNA sequencing; Gene regulatory networks; Cell-type specificity; Transcriptional regulation 1 Introduction Grain filling in rice is a very crucial stage, directly affecting yield and quality. Rice is the staple food for more than half of the world's population. The efficiency of grain filling can affect the weight, size and nutritional components of grains, and thus has become an important goal in crop improvement and food security (Wang et al., 2008). The accumulation of starch accounts for the majority of the dry weight of rice, which is closely related to the grain-filling process. Therefore, to increase the yield and quality of rice, it is very important to optimize the grain-filling process (Xiao et al., 2025). The process of grouting is rather complex, involving the transportation of carbohydrates and the synthesis of starch during the development of endosperm. The speed and completeness of grout filling will directly affect the final weight and quality of the grains. Poor grouting will lead to reduced yield and decreased quality (Peng et al., 2013; Wei et al., 2017). Environmental conditions, such as temperature and light, as well as gene regulation, all play important roles in this process (Chen et al., 2022). The grains of rice have a variety of different cells, and each cell plays a different role in filling and development. Each cell type has its own gene regulatory network, which regulates processes such as cell division, cell expansion and nutrient accumulation, and ultimately determines the size and composition of the grain (Liu et al., 2022). Current research has found that transcription factors, micrornas and hormone signaling pathways can fine-tune gene expression in specific cells, which indicates that it is necessary to study regulatory mechanisms at the single-cell level (Panigrahi et al., 2021; Zhao et al., 2023). The traditional RNA sequencing method involves testing many cells together, which can mask the differences among various cell types. Single-cell RNA sequencing (scRNA-seq) technology can analyze gene expression at
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