RESUMO
Plant morphogenesis relies on precise gene expression programs at the proper time and position which is orchestrated by transcription factors (TFs) in intricate regulatory networks in a cell-type specific manner. Here we introduced a comprehensive single-cell transcriptomic atlas of Arabidopsis seedlings. This atlas is the result of meticulous integration of 63 previously published scRNA-seq datasets, addressing batch effects and conserving biological variance. This integration spans a broad spectrum of tissues, including both below- and above-ground parts. Utilizing a rigorous approach for cell type annotation, we identified 47 distinct cell types or states, largely expanding our current view of plant cell compositions. We systematically constructed cell-type specific gene regulatory networks and uncovered key regulators that act in a coordinated manner to control cell-type specific gene expression. Taken together, our study not only offers extensive plant cell atlas exploration that serves as a valuable resource, but also provides molecular insights into gene-regulatory programs that varies from different cell types.
RESUMO
Single-cell transcriptomics has been fully embraced in plant biological research and is revolutionizing our understanding of plant growth, development, and responses to external stimuli. However, single-cell transcriptomic data analysis in plants is not trivial, given that there is currently no end-to-end solution and that integration of various bioinformatics tools involves a large number of required dependencies. Here, we present scPlant, a versatile framework for exploring plant single-cell atlases with minimum input data provided by users. The scPlant pipeline is implemented with numerous functions for diverse analytical tasks, ranging from basic data processing to advanced demands such as cell-type annotation and deconvolution, trajectory inference, cross-species data integration, and cell-type-specific gene regulatory network construction. In addition, a variety of visualization tools are bundled in a built-in Shiny application, enabling exploration of single-cell transcriptomic data on the fly.
Assuntos
Software , Transcriptoma , Transcriptoma/genética , Biologia Computacional , Perfilação da Expressão Gênica , Plantas , Análise de DadosRESUMO
Rice is a main food crop for more than half of the global population. The brown planthopper (BPH, Nilaparvata lugens) is one of the most destructive insect pests of rice. Currently, repeated overuse of chemical insecticides represents a common practice in agriculture for BPH control, which can induce insect tolerance and provoke environmental concerns. This situation calls for innovative and widely applicable strategies for rice protection against BPH. Here we report that the rice osa-miR162a can mediate cross-kingdom RNA interference (RNAi) by targeting the NlTOR (Target of rapamycin) gene of BPH that regulates the reproduction process. Through artificial diet or injection, osa-miR162a mimics repressed the NlTOR expression and impaired the oviposition of BPH adults. Consistently, overproduced osa-miR162a in transgenic rice plants compromised the fecundity of BPH adults fed with these plants, but meanwhile perturbed root and grain development. To circumvent this issue, we generated osa-miR162a-m1, a sequence-optimized osa-miR162a, by decreasing base complementarity to rice endogenous target genes while increasing base complementarity to NlTOR. Transgenic overexpression of osa-miR162a-m1 conferred rice resistance to BPH without detectable developmental penalty. This work reveals the first cross-kingdom RNAi mechanism in rice-BPH interactions and inspires a potentially useful approach for improving rice resistance to BPH. We also introduce an effective strategy to uncouple unwanted host developmental perturbation from desirable cross-kingdom RNAi benefits for overexpressed plant miRNAs.
