Patterns of immune response to a vaccine or virus as measured by intracellular cytokine staining in flow cytometry: hypothesis generation and comparison of groups

COMPASS identifies T-cell subsets correlated with clinical outcomes

Advances in flow cytometry and other single-cell technologies have enabled high-dimensional, high-throughput measurements of individual cells as well as the interrogation of cell population heterogeneity. However, in many instances, computational tools to analyze the wealth of data generated by these technologies are lacking. Here, we present a computational framework for unbiased combinatorial polyfunctionality analysis of antigen-specific T-cell subsets (COMPASS). COMPASS uses a Bayesian hierarchical framework to model all observed cell subsets and select those most likely to have antigen-specific responses. Cell-subset responses are quantified by posterior probabilities, and human subject-level responses are quantified by two summary statistics that describe the quality of an individual's polyfunctional response and can be correlated directly with clinical outcome. Using three clinical data sets of cytokine production, we demonstrate how COMPASS improves characterization of antigen-specific T cells and reveals cellular 'correlates of protection/immunity' in the RV144 HIV vaccine efficacy trial that are missed by other methods. COMPASS is available as open-source software.

Mixture models for single-cell assays with applications to vaccine studies

Blood and tissue are composed of many functionally distinct cell subsets. In immunological studies, these can be measured accurately only using single-cell assays. The characterization of these small cell subsets is crucial to decipher system-level biological changes. For this reason, an increasing number of studies rely on assays that provide single-cell measurements of multiple genes and proteins from bulk cell samples. A common problem in the analysis of such data is to identify biomarkers (or combinations of biomarkers) that are differentially expressed between two biological conditions (e.g. before/after stimulation), where expression is defined as the proportion of cells expressing that biomarker (or biomarker combination) in the cell subset(s) of interest. Here, we present a Bayesian hierarchical framework based on a beta-binomial mixture model for testing for differential biomarker expression using single-cell assays. Our model allows the inference to be subject specific, as is typically required when assessing vaccine responses, while borrowing strength across subjects through common prior distributions. We propose two approaches for parameter estimation: an empirical-Bayes approach using an Expectation-Maximization algorithm and a fully Bayesian one based on a Markov chain Monte Carlo algorithm. We compare our method against classical approaches for single-cell assays including Fisher's exact test, a likelihood ratio test, and basic log-fold changes. Using several experimental assays measuring proteins or genes at single-cell level and simulations, we show that our method has higher sensitivity and specificity than alternative methods. Additional simulations show that our framework is also robust to model misspecification. Finally, we demonstrate how our approach can be extended to testing multivariate differential expression across multiple biomarker combinations using a Dirichlet-multinomial model and illustrate this approach using single-cell gene expression data and simulations.

Replication of associations between cytokine and cytokine receptor single nucleotide polymorphisms and measles-specific adaptive immunophenotypic extremes

Our objective was to replicate previously reported associations between cytokine and cytokine receptor SNPs and humoral and CMI (cell-mediated immune) responses to measles vaccine. All subjects (n=758) received two doses of MMR (measles/mumps/rubella) vaccine. From these subjects, candidate cytokine and cytokine receptor SNPs were genotyped and analyzed in 29-30 subjects falling into one of four "extreme" humoral (Ab(high/low)) and CMI (CMI(high/low)) response quadrants. Associations between seven SNPs (out of 11 in the discovery study) and measles-specific neutralizing antibody levels and IFN-γ ELISPOT responses were evaluated using chi-square tests. We found one replicated association for SNP rs372889 in the IL12RB1 gene (P=0.03 for Ab(high)CMI(high) vs. Ab(low)CMI(low)). Our findings demonstrate the importance of replicating genotypic-phenotypic associations, which can be achieved using immunophenotypic extremes and smaller sample sizes. We speculate that IL12RB1 polymorphisms may affect IL-12 and IL-23 binding and downstream effects, which are critical cytokines in the CMI response to measles vaccine.

Perforin expression directly ex vivo by HIV-specific CD8 T-cells is a correlate of HIV elite control

Many immune correlates of CD8⁺ T-cell-mediated control of HIV replication, including polyfunctionality, proliferative ability, and inhibitory receptor expression, have been discovered. However, no functional correlates using ex vivo cells have been identified with the known ability to cause the direct elimination of HIV-infected cells. We have recently discovered the ability of human CD8⁺ T-cells to rapidly upregulate perforin—an essential molecule for cell-mediated cytotoxicity—following antigen-specific stimulation. Here, we examined perforin expression capability in a large cross-sectional cohort of chronically HIV-infected individuals with varying levels of viral load: elite controllers (n = 35), viremic controllers (n = 29), chronic progressors (n = 27), and viremic nonprogressors (n = 6). Using polychromatic flow cytometry and standard intracellular cytokine staining assays, we measured perforin upregulation, cytokine production, and degranulation following stimulation with overlapping peptide pools encompassing all proteins of HIV. We observed that HIV-specific CD8⁺ T-cells from elite controllers consistently display an enhanced ability to express perforin directly ex vivo compared to all other groups. This ability is not restricted to protective HLA-B haplotypes, does not require proliferation or the addition of exogenous factors, is not restored by HAART, and primarily originates from effector CD8⁺ T-cells with otherwise limited functional capability. Notably, we found an inverse relationship between HIV-specific perforin expression and viral load. Thus, the capability of HIV-specific CD8⁺ T-cells to rapidly express perforin defines a novel correlate of control in HIV infection.

While the majority HIV-infected individuals progress to AIDS, a fraction of these individuals—for reasons not completely understood—do not develop AIDS and also display sustained control over viral replication; these subjects are sometimes referred to as elite controllers (EC). Prior evidence has shown that HIV-specific CD8⁺ T-cells, a component of adaptive immunity against intracellular pathogens, from EC exhibit enhanced functionality compared to individuals with progressive disease. Therefore, HIV-specific CD8⁺ T-cells likely play an important role in the favorable clinical outcomes witnessed in EC. We show in this study that the ability to control HIV replication in EC is associated with the expression of a protein called perforin, a critical molecule that enables CD8⁺ T-cells to directly kill infected cells - thereby preventing the spread of HIV to previously uninfected cells. In infected subjects with nonprogressive disease, we show that HIV-specific CD8⁺ T-cells demonstrate a superior ability to express perforin upon antigen-specific stimulation, whereas in progressors this property is diminished. Thus, we identify a functional capability of CD8⁺ T-cells, readily measured by standard intracellular cytokine staining assays, that potentially has a direct impact on HIV replication in vivo. These findings may, therefore, provide an important qualifier for future HIV vaccine research.

Conditioning in 2 x 2 tables

Two-by-two tables arise in a number of diverse settings in biomedical research, including analysis of data from a clinical trial with a binary outcome and gating methods in flow cytometry to separate antigen-specific immune responses from general immune responses. These applications offer interesting challenges concerning what we should really be conditioning on-the total number of events, the number of events in the control condition, etc. We give several biostatistics examples to illustrate the complexities of analyzing what appear to be simple data.