(485a) Novel Experimental and Bioinformatic Methods for Accurate Characterization of Humoral Response Landscapes Based on Next-Generation Sequencing

Recent advances in DNA sequencing technology have enabled high-throughput systems analysis of immunological responses. Next-generation sequencing (NGS) has proven to aid or replace existing immunological technologies such as screening (e.g. hybridomas recombinant surface display, etc), antibody measurements (e.g. titers), and cellular characterization (e.g. ELISPOT, cell culture supernatant cytokine quantification, and flow cytometry). The methods presented here facilitate the characterization of immunomodulation by novel means, evaluating alterations to the entire humoral landscape. This analysis has demonstrated a proven ability to identify antigen-specific monoclonal antibodies without the need for time and energy consuming screening approaches. There is also great promise for utilizing these large data sets to quantitatively characterize the level of polarization of the humoral repertoire post-vaccination. Measurements such as these can be used to screen vaccine formulations to ensure patients are left with not only a strong immune response, but one that enables broad epitope protection. Formulations capable of inducing broad epitope protection would be generally preferred, as they would protect the patient from a greater amount of pathogen escape mutants. One potential pitfall of these NGS methods is the lack of uncertainty when quantifying the relative abundance between antibody clones, which compromises the quantitative accuracy of repertoire data. This uncertainty stems from systematic and stochastic biases introduced during sample preparation (i.e. PCR). This is especially problematic for samples derived from sources that currently rely on primer sets, where slight differences in individual primer annealing temperatures manifest into large systematic biases in clonal relative abundance. These primer sets used for NGS of antibody variable regions from mice and humans have relied on multiplex primers composing up to 150 different individual primers to compensate for the high diversity of V genes. Therefore, we are developing experimental and bioinformatics methods to ensure accurate quantitative assessment of clonal frequencies. These methods rely on generating cDNA with unique identifiers, incorporated by gene specific primers in the form of oligonucleotides with specified degenerate regions, to enable bias calibration data. During method development, we employed qPCR assessment to minimize bias effects due to over amplification. After PCR amplification, we then collect NGS sequencing data on the Illumina MiSeq platform enabling read lengths of 2x300, which has allowed us to also perform IgG isotype specific analysis. Identified biases are then able to be corrected using bioinformatics approaches to produce more accurate information about clonal relative abundance. Furthermore, by introducing unique identifiers for each transcript, we are also developing a process to recover bioinformatically relevant genes using primers targeting those unique identifiers (dial-out PCR). In addition to providing more accurate antibody amplicon NGS information, we are using various statistical approaches to characterize the degree of skewed clonal distributions post-immunization (e.g. Shannon entropy). These types of analyses will lead to improved strategies for creating better quantitative vaccine response characterization methods. We believe these strategies will be able to aid or replace quantitative serum based epitope mapping characterization, as a single method could be used to evaluate responses to all antigens and pathogens. This work will lead to increased speed and efficiency for profiling of vaccine-induced immune responses.