UC Davis

Understanding brain development is crucial for advancing knowledge of neurological diseases and improving intervention methods. To understand such processes under normal and perturbed conditions, numerous techniques have been developed, and this pool of possible techniques to choose from is still growing. With the help of these technologies, labs have generated massive datasets that require careful analysis to extract insights that closely reflect actual biological processes. Accurate interpretation of these data is essential for enhancing our knowledge and developing neurological disease treatments effectively. In this dissertation, I employed various computational approaches to analyze data generated to investigate brain development under normal and disturbed conditions, with a focus on datasets related to gene expression and epigenomic changes.

The first project focused on understanding the role of Dlx1 and Dlx2 transcription factors in neurodevelopment post-proliferation. By utilizing single-cell resolution methods, gene expression differences in conditional Dlx1/2 knockouts had been examined, and I computationally identified downstream genes of Dlx1/2 across various subregions of the ganglionic eminence and olfactory bulb. Additionally, analysis of Dlx2 binding regions across different developmental stages and regions revealed significant binding patterns, with enriched GO terms highlighting Dlx2’s role on critical processes within GE subregions.

In the second project, we focused on the development of medial ganglionic eminence (MGE)-derived interneurons. We explored chromatin dynamics and gene regulation during the differentiation of embryonic stem cells (ESCs) into MGE-derived GABAergic cortical interneurons (CINs). I computationally integrated open chromatin signals and histone marks across six developmental time points, identifying putative enhancers and linking them to gene expression levels.

The third chapter employed the method similar to the first project to investigate the immediate effects of maternal immune activation (MIA) on brain development in mouse embryos at 12.5 days in utero using a single-cell resolution gene expression profiling approach. I analyzed both neuronal and non-neuronal cell types in this dataset, examining differential gene expression and changes in cell proportions in response to MIA.

In summary, this dissertation further advances our understanding of brain development under both normal and disturbed conditions by integrating computational methods with cutting-edge techniques.