Computational Protocol To Quantify Artesunate-Associated Transcriptomic Shifts In Experimental Cerebral Malaria Brain...

RNA sequencing (RNA-seq) is widely used to define disease-associated transcriptional programs, but consistent, end-to-end workflows are required to ensure reproducible comparisons across experimental groups and to facilitate biologically interpretable outputs. Here, a complete RNA-seq analysis protocol is presented for evaluating brain transcriptomic changes in experimental cerebral malaria (ECM) and following artesunate treatment using the public dataset GSE162535. The workflow analyzes three groups-control brain (CB), ECM brain (MB), and artesunate-treated ECM brain (AB)-starting from an HTSeq-count matrix. After importing and formatting raw counts, the protocol constructs a DESeq2 dataset with CB as the reference level, filters low-count genes, and performs quality control using library-size visualization, principal component analysis, and sample distance clustering. Differential expression is then calculated for three primary contrasts (MB vs CB, AB vs MB, AB vs CB), with log2 fold-change shrinkage applied for stable effect-size estimation. The protocol exports full and significant differential-expression tables, generates volcano plots and MA plots, and produces heatmaps of the most variable genes and top-ranked differentially expressed genes per contrast. To support mechanistic interpretation, the workflow includes targeted immune marker extraction (e.g., cytokines, chemokines, microglial activation markers, BBB/endothelial genes) and conducts Gene Ontology and KEGG enrichment analyses for significantly up- and downregulated gene sets. This protocol provides a reproducible template for characterizing ECM-associated neuroinflammatory transcriptional programs and for quantifying artesunate-associated modulation of these signatures.