From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches

Abstract

A significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales. Here, we summarize experimental data modalities, for characterizing the genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome, that enable us to generate large-scale immune profiles. We also discuss machine learning and network approaches that are commonly used to analyze and integrate these modalities, to gain insights into correlates and mechanisms of natural and vaccine-mediated immunity as well as therapy-induced immunomodulation.

Keywords: Systems immunology; machine learning; networks; therapeutics; vaccines.

Figure 1.

Single-cell perspectives frequently combine genomic, epigenomic, transcriptomic and spatial modalities to develop parallel “snapshots” of cellular function. Established antibody-omic techniques and target-agnostic proteomics are valuable complements that provide a holistic systems immunology perspective.

Figure 2.

High-level conceptual schematics of common computational multi-omics datasets integration methods including integrated visualization (top panel), factor analysis and matrix factorization (middle panel), and neural network-based methods (bottom panel).

Figure 3.

The top panel illustrates the application of methods like network propagation on biological networks guided by priors like expression and epigenetic information to uncover disease/trait relevant subnetworks or modules. The bottom panel depicts the recent use of generative deep learning models to integrate different biological networks in a transformed (reduced dimension) space.