A Reproducible Seurat-Based Protocol for Single-Cell RNA Sequencing Analysis of Peripheral Blood Mononuclear Cell CD4+ T Cells During Malaria Reinfection

Summary

Here, we present a reproducible Seurat-based protocol for the analysis of single-cell RNA sequencing data from peripheral blood mononuclear cell CD4⁺ T cells to characterize transcriptional heterogeneity and functional immune programs during malaria reinfection. This protocol enables consistent identification, comparison, and biological interpretation of dynamic CD4⁺ T-cell states and immune responses across conditions.

Abstract

Here, we present a reproducible Seurat-based protocol to analyze PBMC CD4⁺ T-cell single-cell RNA sequencing data across malaria reinfection timepoints. This protocol demonstrates a reproducible Seurat-based workflow for analyzing PBMC CD4⁺ T-cell scRNA-seq data across malaria reinfection timepoints, using representative publicly available datasets to demonstrate its application: a Plasmodium-specific TCR-transgenic CD4⁺ T-cell dataset (GSE233703) and a polyclonal CD4⁺ T-cell dataset comparing reinfection-associated timepoints (GSE233713; D27₍₃₎ versus D30). The workflow includes standardized preprocessing, integration, clustering, and downstream transcriptomic analyses within a unified computational framework. The method enables systematic computation of module scores for predefined immune and CD4⁺ T-cell programs, identification of cluster-specific marker genes, timepoint-resolved differential expression analysis, and downstream Gene Ontology and KEGG pathway enrichment. Application of this workflow identifies distinct CD4⁺ T-cell functional states and reveals dynamic transcriptional changes across malaria reinfection timepoints. The protocol generates standardized visualization outputs and tabulated results and provides practical guidance on parameter selection and troubleshooting, facilitating consistent and reproducible analysis of CD4⁺ T-cell scRNA-seq datasets in malaria and related immunological contexts. This protocol enables reproducible and biologically interpretable analysis of immune responses and can be applied to similar single-cell datasets in immunological research.

Introduction

Malaria remains a major global health burden, with repeated infections shaping host immunity in complex and incompletely understood ways¹. CD4⁺ T cells play a central role in antimalarial immune responses by coordinating effector cytokine production, supporting B-cell help, and regulating inflammation^2,3. During malaria reinfection, CD4⁺ T cells undergo dynamic transcriptional reprogramming, reflecting shifts among effector, regulatory, memory, proliferative, and exhausted states that influence both parasite control and immunopathology^4,

Protocol

Ethics statement:

All data used in this study were obtained from publicly available datasets (GSE233703 and GSE233713). The original studies complied with institutional and ethical guidelines for animal experimentation. This study involved secondary bioinformatics analysis of publicly available and de-identified single-cell RNA sequencing datasets obtained from the Gene Expression Omnibus (GEO) repository (GSE233703 and GSE233713). No new human participants, clinical samples, or identifiable patient information were involved in this study. According to the institutional and national guidelines for research involving publicl....

Results

Sequencing quality and cell-level quality control (Antigen-specific (PcAS-reactive) TCR-transgenic CD4⁺ T cells (GSE233703))

Pre-filter QC distributions (Figure 2A) showed heterogeneous transcript complexity, with most cells displaying moderate gene and UMI counts and a smaller subset showing high-count outlier profiles consistent with potential multiplets. Scatterplots (Figure 2B) showed a strong positive relationshi.......

Discussion

This study presents a standardized and reproducible Seurat-based workflow for analyzing CD4⁺ T-cell transcriptional dynamics during malaria reinfection. The protocol integrates adaptive quality control, normalization, dimensionality reduction, clustering, dataset integration, marker validation, module scoring, and differential expression analysis within a unified computational framework. Together, these analytical steps enabled reproducible identification and interpretation of biologically meaningful CD4⁺ T-c.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Name of Material / Equipment	Company / Source	Catalog Number	Comments / Description
10x Genomics–formatted count matrices	NCBI GEO	N/A	Matrix Market files (matrix.mtx, features.tsv, barcodes.tsv)
clusterProfiler (R package)	Bioconductor	N/A	RRID:SCR_016884; Functional enrichment analysis (GO, KEGG)
enrichplot (R package)	Bioconductor	N/A	RRID:SCR_017030; Visualization of enrichment analysis results
GEO Dataset GSE233703	NCBI Gene Expression Omnibus	GSE233703	PcAS-specific TCR-transgenic CD4+ T-cell scRNA-seq dataset
GEO Dataset GSE233713	NCBI Gene Expression Omnibus	GSE233713	Polyclonal CD4+ T-cell scRNA-seq dataset (D273 vs D30)
GitHub (optional)	GitHub Inc.	N/A	RRID:SCR_002630; Version control and reproducibility (optional)
glmGamPoi (R package)	Bioconductor	N/A	RRID:SCR_021001; Accelerated SCTransform model fitting
Matrix (R package)	CRAN	N/A	RRID:SCR_008389; Sparse matrix handling for scRNA-seq data
Operating system	Microsoft / Apple / Linux	N/A	Windows 10+, macOS, or Linux supported
org.Mm.eg.db (R package)	Bioconductor	N/A	RRID:SCR_002643; Mouse gene annotation database
patchwork (R package)	CRAN	N/A	RRID:SCR_018787; Multi-panel figure assembly
PDF viewer	Any	N/A	Viewing QC plots, UMAPs, and heatmaps
Personal computer or workstation	Any	N/A	Minimum 16–32 GB RAM recommended for integration
pheatmap (R package)	CRAN	N/A	RRID:SCR_016418; Heatmap visualization of gene expression
R Statistical Software (version ≥ 4.2)	R Foundation for Statistical Computing	N/A	RRID:SCR_001905; Core computational environment
RStudio Desktop	Posit Software	N/A	RRID:SCR_000432; Integrated development environment for R
Seurat (R package, v4 or later)	Satija Lab	N/A	RRID:SCR_016341; Single-cell RNA-seq analysis
tidyverse (R package suite)	CRAN	N/A	RRID:SCR_019186; Data manipulation and visualization