A Reproducible Seurat-Based Protocol for Single-Cell RNA Sequencing Analysis of Peripheral Blood Mononuclear Cell...

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.