Systems immunology: Integrating multi-omics data to infer regulatory networks and hidden drivers of immunity

Abstract

The immune system is a highly complex and dynamic biological system. It operates through intracellular molecular networks and intercellular (cell-cell) interaction networks. Systems immunology is an emerging discipline that applies systems biology approaches of integrating high-throughput multi-omics measurements with computational network modeling to better understand immunity at various scales. In this review, we summarize key omics technologies and computational approaches used for immunological studies at both population and single-cell levels. We highlight the hidden driver analysis based on data-driven networks and comment on the potential of translating systems immunology discoveries to immunotherapy of cancer and other human diseases.

Keywords: Systems immunology; cell–cell communication; gene regulatory network; hidden driver analysis; scRNA-seq.

Figure 1.

Overview of the omics profiling technologies to characterize the immune system of human and mouse at population and single-cell levels.

Figure 2.

Overview of common computational analyses and algorithms in systems immunology.

Figure 3.

Hidden driver analysis by NetBID. (A) The overview flowchart of NetBID analysis to identify hidden drivers of phenotype case vs. control. (B) An illustration of an example hidden driver (HD) that has no differential expression but has network enrichment and activity. Diff-exp, differential expression.