(560b) Single-Cell Immune Landscape of Influenza Infection in the Mouse Lung: Implications of Aging and Genetic Background

Influenza virus infection poses a significant global disease burden, causing an estimated three to five million cases of severe illness and 290,000 – 650,000 deaths each year worldwide. While vaccination has been successful in generating long-lasting immunity to many other viral infections, the current influenza vaccine effectiveness in the U.S. fluctuates from 60% to as low as 10% for a given year, even during flu seasons when the vaccine strain matches the circulating virus. The reason for the underwhelming protection has been partially attributed to problems with vaccine immunogenicity and host-specific factors such as aging, genetic background, and pre-existing immunity. However, due to the complexity of the immune landscape, it has been difficult to comprehensively characterize how these factors affect the immune response. In this work, we demonstrate the utility of mass cytometry (CyTOF), a technology that allows the simultaneous detection of 40 unique biomarkers on single cells, to comprehensively profile the immune response to influenza infection in mice of different ages and genetic background. To support the rapid analysis of such a large high dimensional dataset and reduce human bias, we developed a customizable and automated gating approach based on a probabilistic support vector machine (SVM) classifier to enable efficient, automated classification of over 30 million cells into 24 defined cellular subsets with high accuracy (97%). Analysis of the resulting data revealed significant discrepancies in immune cell infiltration, cellular activation, and viral infectivity due to both aging and genetics. Taken together, we show that such a high-resolution approach, combined with high-dimensional data analysis methods, permits a systems-level exploration of the immune landscape, providing new cellular and molecular insights into the role of host specific-factors during influenza infection.