🧬 Single-nucleus transcriptome analysis of Arabidopsis thaliana roots infected with Phytophthora capsici

Introduction

Understanding how plant roots coordinate immune responses at the cellular level is fundamental for dissecting host–pathogen interactions. In this study, we employed single-nucleus RNA sequencing (snRNA-seq) to profile the Arabidopsis thaliana root transcriptome following infection with the oomycete pathogen Phytophthora capsici.

By capturing transcriptional changes at 24 hours post-infection (24 hpi), we generated a high-resolution cellular atlas that reveals the spatial and functional dynamics of plant immune responses. Using Seurat-based integration, we analyzed over 26,000 nuclei and identified 12 transcriptionally distinct cell clusters representing major root tissues and cell types.

Comparative analyses between infected and mock samples highlighted the activation of key defense and hormone signaling pathways, including salicylic acid (SA) and ethylene (ET) responses, alongside oxidative stress mechanisms. Conversely, mock-treated roots were enriched for metabolic and translational processes.

Gene Ontology (GO) enrichment and marker gene expression profiling further revealed that immune responses to P. capsici are cell-type specific and involve distinct sets of defense-related genes. Together, this study provides a comprehensive reference for host transcriptional reprogramming during early stages of oomycete infection and establishes a framework for exploring plant–pathogen interactions at single-cell resolution.

This repository contains all scripts and workflows used for the analysis of single-nucleus RNA-seq (snRNA-seq) data from Arabidopsis thaliana roots infected with Phytophthora capsici (24 hours post infection). The pipeline integrates host–pathogen mapping, reference construction, data integration, clustering, differential expression, and Gene Ontology (GO) enrichment.

🧩 Requirements

To reproduce this workflow, install the following tools and R packages with the specified versions (or newer):

Tool / Package Version Description

🧰 Installation

# CRAN packages
install.packages(c("ggplot2", "dplyr", "cowplot"))

# Bioconductor packages
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install(c("Seurat", "clusterProfiler", "org.At.tair.db", "enrichplot"))

📂 Repository Structure

├── 1_ReadMapping/
│   ├── Cellranger_Filtred_mkgtf.sh
│   ├── Cellranger_mkref_Multispecies.sh
│   ├── Cellranger_mkref_ATH.sh
│   ├── CellRanger_Pos24hpi_1_scRNAseq_Multi_Genome.sh
│   └── README.md
│
├── 2_Integration_UMAP/
│   ├── Integration_Seurat_SCT.R
│   ├── UMAP_ClusterID.R
│   └── README.md
│
├── 3_CellMarkers_DEG/
│   ├── CellMarkers_DEG_Analysis.R
│   └── README.md
│
├── 4_GO_Annotation/
│   ├── GO_Enrichment_Annotation.R
│   ├── GO_Enrichment_ImmuneDefense.R
│   ├── GO_Enrichment_HormoneResponse.R
│   └── README.md
│
├── 5_Visualization/
│   ├── Heatmap_Markers.R
│   ├── DotPlot_FunctionalGenes.R
│   ├── FeaturePlot_CellMarkers.R
│   ├── FeaturePlot_SelectedMarkers.R
│   └── README.md
│
├── 6_QC_Summary/
│   ├── RelativeFraction_CellNumber.R
│   ├── UMI_Distribution.R
│   └── README.md
│
└── main_README.md  ← (this file)

🧬 Pipeline Overview

Step 1 - Read Mapping & Reference Preparation

Scripts:

Cellranger_Filtred_mkgtf.sh, Cellranger_mkref_Multispecies.sh, Cellranger_mkref_ATH.sh, CellRanger_Pos24hpi_1_scRNAseq_Multi_Genome.sh

Goal: build host-only and multi-species references and align reads with Cell Ranger. Outputs: multi-species reference (refdata-Arabidopsis_Pcapsici_MultiRef/), Arabidopsis-only reference (refdata-Arabidopsis_thaliana.TAIR10.59/), BAM files, filtered matrices.

Step 2 - Data Integration & UMAP Clustering

Scripts:

Integration_Seurat_SCT.R, UMAP_ClusterID.R

Integrates infected (Pos) and non-infected (Neg) samples using Seurat v5 SCTransform workflow, performs PCA, clustering, and UMAP visualization. Outputs: Integrated_Seurat_Object.rds, UMAP_Cluster_Labelled.png, Cluster_Metadata.csv

Step 3 — Marker Gene & Differential Expression

Script:

CellMarkers_DEG_Analysis.R

Identifies cluster-specific marker genes and DEGs between infected and control samples. Outputs: Cluster_Markers_All.csv, Infected_vs_NonInfected_DEG.csv, Volcano_DEG_Pos_vs_Neg.png, Top10_Markers_Heatmap.png

Step 4 — GO Enrichment and Functional Annotation

Scripts:

GO_Enrichment_Annotation.R, GO_Enrichment_ImmuneDefense.R, GO_Enrichment_HormoneResponse.R

Performs:

🧠 global GO enrichment (BP / MF / CC)

🦠 immune & defense pathway analysis

🌿 hormone response enrichment (SA / JA / ET)

Outputs: GO_Enrichment_Results.csv, ImmuneDefense_HormoneResponse_GO.csv, GO_Enrichment_Dotplot.png

Step 5 — Visualization

Scripts:

Heatmap_Markers.R, DotPlot_FunctionalGenes.R, FeaturePlot_CellMarkers.R, FeaturePlot_SelectedMarkers.R

Generates visual summaries for marker expression and functional genes. Outputs:

Script Visualization Heatmap_Markers.R Expression of top markers per cluster DotPlot_FunctionalGenes.R Defense/hormone gene expression across clusters FeaturePlot_CellMarkers.R UMAP of cluster-defining genes FeaturePlot_SelectedMarkers.R UMAPs of selected functional marker genes

Step 6 — Quality Control and Summary Metrics

Scripts:

RelativeFraction_CellNumber.R, UMI_Distribution.R

Quantifies cluster abundance and evaluates sequencing depth (UMIs). Outputs: Cluster_RelativeFraction_Barplot.png, UMI_Distribution_Violin.png, Cluster_CellNumber_RelativeFraction.csv

Step 7 — Focused Enrichment (Immune & Hormone Pathways)

Scripts:

GO_Enrichment_ImmuneDefense.R, GO_Enrichment_HormoneResponse.R

Highlights GO terms such as:

Defense response to oomycete (GO:0002239)

Response to salicylic acid (GO:0009751)

Response to jasmonic acid (GO:0009753)

Ethylene-activated signaling pathway (GO:0009873)

Outputs: GO_ImmuneDefense_Dotplot.png, GO_HormoneResponse_Dotplot.png

🧠 Interpretation Summary

Step	Biological Insight
Integration & Clustering	Defines transcriptionally distinct root cell populations.
Marker Analysis	Identifies genes defining clusters and infection-induced changes.
GO Enrichment	Highlights biological processes for defense, hormone, and stress pathways.
Visualization	Displays expression landscapes of tissue-specific and immune genes.
QC Metrics	Ensures balanced representation and sequencing consistency.