Characterization of Influenza Vaccine Immunogenicity Using Influenza Antigen Microarrays

<div><p>Background</p><p>Existing methods to measure influenza vaccine immunogenicity prohibit detailed analysis of epitope determinants recognized by immunoglobulins. The development of highly multiplex proteomics platforms capable of capturing a high level of antibody binding information will enable researchers and clinicians to generate rapid and meaningful readouts of influenza-specific antibody reactivity.</p><p>Methods</p><p>We developed influenza hemagglutinin (HA) whole-protein and peptide microarrays and validated that the arrays allow detection of specific antibody reactivity across a broad dynamic range using commercially available antibodies targeted to linear and conformational HA epitopes. We derived serum from blood draws taken from 76 young and elderly subjects immediately before and 28±7 days post-vaccination with the 2008/2009 trivalent influenza vaccine and determined the antibody reactivity of these sera to influenza array antigens.</p><p>Results</p><p>Using linear regression and correcting for multiple hypothesis testing by the Benjamini and Hochberg method of permutations over 1000 resamplings, we identified antibody reactivity to influenza whole-protein and peptide array features that correlated significantly with age, H1N1, and B-strain post-vaccine titer as assessed through a standard microneutralization assay (p<0.05, <i>q</i> <0.2). Notably, we identified several peptide epitopes that were inversely correlated with regard to age and seasonal H1N1 and B-strain neutralization titer (p<0.05, <i>q</i> <0.2), implicating reactivity to these epitopes in age-related defects in response to H1N1 influenza. We also employed multivariate linear regression with cross-validation to build models based on age and pre-vaccine peptide reactivity that predicted vaccine-induced neutralization of seasonal H1N1 and H3N2 influenza strains with a high level of accuracy (84.7% and 74.0%, respectively).</p><p>Conclusion</p><p>Our methods provide powerful tools for rapid and accurate measurement of broad antibody-based immune responses to influenza, and may be useful in measuring response to other vaccines and infectious agents.</p></div

Comparison of a subset of manual gates and OpenCyto automated gates for a representative sample from the HVTN080 ICS data set.

<p>The automated gates are data-driven. Each panel shows a corresponding manual and automated gate side-by-side. The left panel is the manual gate; the right panel is the OpenCyto data-driven gate. Parent population names differ between manual and automated gates for singlets and lymphocytes because the automated gating hierarchy differs from the manual gating by including boundary and boundary debris gates, respectively, before these populations. Starting at the top left and proceeding along the rows, the gates shown are singlets, live cells, lymphocytes, CD3⁺ T-cells, CD4⁺ and CD8⁺ T-cells, IFN-γ⁺ and IL2⁺ expressing CD4⁺ and CD8⁺ T-cells, and Granzyme B⁺ and CD57⁺ expressing CD8⁺ T-cells. The manual and automated gates are very comparable.</p

The distribution of cells of each maturational state and their degree of functionality.

<p>The majority of naïve CD8 T cells (TN) do not express any cytokines (degree of functionality 0) or are mono-functional, while effector memory cells (TEM) are the most polyfunctional of the subsets (peaking at degree 5). Short-lived effector (TEF) cells have lower polyfunctionality (peaking at degree 4), and central memory (TCM) populations tend to have a constant level of polyfunctionality from degree1 through degree 7. The area under the curve for each cell subset integrates to one. The y-axis is transformed by a hyperbolic-arcsine to facilitate visualization of differences between subsets at higher degrees of polyfunctionality.</p

Performance metric of OpenCyto on the flow cytometry and CyTOF data sets, on a single-processor machine with 8 GB of RAM.

<p>OpenCyto can reproduce the FlowJo manual gates from a 16-workspace data set in 21 minutes with a peak memory usage of 1.8 GB. Once gated, the data occupies only 4.6 MB of RAM and is efficiently stored on disk in the HDF5/NetCDF format. Automated gating of the same data set using on OpenCyto GatingTemplate to generate data-driven gates for each of the 470 samples takes 1.74 hours on a single-processor. This can be parallelized across multiple cores for greater efficiency. The 420×2⁴ Boolean subsets of 4-cytokine producing cells can be generated and extracted efficiently, taking only 17 minutes for 7520 different subsets. Analogous results are shown for the CyTOF data, which has higher dimensionality. Calculating the Boolean subsets of 9 cytokine gates for the four maturation subsets in the data was extremely quick. In contrast, the 4×2⁹ Boolean subsets took 104 minutes to compute in FlowJo.</p><p>Performance metric of OpenCyto on the flow cytometry and CyTOF data sets, on a single-processor machine with 8 GB of RAM.</p

Comparison of OpenCyto automated gating and manual gating (performed with FlowJo and imported and reproduced in R using OpenCyto) for HVTN 080.

<p>A) Box-plots of the paired differences (post-vaccination – baseline) in proportions of cytokine-producing cells from significant cell subsets identified by the linear model (see Supplementary Methods) for each stimulation condition, gating method, and vaccine regimen. Differences between baseline and post-vaccination are background-corrected (stimulated – non-stimulated). There were no significant differences between the observed distributions for manual or OpenCyto gating (paired Wilcoxon test). B) Scatter plots comparing manual gating vs. OpenCyto gating. The per-subject, background-corrected difference between vaccine and baseline is plotted for OpenCyto and manual gating, with concordance correlation coefficients shown for all stimulations.</p

An overview of the OpenCyto infrastructure.

<p>When reproducing manual gating, raw FCS files and FlowJo workspace XML files are read into the R environment using <i>parseWorkspace</i>, creating a <i>GatingSet</i> object that represents the compensated, transformed and gated data stored in an <i>ncdfFlowSet</i> on disk. Cell populations annotated with gates can be visualized using <i>plotGate</i>, from the <i>flowViz</i> package Gating schemes can be visualized using <i>plot</i>. To perform automated gating, the user defines a <i>csv</i> representation of a gating tree, which is read by the <i>OpenCyto</i> package to generate a <i>gatingTemplate object</i>. This template can be applied to a <i>GatingSet</i> containing data, but no gates, provided the data uses the markers defined in the template. OpenCyto utilizes built-in automated gating methods, or external methods registered via a plug-in framework, to gate different cell subsets and populate the <i>GatingSet</i> with data-driven gate definitions for each sample. Manual and automated gating may be readily compared within a single framework. Cell populations and features can be extracted for further statistical analysis with other R and BioConductor software packages. Data (red boxes), software packages (blue boxes), framework functionality (gray boxes), and data flow/data structures (arrows/labeled arrows) are represented. <i>flowCore</i>, <i>flowStats</i>, and <i>flowViz</i>, are the <i>core</i> Bioconductor flow packages that benefit from the substantial infrastructure changes we have made to improve scalability and data visualization.</p

The average frequency of expression across two CyTOF samples for cytokine-producing cell subsets from four T-cell maturational states.

<p>Samples were stimulated with PMA-Ionomycin for 3 hours. Rows represent different maturational cell subsets (TN: naïve, TCM: central memory, TEF: effector, TEM: effector memory) and are clustered by Euclidean distance similarity. Columns represent different cytokine-producing cell subsets. The bottom legend defines the cell subset in a column. The legend is colored by degree of functionality of the cell subsets (light blue: degree 1, dark blue: degree 2, light green: degree 3, dark green: degree 4, salmon: degree 5, red: degree 6, orange: degree 7). The shading of individual blocks of the heatmap represents the average proportion of cells in the subset across the two samples, normalized to the total number of CD8 T-cells. Naïve cells have low polyfunctionality compared to effector, effector memory, and central memory cells.</p

Defective T Memory Cell Differentiation after Varicella Zoster Vaccination in Older Individuals

<div><p>Vaccination with attenuated live varicella zoster virus (VZV) can prevent zoster reactivation, but protection is incomplete especially in an older population. To decipher the molecular mechanisms underlying variable vaccine responses, T- and B-cell responses to VZV vaccination were examined in individuals of different ages including identical twin pairs. Contrary to the induction of VZV-specific antibodies, antigen-specific T cell responses were significantly influenced by inherited factors. Diminished generation of long-lived memory T cells in older individuals was mainly caused by increased T cell loss after the peak response while the expansion of antigen-specific T cells was not affected by age. Gene expression in activated CD4 T cells at the time of the peak response identified gene modules related to cell cycle regulation and DNA repair that correlated with the contraction phase of the T cell response and consequently the generation of long-lived memory cells. These data identify cell cycle regulatory mechanisms as targets to reduce T cell attrition in a vaccine response and to improve the generation of antigen-specific T cell memory, in particular in an older population.</p></div

Influence of pre-existing VZV immunity on vaccine responses.

<p>(A) VZV-specific T cell frequencies were determined by IFN-γ–specific ELISpot before (day 0) and at days 8±1, 14±1 and 28±3 after vaccination. **p<0.0001 by paired Wilcoxon-Mann-Whitney test. (B) Antibody indices determined by VZV-IgG-specific ELISA increased between day 0 and 28 (p<0.01). Data from individual vaccinees are joined with a line. (C and D) Fold change in VZV-specific antibody concentrations from day 0 to day 28 were negatively correlated with initial antibody concentrations (C, r² = 0.73, p <0.001), but not with initial VZV-specific T cell frequencies (D, p = 0.16). (E and F) Fold change in VZV-specific T cell frequencies from day 0 to day 28 showed no correlation with initial VZV-specific antibodies (E, p = 0.47) or initial T cell frequencies (F, p = 0.12). (G) Fold change in VZV-specific antibodies from day 0 to day 28 did not correlate with fold change in VZV-specific T cell frequencies from day 0 to day 28 (p = 0.38).</p

Correlation of whole blood-derived gene signatures with VZV-specific T cell responses.

<p>(A) Deconvolution of whole blood gene expression for leukocyte subsets was performed. Volcano plots show fold change of gene expression in monocytes (left), lymphocytes (middle) and neutrophils (right) between day 0 and day 1. The genes with significant changes in expression (fold change>1.5, p<0.05) after vaccination are colored in red. (B) Fold changes in monocyte-derived genes (day 0 to day 1, based on deconvolution analysis) were correlated with log-transformed changes in frequencies of antigen-specific T cells after VZV vaccination. Results are shown as volcano plots of correlation coefficients for VZV-specific T cell expansion (VZV-specific T cell frequencies day 0 to peak, left panel), contraction (peak to day 28, middle panel) and overall responses (day 28 to day 0, right panel). The genes with significant correlations (p<0.05) are colored in red. (C) The Venn diagram shows the overlap in genes that significantly correlate with expansion, contraction or global responses.</p