Advances in sequencing technologies have sparked the discovery of new genetic etiologies for neurological and neurodevelopmental disorders. As new disease-causing mutations are unveiled, questions into the specific mechanisms of pathogenicity and potential therapeutic approaches arise. To address these questions, in vivo and in vitro models have been generated and analyzed; but how best to utilize these models, and how well they recapitulate the human brain, is still not fully understood. Within the work discussed in this thesis, we address this problem through the transcriptomic and functional interrogation of these models in the context of neurodevelopment and disease. In Chapter 2 of this thesis, we describe the use of single-cell RNA-sequencing to examine the longitudinal transcriptomic profiles of neuronal network establishment and maturation in ex vivo mouse cortex- and hippocampus-derived cultures. Our data highlights unique developmental transcriptomic profiles for individual genes, disease gene subclasses, and biological processes, and discusses cell population-specific divergent transcriptomic profiles between genes associated with neurological diseases, focusing on epilepsy and autism spectrum disorder.
We also compared the data from our ex vivo system to transcriptomic data collected from in vivo neonatal and adult mouse brains and human cortical organoids, highlighting the importance of the generation and consideration of system-specific transcriptomic datasets when looking into a gene, disease, or biological process of interest, and serves as a vital resource for researchers. In Chapter 3, we propose a high-throughput drug discovery paradigm utilizing the application of transcriptome reversal for neurodevelopmental disorder-associated genes that affect the transcriptome. This approach describes the idea that if gene dysregulation is causal for the pathogenicity of a disease, then correcting the transcriptional signature should have a therapeutic effect. We demonstrated that small-molecule induced gene expression changes vary between both cell lines and neural cell populations, and highlight both the importance of selecting the appropriate model of disease and creating cell population-specific signatures for compounds and disease.
In Chapter 4, we focus on the utilization of multi-electrode arrays for the electrophysiological characterization of primary cortical networks derived from mouse models of epileptic encephalopathy. This technique allows for the analysis of numerous neuronal and network synchronization metrics for spontaneous longitudinal activity and responses to external stimuli in the form of electrical stimulation and compound addition. In particular, mouse models with mutations in the genes Grin2a, Gnb1, and Scn1a were analyzed. We discovered significant hyperexcitability, bursting, and synchrony phenotypes, and discuss how acute and chronic compound addition can be used to interrogate biological pathways and reverse disease activity signatures.
