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We have been generating and analyzing multi-modal data to assess the immune phenotypes of healthy individuals at baseline and after perturbations, particularly with influenza vaccination. For each individual we generate multiple types of measurements from blood, including gene expression, flow cytometry for assessing single-cell phenotypes and cell population frequencies (and relative expression of protein markers), proteomic assays (Somalogic and Luminex) for measuring serum protein levels, genome-wide genotyping, and serological information such as virus-specific antibody titers. We have been conducting integrative modeling analyses using both in-house and public data sets to draw novel insights into human immunobiology. We have also initiated collaborative projects with both extramural and intramural colleagues by applying our human systems immunology approach, including in the context of vaccination, single-cell analysis, early immune development, maternal and infant immunity. Recent highlights of our efforts include: 1. By utilizing vaccination and associated adjuvants as a perturbation, we have developed a conceptual and methodological framework to quantify baseline and response variations at the level of genes, pathways, and cell populations in a cohort of individuals. The conceptual and computational analysis framework we have developed have now been applied to systems and population level exploration in a number of contexts, including the study of vaccination/adjuvants, predicting flares in autoimmunity, and maternal and early life immunity. 2. Together with colleagues at the CHI, we have analyzed the multi-modal data obtained from the H5N1 adjuvanted vaccine systems biology study. The data were obtained at baseline and from multiple time-points post vaccination. The vaccine together with the adjuvant were administered in one of the arms of the study, while subjects in the second arm only had the vaccine without the adjuvant. One of the goals is to evaluate the effect of the adjuvant. We have developed a novel analysis framework to extract, in an unsupervised manner, information about the response dynamics as possible. We will correlate the distinct patterns of dynamical responses to biological variables, including the adjuvant status and antibody responses. 3. Together with NIH clinical colleagues studying immune-mediated monogenic diseases, we have collected samples from different patient groups and have phenotyed them using modern, multiplexed approaches such as blood and cell subset profiling, immune cell phenotyping, assessing circulating serum cytokines, and epigenetic evaluation. One of the goals is to obtain an integrative understanding of similarities and differences across diseases and individuals, to assess whether data from such a collection can help dissect genetically more complex diseases, and to utilize the natural monogenic lesions as perturbations to study the wiring of the immune system. 4. Since we routinely use and analyze publicly available data to argument data generated in-house, we have developed a web-based framework (including both user interfaces and database components) to facilitate search, retrieval, annotating, meta-analysis, and gene-expression signature generation. At the core of our tool are interfaces for creating, annotating, and sharing (among the user community) of which groups of samples can be compared to form classical gene-expression signatures or expression difference profiles the latter can be used to integrate across data sets and studies to generate virtual perturbation profiles across all genes. Since annotating such comparison groups is often one of the most time-consuming steps of reusing existing data, our framework provides functions for users to share their own annotations and search for others annotations. Our tool was designed for experimental biologist to take full advantage of reusing and sharing large-scale data for obtaining biological insights. (See Shah, Guo and Wendelsdorf et al. 2016). We have recently added major new features, including the incorporation of RNAseq data (recount 2 resource), adding mixed-effect meta-analysis models, allowing sharing of entire compendia, and better user interfaces for forming comparison groups. 5. Together with Arnaud Marchant, Marcela Pasetti, Margaret Ackerman, Anne Hoen, and Galit Alter, we have initiated a consortium program focusing maternal-infant immunity. My labs focus is on using systems immunology to develop a better, more predictive understanding of maternal and infant immunology and vaccination responses. 6. We have started to use influenza infection and vaccination as models to study the multi-tissue/organ dynamics of immune responses. A major goal is also to integrate such tissue level data with data from blood in humans to build more quantitative models of immune responses in humans. 7. Together with Eva Harris (UC Berkeley), we have been mapping the immune system status from 6 months of age to post puberty through longitudinal analysis within the same individual at different age periods and integration of the individual profiles to tease apart individuality (characteristic of the person) vs. age-dependent effects.