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, multiple panels of 15-color 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. Our framework takes advantage of such natural variations to systematically infer correlates, build predictive models of response quality after immune perturbation, and infer novel functional connections among various components in the human immune system. We have applied this framework to the influenza vaccination study utilizing antibody titer response as an exemplar endpoint. We confirmed previously known post-vaccination correlates based on gene expression and plasmablast frequency from day 7 samples. More importantly, using an approach that compensates for the influence of pre-existing serology, age and gender, we derived accurate predictive models of antibody responses using pre-vaccination data alone. This finding has obvious implications for the design of future vaccine trials and for developing a deeper understanding of the molecular and cellular parameters that contribute to robust vaccine responses. The robustness and translational potential of these findings is further emphasized by our discovery that the parameters playing the greatest role in correct response prediction are those with the most stable baseline values across individuals. This raises the prospect of monitoring immune health and predicting the quality of responses in the clinic via the evaluation of these baseline blood biomarkers. 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.

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Matsuyama, Masashi; Martins, Andrew J; Shallom, Shamira et al. (2018) Transcriptional Response of Respiratory Epithelium to Nontuberculous Mycobacteria. Am J Respir Cell Mol Biol 58:241-252
Candia, Julián; Cheung, Foo; Kotliarov, Yuri et al. (2017) Assessment of Variability in the SOMAscan Assay. Sci Rep 7:14248
Vodovotz, Yoram; Xia, Ashley; Read, Elizabeth L et al. (2017) Solving Immunology? Trends Immunol 38:116-127
Lau, William W; Tsang, John S (2016) Humoral Fingerprinting of Immune Responses: 'Super-Resolution', High-Dimensional Serology. Trends Immunol 37:167-169
Lau, William W; Sparks, Rachel; OMiCC Jamboree Working Group et al. (2016) Meta-analysis of crowdsourced data compendia suggests pan-disease transcriptional signatures of autoimmunity. F1000Res 5:2884
Sparks, Rachel; Lau, William W; Tsang, John S (2016) Expanding the Immunology Toolbox: Embracing Public-Data Reuse and Crowdsourcing. Immunity 45:1191-1204
Lu, Yong; Biancotto, Angelique; Cheung, Foo et al. (2016) Systematic Analysis of Cell-to-Cell Expression Variation of T Lymphocytes in a Human Cohort Identifies Aging and Genetic Associations. Immunity 45:1162-1175
Olnes, Matthew J; Kotliarov, Yuri; Biancotto, Angélique et al. (2016) Effects of Systemically Administered Hydrocortisone on the Human Immunome. Sci Rep 6:23002
Crompton, Joseph G; Narayanan, Manikandan; Cuddapah, Suresh et al. (2016) Lineage relationship of CD8(+) T cell subsets is revealed by progressive changes in the epigenetic landscape. Cell Mol Immunol 13:502-13
Shah, Naisha; Guo, Yongjian; Wendelsdorf, Katherine V et al. (2016) A crowdsourcing approach for reusing and meta-analyzing gene expression data. Nat Biotechnol 34:803-6

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