Rebooting Human Immunology

Abstract

Recent progress in both conceptual and technological approaches to human immunology have rejuvenated a field that has long been in the shadow of the inbred mouse model. This is a healthy development both for the clinical relevance of immunology and for the fact that it is a way to gain access to the wealth of phenomenology in the many human diseases that involve the immune system. This is where we are likely to discover new immunological mechanisms and principals, especially those involving genetic heterogeneity or environmental influences that are difficult to model effectively in inbred mice. We also suggest that there are likely to be novel immunological mechanisms in long-lived, less fecund mammals such as human beings since they must remain healthy far longer than short-lived rodents in order for the species to survive.

Keywords: CMV; human evolution; human immunology; systems immunology.

Figure 1

Systems immunology. (a) Mass cytometry is an example of high-dimensional single-cell technology enabling systems immunology. (b,c) This method allows for two principally different types of analyses: (b) a very detailed characterization of well-defined and rare cell types, such as antigen-specific T cells identified by MHC-peptide multimer staining, or (c) broad analyses across all cell populations in the system and their interdependencies.

Figure 2

(Figure appears on preceding page) From T cells to specificities. New methods allow an efficient path with which to go from a T cell of interest in a given disease or response to knowledge about shared specificities, clonal expansions, and specific peptide-MHC targets. Live T cells are extracted from blood or the tissue of interest and sorted into 96-well plates. This step can be done using index sorting to enable >15 cell surface markers to be associated with each well. The cells are lysed, poly A+ RNA is reverse transcribed, and both TCR chains and phenotypic markers are PCR amplified in three stages by using the protocol described in Reference 16. Finally, a bar code is attached to all the amplicons in a given well, and the wells can be combined and sequenced using a high-throughput device such as Illumina MiSEQ. Pipeline software is used to establish a consensus sequence for each well’s TCRs (to eliminate PCR errors) and to assign TCR sequences and phenotypic markers (on a positive or negative basis). Here clonal expansions typically indicate dominant clones in a given individual’s response. At this point, GLIPH analysis of TCRs across individuals can be used to identify specificity groups and to indicate restricting MHC alleles (see Figure 3). Reporter T cells can be made by transfecting reconstructed TCR pairs in αβ T cells. Candidate antigenic peptides can thereafter be generated in a number of ways, such as by screening yeast display libraries or by sequencing peptides extracted from MHC molecules, and be analyzed by mass spectroscopy. Alternatively, relevant genomes can be screened for possible epitopes by using MHC binding motif algorithms such as those in the Immune Epitope Database. Abbreviations: FACS, fluorescence-activated cell sorting; GLIPH, grouping of lymphocyte interactions by paratope hotspots; TF, transcription factor.

Figure 3

Grouping TCR sequences according to peptide-MHC specificity. New algorithms such as GLIPH use the observed common properties of αβ TCRs that share specificities to take raw TCR CDR3 sequences and to group those likely to share specificities, especially across individuals. This is a multistep process, as indicated in panel a. The typical results are shown in panel b. (c) Of 5,700 TCR β sequences from 22 South African subjects with latent Mycobacterium tuberculosis infection, we were able to identify more than 100 different specificity groups. Of the first five groups, by fitting the most stringent criteria, we correctly assigned HLA alleles (Table 1)and found the correct peptide antigen from a large collection of CD4⁺ epitopes curated by Sette and colleagues (23).

Figure 4

The role of CMV in shaping immune systems. (a) Network model showing dependencies between serum proteins and cell populations in the immune systems of healthy twins. Yellow nodes represent the 58% of all 126 measurements with reduced correlations in CMV+/− compared with CMV−/− monozygotic twin pairs. (b) Individuals are projected onto the immunological age axis in ImmuneSpace. The positions on this line are compared between CMV+ and CMV− individuals across age groups (mean ± SD). (c) Young (22–32-year-old), but not old (62–89-year-old), CMV+ individuals respond with higher titers of anti-influenza antibodies (28/0 days postvaccination). (d) C57BL/6 mice mock infected or infected with MCMV (Smith strain) and challenged with flu (IAV) intranasally 5 weeks later and viral titers assessed. Abbreviations: CMV, cytomegalovirus; LV, latent variable; MCMV, murine cytomegalovirus; oCMV, old (62–89-year-old) CMV+ individuals; PFU, plaque-forming unit; yCMV, young (22–32-year-old) CMV+ individuals.