Mass Cytometry Defines Virus-Specific CD4 T Cells in Influenza Vaccination.

TitleMass Cytometry Defines Virus-Specific CD4 T Cells in Influenza Vaccination.
Publication TypeJournal Article
Year of Publication2020
AuthorsSubrahmanyam PB, Holmes TH, Lin D, Su LF, Obermoser G, Banchereau J, Pascual V, García-Sastre A, Albrecht RA, Palucka K, Davis MM, Maecker HT
JournalImmunohorizons
Volume4
Issue12
Pagination774-788
Date Published2020 Dec 11
ISSN2573-7732
Abstract

The antiviral response to influenza virus is complex and multifaceted, involving many immune cell subsets. There is an urgent need to understand the role of CD4 T cells, which orchestrate an effective antiviral response, to improve vaccine design strategies. In this study, we analyzed PBMCs from human participants immunized with influenza vaccine, using high-dimensional single-cell proteomic immune profiling by mass cytometry. Data were analyzed using a novel clustering algorithm, denoised ragged pruning, to define possible influenza virus-specific clusters of CD4 T cells. Denoised ragged pruning identified six clusters of cells. Among these, one cluster (Cluster 3) was found to increase in abundance following stimulation with influenza virus peptide ex vivo. A separate cluster (Cluster 4) was found to expand in abundance between days 0 and 7 postvaccination, indicating that it is vaccine responsive. We examined the expression profiles of all six clusters to characterize their lineage, functionality, and possible role in the response to influenza vaccine. Clusters 3 and 4 consisted of effector memory cells, with high CD154 expression. Cluster 3 expressed cytokines like IL-2, IFN-γ, and TNF-α, whereas Cluster 4 expressed IL-17. Interestingly, some participants had low abundance of Clusters 3 and 4, whereas others had higher abundance of one of these clusters compared with the other. Taken together, we present an approach for identifying novel influenza virus-reactive CD4 T cell subsets, a method that could help advance understanding of the immune response to influenza, predict responsiveness to vaccines, and aid in better vaccine design.

DOI10.4049/immunohorizons.1900097
Custom 1

https://www.ncbi.nlm.nih.gov/pubmed/33310880?dopt=Abstract

Alternate JournalImmunohorizons
PubMed ID33310880
PubMed Central IDPMC7891553
Grant ListHHSN272201400008C / AI / NIAID NIH HHS / United States
U19 AI057229 / AI / NIAID NIH HHS / United States
S10 RR027582 / RR / NCRR NIH HHS / United States
U19 AI090019 / AI / NIAID NIH HHS / United States
P01 AI097092 / AI / NIAID NIH HHS / United States

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