Inference of Gene Regulatory Networks from single-cell RNA-seq and ATAC-seq data

Gene Regulatory Networks (GRNs) provide a conceptual framework to describe the regulatory interactions that control gene expression and cellular identity. The advent of single-cell RNA sequencing (scRNA-seq) has enabled the investigation of transcriptional regulation at unprecedented resolution, revealing extensive cellular heterogeneity. However, gene expression data alone offer limited insight into upstream regulatory mechanisms such as chromatin accessibility and transcription factor binding. Integrating scRNA-seq with single-cell ATAC sequencing (scATAC-seq) allows a more comprehensive reconstruction of gene regulatory programs. In this thesis, single-cell multi-omics data from mouse hippocampal nuclei are analyzed to investigate transcriptional regulation under normal sleep and sleep deprivation conditions. By integrating transcriptional and chromatin accessibility profiles, this work aims to characterize regulatory differences associated with sleep deprivation in wild-type mice and to define a reference gene regulatory landscape. This analysis provides a foundation for future comparative studies aimed at elucidating regulatory alterations associated with sleep disturbances in autism-related genetic models.