Unlocking immune-mediated disease mechanisms with transcriptomics

Abstract

The transcriptome represents the entire set of RNA transcripts expressed in a cell, reflecting both the underlying genetic and epigenetic landscape and environmental influences, providing a comprehensive view of functional cellular states at any given time. Recent technological advances now enable the study of the transcriptome at the resolution of individual cells, providing exciting opportunities to characterise cellular and molecular events that underpin immune-medicated diseases. Here, we draw on recent examples from the literature to highlight the application of advanced bioinformatics tools to extract mechanistic insight and disease biology from bulk and single-cell transcriptomic profiles. Key considerations for the use of available analysis techniques are presented throughout.

Keywords: gene expression and regulation; immunology; transcriptomics.

Figure 1.. An overview of applications for transcriptomics to better understand disease biology and extract mechanistic insight.

(A) Unsupervised hierarchical clustering can reveal novel molecular phenotypes which may inform endotype-based therapeutics. Gene expression signatures can also be used to derive biomarkers through supervised analyses. (B) Unsupervised gene co-expression networks can capture disease-associated changes in the network topology that may remain undetected through evaluation of gene expression levels alone. The use of both prior knowledge-based and data-driven bioinformatic tools can provide mechanistic insights into observed transcriptional changes (C), and offer a personalised view of the transcriptome in terms of both specific biological pathways, and network topology (D). (E) Single-cell RNA-sequencing provides opportunities for discovery through the identification of novel cell subsets, analysis of transitional states, and the study of cell–cell communication through ligand–receptor signalling. URA, Upstream Regulator Analysis; ChEA3, ChIP-X Enrichment Analysis 3; CARNIVAL, CAusal Reasoning pipeline for Network identification using Integer VALue programming; VIPER, Virtual Inference of Protein-activity by Enriched Regulon analysis; ARACNE, Algorithm for the Reconstruction of Accurate Cellular Networks; ssGSEA, single-sample Gene Set Enrichment Analysis; LIONESS, Linear Interpolation to Obtain Network Estimates for Single Samples.