ABSTRACT – IMMUNE DATA SCIENCE (IDS) CORE The Immune Data Science (IDS) Core will be responsible for analysis of high-dimensional immune data sets generated by Projects 1-3 and the Model Systems (MODS) Core. The objectives of the analysis are to address 1) viral factors contributing to transmission, 2) immune factors contributing to blocking transmission, 3) efficacy of passive immunization on blocking transmission, 4) efficacy of specific vaccines on blocking transmission, 5) immunogenicity of specific vaccines, and 6) biomarkers associated with outcomes. To address these objectives, we need to consider the data, outcomes, and experimental context. Data comes in several categories that require different processing and analytic approaches: bulk (multiplexed cytokines, antibody concentration, antibody function), single cell (flow cytometry, scRNA-seq), antigen receptor repertoire (scTCR-seq), and spatial (spatial proteomics, spatial transcriptomics). The primary outcome is viral transmission, defined as detectable CMV by qPCR on 2 technical replicates, but there are several other outcomes of interest: maternal plasma viral load over time, shedding in urine and saliva, and dissemination across multiple tissues and organ systems. Each set of data and outcomes comes with a specific context: viral strain, immunocompetent or CD4 T cell-depleted Rhesus macaques (RM), and treatment (none, specific vaccine, or HIG) administered. The first aim (SA1) will develop computational biology pipelines for the processing, quality control, and exploratory analysis of immune data sets only. SA1 will focus on 1) single cell data science applied to flow cytometry and scRNA-seq data sets to identify immune cell types and their gene expression profiles, 2) antigen receptor diversity analysis to identify abundant clonotypes related to their gene expression profiles and immunodominant CMV epitopes, and 3) spatial biology, especially co-localization of virus and immune cells. The computational biology pipelines will also generate informative features from these immune assays to be used for downstream analysis. The second aim (SA2) will apply or develop statistical models to address the six analytic objectives, which require linking data, context, and outcomes. In addition to standard statistical analysis described in Projects 1-3, but centrally executed by the IDS Core for consistency, SA2 will focus on 1) developing a hierarchical Bayesian model to model the complex conditional dependency structure of viral dissemination experiments, 2) extension of our powerful COMPASS framework for identification of cellular immune correlates to include bulk data, and 3) hypothesis testing to rigorously evaluate spatial patterns of variation and co-localization that inform us about viral transmission and vaccine mechanisms. Overall, the IDS Core will serve as the quantitative hub of the overall Program, providing the quantitative expertise and resources to analyze and interpret the com...