Department of Biology, University of York, York YO10 5DD, UK.
Plant Physiol. 2024 Jun 28;195(3):1941-1953. doi: 10.1093/plphys/kiae117.
Mature plant leaves are a composite of distinct cell types, including epidermal, mesophyll, and vascular cells. Notably, the proportion of these cells and the relative transcript concentrations within different cell types may change over time. While gene expression data at a single-cell level can provide cell-type-specific expression values, it is often too expensive to obtain these data for high-resolution time series. Although bulk RNA-seq can be performed in a high-resolution time series, RNA-seq using whole leaves measures average gene expression values across all cell types in each sample. In this study, we combined single-cell RNA-seq data with time-series data from whole leaves to assemble an atlas of cell-type-specific changes in gene expression over time for Arabidopsis (Arabidopsis thaliana). We inferred how the relative transcript concentrations of different cell types vary across diurnal and developmental timescales. Importantly, this analysis revealed 3 subgroups of mesophyll cells with distinct temporal profiles of expression. Finally, we developed tissue-specific gene networks that form a community resource: an Arabidopsis Leaf Time-dependent Atlas (AraLeTa). This allows users to extract gene networks that are confirmed by transcription factor-binding data and specific to certain cell types at certain times of day and at certain developmental stages. AraLeTa is available at https://regulatorynet.shinyapps.io/araleta/.
成熟植物叶片是由不同类型的细胞组成的，包括表皮细胞、叶肉细胞和维管束细胞。值得注意的是，这些细胞的比例和不同细胞类型中的相对转录浓度可能随时间而变化。虽然单细胞水平的基因表达数据可以提供细胞类型特异性的表达值，但获取这些数据进行高分辨率时间序列通常过于昂贵。虽然可以在高分辨率时间序列中进行 bulk RNA-seq，但使用整个叶片的 RNA-seq 则测量每个样本中所有细胞类型的平均基因表达值。在这项研究中，我们将单细胞 RNA-seq 数据与来自整个叶片的时间序列数据相结合，为拟南芥（Arabidopsis thaliana）构建了一个随时间变化的细胞类型特异性基因表达图谱。我们推断了不同细胞类型的相对转录浓度如何在昼夜和发育时间尺度上变化。重要的是，这项分析揭示了具有不同表达时间模式的 3 个亚群的叶肉细胞。最后，我们开发了组织特异性基因网络，形成了一个社区资源：拟南芥叶片时间依赖性图谱（AraLeTa）。这允许用户提取转录因子结合数据和特定细胞类型在特定时间点和特定发育阶段的特定基因网络。AraLeTa 可在 https://regulatorynet.shinyapps.io/araleta/ 上获取。
