New approaches to understanding the immune response to vaccination and infection

Abstract

The immune system is a network of specialized cell types and tissues that communicates via cytokines and direct contact, to orchestrate specific types of defensive responses. Until recently, we could only study immune responses in a piecemeal, highly focused fashion, on major components like antibodies to the pathogen. But recent advances in technology and in our understanding of the many components of the system, innate and adaptive, have made possible a broader approach, where both the multiple responding cells and cytokines in the blood are measured. This systems immunology approach to a vaccine response or an infection gives us a more holistic picture of the different parts of the immune system that are mobilized and should allow us a much better understanding of the pathways and mechanisms of such responses, as well as to predict vaccine efficacy in different populations well in advance of efficacy studies. Here we summarize the different technologies and methods and discuss how they can inform us about the differences between diseases and vaccines, and how they can greatly accelerate vaccine development.

Keywords: Computational immunology; Elastic net; Feature selection; High-throughput methods; Human immunology; Immune profiling; Regularization; Systems immunology; Vaccinology.

Figure 1. Multi-level high-throughput analysis of the human immune system

Comprehensive immune profiling involves multiple technological platforms that allow us to capture and observe an important portion of the immune response. Peripheral blood is used to survey the perturbations in the immune system through a suite of available techniques including next-generation sequencing (NGS); gene and protein microarrays; multiparameter flow cytometry and mass cytometry (CyTOF); multiplex cytokine and chemokine analysis by Luminex and Mesoscale, and metabolomics that relies on major recent improvements in mass spectrometry now capable of resolving close to a thousand metabolites. This human-centered approach to immunology promise to improve our understanding of the immune response to vaccination and infection.

Figure 2. Laplace versus Gaussian probability distribution

The figure shows the probability (Y-axis) of occurrence of a regression coefficient (x-axis) with Laplace prior (l1) (blue line) or Gaussian prior (l2) (red line). As can be observed, with l2/Gaussian as one moves away from zero the probability for such regression coefficient becomes progressively smaller, whereas in l1/Laplace the probability of both very small and large coefficients is higher than with l2/Gaussian.