bioRxivBioinformaticsPreprint
Single nucleus transcriptome analysis of Arabidopsis thaliana roots infected with Phytophthora. capsici
Razan M. AlajoleeniD, Tran N ChauiD, Joel Shuman, Bastiaan O. R. BargmanniD, Song LiiD
Abstract
Understanding how plant roots coordinate immune responses at the cellular level is key to unraveling host-pathogen interactions. Using single-nucleus RNA sequencing (snRNA-seq) of Arabidopsis thaliana roots 24 hours after Phytophthora. capsici(P. capsici)inoculation, we captured the transcriptional landscape of early infection at single-cell resolution. Four libraries (two infected and two mock-treated) were generated with approximately 26,000 high-quality nuclei with consistent sequencing depth and viability. A reference-based pipeline distinguished host and pathogen transcripts, enabling species-resolved mapping and host-focused single-nucleus transcriptomic analysis. Integration and clustering identified 12 transcriptionally distinct root cell types, encompassing major tissues such as the meristem, cortex, endodermis, and vasculature. Cluster-specific marker analysis confirmed cell-type identities, while differential expression and Gene Ontology enrichment revealed a global transcriptional shift from metabolic and translational processes in mock samples to defense-, stress-, and pathogen-response pathways upon infection. Hormone-related enrichment indicated broad salicylic acid activation across root tissues, spatially confined ethylene signaling in vascular-associated clusters, and localized jasmonic acid responses in cortex and phloem. Together, these results provide a high-resolution view of Arabidopsis root immunity, highlighting a coordinated yet tissue-specific defense architecture in which salicylic acid underpins systemic protection, ethylene modulates vascular defense, and jasmonic acid contributes targeted reinforcement during early P. capsici infection.
- organism: Arabidopsis
- assay: snRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
- Citations
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