Integrated Bulk and Single-Cell Transcriptomic and Metabolic Differences in Human and Mouse Immune Cells

T-cell populations from human and mouse models of chronic inflammatory disease, processed into samples and sequenced two ways: bulk RNA-seq for population-level expression and single-cell RNA-seq to see the cells individually.

Quality control of the raw FASTQ reads, adapter and quality trimming, splice-aware alignment to the reference genome and read quantification, then differential gene expression to find up- and down-regulated genes.

Built gene co-expression networks from the bulk expression matrix to find modules of genes that move together, correlated each module with immune-cell states and disease traits, and picked out the hub genes at the centre of each module.

Mapped the transcriptomic profiles onto a genome-scale metabolic network with constraint-based modelling to infer reaction-level metabolic activity, quantify flux differences, and find differentially active pathways and state-specific metabolic programs.

Cell-level quality control (genes and counts per cell, mitochondrial fraction), normalisation, PCA and UMAP, clustering and cell-type annotation (CD4+ and CD8+ T cells, Tregs, NK cells, B cells, monocytes), then trajectory inference and pseudotime to follow how cell states change.