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 immobiology. We have also initiated collaborative projects with both extramural and intramural colleagues by applying our human systems immunology approach to meta-analyze data from multiple cohorts, and to generate and integrate new data from both healthy and disease subjects. Recent highlights of our efforts include: 1. By utilizing vaccination 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 can also be applied to systems and population level exploration in a number of medically relevant circumstances, including but not limited to the effects of drug intervention or natural disease history studies in humans. See Tsang JS. 2015 for details. 2. Cell-to-cell expression variation (CEV) is a prevalent feature of cell populations, but the functional relevance of this variation is not well understood. We have developed an analysis framework for quantifying CEV in human immune cell populations. We show that CEV can exhibit substantial subject-to-subject differences but is largely stable within individuals, and we identify CEV correlates of aging and disease-associated genetic polymorphisms. Our findings indicate that CEV can be a temporally stable phenotype reflective of personal immune states. (See Lu Y et al. 2016) 3. Together with colleagues at the Human Immunology Project Consortium (HIPC), we have been performing meta-analysis of multiple human influenza vaccination data sets to derive common predictive signatures of vaccine responses using pre-vaccination gene-expression data. We have successfully uncovered and validated using independent cohorts both gene- and gene module-based predictive signatures for younger subjects using data from several study cohorts spanning multiple vaccination seasons and geographic locations. 4. Together with colleagues at the CHI, we have begun analyzing 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 are developing 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. 5. Together with NIH clinical colleagues studying immune-mediated monogenic diseases, we have begun to collect baseline samples from different patient groups and are in the process of phenotyping them using modern, multiplexed approaches such as blood and cell subset profiling, immune cell phenotyping, assessing circulating serum cytokines. One of the key goals is to obtain an integrative understanding of similarities and differences across disease groups and to further assess whether data from such a collection can help dissect genetically more complex diseases. 6. 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) 7. Using our OMiCC platform, we tested the idea of using crowdsourcing for utilizing public data sets for exploratory data analysis. Advertised to the NIH immunology community, we organized a jamboree attracting volunteer participants, consisting of faculty, fellows, and student participants. The group focused on using OMiCC to search for and annotate data from five autoimmune diseases and the corresponding mouse models. We used meta-analysis within OMiCC to derive (1) robust gene expression signatures for each disease (disease versus healthy control comparisons) and (2) conserved gene expression signatures across all diseases within each species (pan-disease signatures); we also examined signature conservation between human and mouse. Our jamboree experiment suggests that there are potentially interesting and robust signals to be mined; given a programming-free, didactic, community-based platform such as OMiCC, together with some upfront training on the platform, biologists with any level of bio- informatics experience can benefit from and contribute to utilizing public data for hypothesis exploration. See (Sparks et al., 2016) and (Lau et al., 2016) for further details.
| 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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