Developmental CellPlant BiologyPeer-reviewed journal article
Single-Cell Transcriptomic Analysis Highlights Specific Cell Types of Wheat Manipulated by Fusarium graminearum Leading to Susceptibility
Abstract
Phytopathogenic fungi can be recognized by the plant immune system and trigger host defenses, but adapted pathogens cause susceptibility. How different cell types cooperate and orchestrate biological processes in response to heterogeneous colonization of organs by adapted and non-adapted pathogens remains largely unknown. Here we employed single-cell RNA sequencing to dissect the responses of wheat coleoptiles to infection by the adapted fungal pathogen Fusarium graminearum (Fgr) and the non-adapted fungal pathogen Fusarium oxysporum f. sp. cubense (Foc) at 1-, 2-, and 3-days post-inoculation. We profiled the transcriptomes of over 90,000 cells and identified eight major cell types in coleoptiles: stomata, epidermis, chlorenchyma, parenchyma, outer sheath, inner sheath, phloem, and procambium. Differential expression analyses showed that the capacity of different cell types to respond to fungal infection varied. The upregulation of immune pathways was compartmentalized in nonhost resistance to Foc, but widespread in susceptible interaction with Fgr. Pseudotime analyses revealed continuous cell state transitions in the disease progression of infected cell types. Our work indicates that the phloem and outer sheath are specific cell types that collaborate for the rapid onset of nonhost resistance. Fgr induces a state of low transcriptional activity in the chlorenchyma. Cell trajectory analysis suggests that the competition between immune and susceptible processes in parenchyma results in specific cell states that are favored by the adapted pathogen Fgr. Overall, this work explains how cell types collaborate and are manipulated during fungal infections, providing insight into the intercellular mechanisms of plant immunity.
- organism: plant (other)
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: cell-cell communication
- approach: deep learning
- approach: statistical model
- Citations
- 14
- FWCI
- 6.23
- Field percentile
- top 3%
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