A central challenge for immune profiling, particularly for monitoring vaccine efficacy or disease progression, has been the identification of measurable parameters that can predict the outcome of an immune response. Given the heterogeneity of individual responses, we expect that the characteristic factors that define a healthy immune system, and its response to antigenic perturbations, are manifold rather than singular. We hypothesize that integrative analysis of data from individuals responding to vaccines or viral infections will allow for inference of causal chains of immunological processes associated with clinically defined outcomes (responsiveness, disease severity). Research Project 3 will focus on interrogating temporal behaviors of immune responses in diverse cohorts using novel single-cell techniques and mathematical methods. The application of time-series gene expression data to determine temporal gene expression patterns (SA1), single-cell analyses to determine intercellular influence networks(SA2),and multivariate statistical approaches (SA3) will generate models that describe the dynamic functional responses of the immune system, and identify sets of measurable predictors of clinical outcomes. This project involves a collaboration among four labs: The Xavier lab (MGH/Broad) and the Kleinstein lab (Yale) with expertise in bioinformatics and modeling approaches to innate and adaptive immunity, and the Love lab (MIT/Broad Institute) and the Lauffenburger lab (MIT/Broad) with expertise in microscale single-cell assays and mathematical algorithms to infer cellular networks. We will combine efforts 1) to integrate data on the diverse immune responses studied in this research program (vaccinations in healthy or aged persons, natural infections by viruses) and 2) to generate new network models based on comprehensive single-cell analyses. This project will leverage emerging approaches from engineering and computer science to improve detailed modeling of biological phenomena and provide feedback to refine experimental hypotheses in other areas of the program. The outcome of this research for health will be a set of comprehensive, integrated models for systemic immune responses that will inform vaccine design and improve the selection of biomarkers for clinical monitoring.
Many studies have measured independent parameters as related to disease or the efficacy of treatment. While these studies have advanced analysis of immune responses to infection and vaccination, we still lack a cohesive approach to describe the coordinated and integrated immune system. This project will develop new methods to define immunological signatures within large sets of clinical data and to translate those signatures back to simple, predictive sets of parameters for routine clinical monitoring of immune responses
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