摘要Multicellular organisms,such as plants,are characterized by highly specialized and tightly regulated cell populations,establishing specific morphological structures and executing distinct functions.Gene regula-tory 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 annota-tions,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 demon-strate 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 regula-tors controlling the development of specific cell types in plants.