bioRxivPlant BiologyPreprint
Single-cell transcriptomic landscapes reveal cell-type-specific regulatory mechanisms of nutrient accumulation and transport in wheat grain
Zhaoheng Zhang, Xiaohui LiiD, Xuelei LiniD, Fuqiang Zhu, Qiangqiang Zhang, Jun Xiao, Yuan CheniD
Abstract
To elucidate the transcriptional regulatory mechanisms underlying wheat grain development, we constructed single-cell transcriptomic atlases of wheat grains at 4 and 8 days after pollination (DAP) using single-nucleus RNA sequencing. A total of 58,546 high-quality nuclei were clustered into 16 distinct cell populations representing all major seed tissues, including the embryo, aleurone layer, transfer cells, endosperm, inner pericarp, seed coat, and embryo surrounding region. Comparative analysis revealed a dynamic transcriptional reprogramming from growth-related pathways at DAP 4 to nutrient accumulation at DAP 8, which coincided with shifts in the expression of hormone signaling and organ development genes, as well as starch and storage protein genes. Pseudotime trajectory analysis identified two major differentiation branches, highlighting distinct roles in nutrient storage and support. Notably, the transcription factor TaMADS58 was found to regulate pericarp cell number while simultaneously enhancing protein content and processing quality. Concurrently, TaJEKLL, specifically expressed in nucellar projection cells, was demonstrated to influence nucellar projection development and nutrient transport. Natural variation in TaMADS58 and TaJEKLL revealed their potential to decouple yield from quality traits, offering promising applications for crop improvement. Collectively, this study provides a high-resolution single-cell resource for wheat seed biology and demonstrates that investigating gene expression with spatiotemporal precision at the single-cell level can facilitate the discovery of key genes for precisely regulating individual traits.
- organism: plant (other)
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 0
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