Multi-omic immunophenotyping to identify predictors of mRNA vaccine responsiveness in immunocompromised individuals

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Presentation Time: 03:15 PM - 04:30 PM
Poster Board Number: B902
Abstract ID: 4167

Presenting Author:
Sam M Murray , Graduate Student at Univ. of Oxford

Abstract:

Recent advances in -omics technologies enable detailed evaluation of the human immune system in the context of important clinical phenotypes. Here, we apply such technologies to investigate predictors of vaccine immune responsiveness in individuals with a range of immunosuppressive conditions who are vulnerable to COVID-19 vaccine failure.

Using bulk and single-cell RNA sequencing (scRNA-seq), Olink proteomics and spectral flow cytometry we assessed the pre- and 21 days post-third dose COVID-19 mRNA vaccine immunophenotype of 133 immunocompromised individuals and 22 healthy controls. Machine learning dependent analyses associated these phenotypes with vaccine-induced antibody and T cell responses.

We identified distinct pre-vaccination transcriptional signatures which predicted post-vaccine antibody responsiveness across immunosuppressive conditions. Signatures included increased B cell related gene modules and decreased inflammatory and monocyte related modules. These patterns were confirmed by detailed cellular and proteomic immunophenotyping. Persistence of this altered phenotype after vaccination was observed using scRNA-seq, which gave insights into the cellular identity of signatures related to vaccine responsiveness.

Through comprehensive immune profiling of pre- and post-vaccine blood samples from immunocompromised individuals we identify novel cellular and molecular signatures that predict vaccine responsiveness, and give insight into mechanisms of vaccine failure.