High-resolution antibody dynamics of vaccine-induced immune responses

Abstract

The adaptive immune system confers protection by generating a diverse repertoire of antibody receptors that are rapidly expanded and contracted in response to specific targets. Next-generation DNA sequencing now provides the opportunity to survey this complex and vast repertoire. In the present work, we describe a set of tools for the analysis of antibody repertoires and their application to elucidating the dynamics of the response to viral vaccination in human volunteers. By analyzing data from 38 separate blood samples across 2 y, we found that the use of the germ-line library of V and J segments is conserved between individuals over time. Surprisingly, there appeared to be no correlation between the use level of a particular VJ combination and degree of expansion. We found the antibody RNA repertoire in each volunteer to be highly dynamic, with each individual displaying qualitatively different response dynamics. By using combinatorial phage display, we screened selected VH genes paired with their corresponding VL library for affinity against the vaccine antigens. Altogether, this work presents an additional set of tools for profiling the human antibody repertoire and demonstrates characterization of the fast repertoire dynamics through time in multiple individuals responding to an immune challenge.

Keywords: immunology; influenza; next-generation sequencing.

Fig. 1.

Neighbor-joining tree of V-use vectors. V-use vectors are calculated for each individual–isotype combination, and clustered by using the neighbor-joining algorithm. Each isotype is colored according to the legend. The tree naturally clusters by individual and then by isotype.

Fig. 2.

Probability of clone activation by VJ use. (A) Each clone is labeled according to its VJ use and whether it is naïve (IgM-only with low mutation) or activated (IgG or IgA with high mutation). The probability of observing naïve or activated clones is estimated assuming a binomial distribution. The line plots the estimated probability of activation, and bars represent ±1 SD. VJ combinations are ordered according to G.M.C. (B) For each VJ combination, we plot its VJ-use frequency rank against its rank of probability of activation. This is filtered on VJ combinations for which we obtain at least 100 clones that are classifiable as naïve or activated.

Fig. 3.

Vaccination clone dynamics colored by mutation. (A) Each layer represents a clone. Time is shown by the grid lines on the x axis, and labeled relative to the two vaccination events. The thickness of each layer is proportional to the frequency of that clone at that time point. Each clone is colored based on the average mutation level of the corresponding reads (see color bar for B). Only clones seen in at least two time points are shown here. (B) Histogram of the average mutation level of all of the clones. Each clone is counted once (i.e., clones are not weighted by the number of corresponding reads). Stream graphs are stacked bar charts with moving baselines; if we did not filter out clones in only a single time point, the total thickness of the graph would add up to unity.

Fig. 4.

Vaccination clone dynamics colored by onset time. Same as Fig. 3, except clones are colored by onset time. Onset times are ordered spectrally, so that all clones seen in the first time points are blue, followed by cyan, etc.

Fig. 5.

Dynamics of persistent clones. (A) Stream graphs of only clones that are observed in every single time point for a given individual. They are colored as in Fig. 3. (B) Distribution of average mutation level of the persistent clones.