Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium; Center for Plant Systems Biology, VIB, 9052 Ghent, Belgium.
Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium; Center for Plant Systems Biology, VIB, 9052 Ghent, Belgium; Bioinformatics Institute Ghent, Ghent University, 9052 Ghent, Belgium.
Mol Plant. 2022 Nov 7;15(11):1807-1824. doi: 10.1016/j.molp.2022.10.016. Epub 2022 Oct 27.
Multicellular organisms, such as plants, are characterized by highly specialized and tightly regulated cell populations, establishing specific morphological structures and executing distinct functions. Gene regulatory networks (GRNs) describe condition-specific interactions of transcription factors (TFs) regulating the expression of target genes, underpinning these specific functions. As efficient and validated methods to identify cell-type-specific GRNs from single-cell data in plants are lacking, limiting our understanding of the organization of specific cell types in both model species and crops, we developed MINI-EX (Motif-Informed Network Inference based on single-cell EXpression data), an integrative approach to infer cell-type-specific networks in plants. MINI-EX uses single-cell transcriptomic data to define expression-based networks and integrates TF motif information to filter the inferred regulons, resulting in networks with increased accuracy. Next, regulons are assigned to different cell types, leveraging cell-specific expression, and candidate regulators are prioritized using network centrality measures, functional annotations, and expression specificity. This embedded prioritization strategy offers a unique and efficient means to unravel signaling cascades in specific cell types controlling a biological process of interest. We demonstrate the stability of MINI-EX toward input data sets with low number of cells and its robustness toward missing data, and show that it infers state-of-the-art networks with a better performance compared with other related single-cell network tools. MINI-EX successfully identifies key regulators controlling root development in Arabidopsis and rice, leaf development in Arabidopsis, and ear development in maize, enhancing our understanding of cell-type-specific regulation and unraveling the roles of different regulators controlling the development of specific cell types in plants.
多细胞生物，如植物，具有高度特化和严格调控的细胞群体，形成特定的形态结构并执行独特的功能。基因调控网络（GRN）描述了转录因子（TF）调节靶基因表达的条件特异性相互作用，为这些特定功能提供了基础。由于缺乏从植物单细胞数据中识别细胞类型特异性 GRN 的高效且经过验证的方法，限制了我们对模式物种和作物中特定细胞类型组织的理解，因此我们开发了 MINI-EX（基于单细胞表达数据的 motif 信息网络推断），这是一种推断植物中细胞类型特异性网络的综合方法。MINI-EX 使用单细胞转录组数据定义基于表达的网络，并整合 TF motif 信息来过滤推断的调控网络，从而提高网络的准确性。接下来，将调控网络分配到不同的细胞类型，利用细胞特异性表达，并使用网络中心性度量、功能注释和表达特异性对候选调节剂进行优先级排序。这种嵌入式优先级排序策略提供了一种独特而有效的方法，可以揭示控制感兴趣生物学过程的特定细胞类型中的信号级联。我们证明了 MINI-EX 对具有少量细胞的输入数据集的稳定性及其对缺失数据的稳健性，并表明它推断出的网络具有比其他相关单细胞网络工具更好的性能。MINI-EX 成功地鉴定了控制拟南芥和水稻根发育、拟南芥叶片发育以及玉米穗发育的关键调节剂，增强了我们对细胞类型特异性调控的理解，并揭示了不同调节剂在控制植物特定细胞类型发育中的作用。
