Multi-scale analysis of single cell sequencing data to dissect the complexity of influenza infections

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Project Summary Influenza virus infection is a recurrent health and economic burden. It cycles between the human population and the animal reservoir, causing millions of hospitalizations and thousands of deaths each year, especially in high-risk groups, such as young children, pregnant women, obese, individuals with compromised immune system and indigenous populations. Disease morbidity and mortality increase when a new influenza strain reasserts or jumps the host, and becomes capable of infecting humans. In this case, there is no (or minimal) pre-existing antibody-mediated immunity to the new viral strain at the population level, leading to millions of infections and a rapid global spread of the virus. In the absence of antibodies, the severity of the disease can be ameliorated by broadly cross-reactive cellular immunity. But, the precise mechanism of how immune cells mediate recovery in some individuals, but not others, is far from clear. However, a diverse and rich collection of datasets are available in the public domain that have already addressed specific aspects of these concerns. Expression profiles from human cohorts and animal studies in GEO/SRA, immunological profiles in ImmPort or influenza strain data and interaction with immune epitopes in the Influenza Research Database (IRD), a Bioinformatics Resource Center (BRC) of NIAID, are examples of such resources. In particular, high-resolution single-cell RNA-seq data enables us to study relevant processes during influenza infection in great detail. The combination of multiple previously collected datasets, in particular across biological scales, single cell and bulk data, is a central goal in this research. The overarching hypothesis that guides our proposed work is that diversity in influenza virus strains, genetic immune epitopes and in the responding immune cell population contributes to the diverse outcome after influenza infection. In detail we will address the questions about determinants of influenza infections, and key processes that impede any replication, on the one hand, or contribute to a weak immune response, on the other hand. Sex as biological factor will be addressed whenever appropriate data with sufficient sample-size is available. We will further develop an approach to increase the resolution of bulk data by guidance of single cell data. For this purpose, we will not only develop multi-scale models of high resolution and detail but also develop the appropriate tools to facilitate and enable such precision modeling.