Systematic analysis of cellular and immune system responses for therapy development

Abstract

The human body is a complex program where many important regulatory molecules are reused throughout the body, but their functions are specific to particular locations and biological contexts. This notion of bio-specificity is especially valuable in the context of immunology, which can be harnessed to develop effective diagnostics and therapeutics for human diseases. However, given the extensive number of potential candidate molecules, experimental analysis of the functions of all target genes is not feasible. Moreover, integrative interpretation of public omics datasets remains challenging due to substantial noise and heterogeneity present in the data. Systematically harmonizing and mapping transcriptional regulation and gene expression is thus of critical importance. In this thesis we developed and applied two computational frameworks, SPEEDI and TissueGPS, which harness large-scale heterogeneous omics datasets that systematically integrate and analyze epigenetic and transcriptional omics data to understand immune system dynamics. We applied these frameworks in the context of cellular and systemic immunity. First, we systematically study the systemic immune transcriptional responses of vaccinated subjects to SARS-CoV-2 infections. Second, by investigating the immune landscape of infected individuals, we predict epigenetic-informed gene signatures to assist rapid diagnosis for infectious disease assessments. Third, we designed cell-based therapeutics to arm the immune system with customized sensing circuits to detect and treat central nervous system disorders, such as malignant tumors, with reduced systemic off-target toxicity. Collectively, our methods offer novel frameworks for interrogating human diseases with precision and reproducibility across immune contexts.