Rice Genomics and Genetics 2025, Vol.16, No.4, 199-210 http://cropscipublisher.com/index.php/rgg 202 plants are too hard, or they are directly made into protoplasts. However, this may cause stress and even affect the accuracy of the experiment (Sun et al., 2024). The subsequent process includes RNA extraction, reverse transcription, amplification, library construction, and then high-throughput sequencing. The final step is to classify the cells using computational methods, identify their respective marker genes, and attempt to restore the regulatory networks among them. Sometimes, if the samples are difficult to handle, researchers will also choose single-core RNA sequencing (snRNA-seq) as an alternative. It can avoid some deviations caused by cell disruption and can also handle frozen tissues (Wang et al., 2022). Of course, no matter which method is used, the quality of RNA and the control of experimental details will directly affect the final analysis result. 3.2 Advantages over bulk RNA-seq in resolving cell-type specific expression Traditional bulk RNA-seq often mixes a bunch of cells together for measurement, and the result is an average value. In this way, rare cells or the subtle differences between cells can be easily "smoothed out". Unlike scRNA-seq, it can observe the expression of each cell separately (Denyer et al., 2019). This enables us to identify some newly discovered cell types or observe the transformation trajectories of certain cells during their development. For example, when studying plant development or environmental response, the spatial distribution and regulatory patterns of cell expression are crucial (Shaw et al., 2020). Nowadays, many plants, especially model plants and crops, have established their own single-cell atlases. 3.3 Challenges in applying scRNA-seq to plant tissues However, when scRNA-seq is actually applied to plants, it is not without difficulties. The most common trouble is cell separation. Plant cells have thick cell walls, and some tissues are particularly complex. It is not easy to "disassemble" them into individual cells. Even if the isolation is successful, the RNA content of individual cells is very low and they are easily affected by noise. In addition, the possible stress response during the digestion of cells will also affect the data quality (Jovic et al., 2022). Furthermore, not all plants have complete reference maps, which also limits the depth of interpretation. Some people may have overlooked another issue: Batch effects and RNA degradation may cause bias, and at the same time, the requirements for analytical tools are also very high (Islam et al., 2024). However, over the years, the separation methods, database construction processes and data analysis techniques have all been continuously optimized. Although there are many challenges, these improvements are gradually expanding the application scope of this technology in plant research. 4 Cell-type Specific Transcriptomic Profiles during Rice Grain Filling 4.1 Identification and classification of distinct cell populations in the grain It is now quite clear that there are actually many types of cells in rice grains, and their functions vary greatly. Thanks to techniques such as laser capture microdissection (LCM) and RNA sequencing, researchers were able to analyze several major tissues in grains separately, such as endosperm, alomalone layer, transverse cells, pericarp epidermis, and ovule vascular bundles (OVT) (Ram et al., 2020). The tasks of each cell group in these tissues are not exactly the same: endosperm mainly stores nutrients, the aleurone layer participates in nutrient metabolism, transverse cells assist in transportation, the epidermis of the globule is related to the development of endosperm, and OVT is more like a bridge, responsible for bringing in nutrients from the mother. I didn't know much about this area in the past, but now it's gradually becoming clear. 4.2 Gene expression patterns unique to specific cell types There is a pattern to which genes are expressed by different tissues. By means of spatial transcriptomics, researchers have found that each type of cell has its own "preferred" set of genes. For example, the genes expressed in this part of OVT are closely related to hormone transducers and transporters, which also indicates that it is quite crucial in nutrient delivery (Wu et al., 2020). For instance, endosperm, where the genes are concentrated on starch and protein storage. The gene expression in the aleurone layer is rather complex and involves many metabolic regulations. In addition, the research also identified many cis-acting elements through promoter analysis, some of which had never been seen before. It is precisely these elements that enable different tissues to perform their respective duties during grouting, with some genes on and others off, clearly distinguishable.
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