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This thesis leverages the power of single-cell RNA and ATAC sequencing to enhance our understanding of the innate and adaptive immune systems in higher mammals. The primary focus is on the transcriptional networks that guide the activation and differentiation of human primary CD4+ T cells into Th1, Th2, Th17, and iTreg subsets, using a GMP-based protocol and ex vivo/in vitro approaches. Additionally, computational models for gene regulatory network (GRN) inference and analysis were employed to elucidate gene regulation using a data-driven, multi-omics approach. This research also encompasses viral response-related studies to provide a comprehensive view of the immune response, specifically targeting the central nervous system (CNS) upon TBEV infection and lung tissues during SARS-CoV-2 infection.

In Paper 1, a multi-omics linear and non-linear approach is developed to predict gene popularity using a large number of high-throughput sequencing datasets. We show that additional omics layers are beneficial to construct GRNs capable of accurately predicting gene popularity. In Paper 2, a comprehensive atlas of human primary CD4+ T cell activation and differentiation is created using in vitro cell differentiation and single-cell RNA and ATAC sequencing. Novel gene regulatory dynamics of JUNB are identified, and a new probabilistic approach based on Markov chains for GRN analysis and interpretation is introduced. In Paper 3, the connection between type I interferon response in the mouse brain and TBEV infection is explored using single nuclei RNA sequencing. In Paper 4, the role of intrinsic resistance factors in human COVID-19 susceptibility is investigated using both single-cell and bulk RNA sequencing, and identifies SERPINS as critical regulators of the process.

The findings of this thesis contribute significantly to the understanding of transcriptional networks governing human CD4+ T cell differentiation and activation. This work aims to improve therapy and demonstrate the efficacy of NGS and computational tools in deciphering the transcriptional networks involved in various viral infections.