Brain development at single cell resolution in health and disease

Using RNA sequencing, we can examine distinctions between different cell types and capture a moment in time of the dynamic activities taking place inside a cell. Researchers in fields like developmental biology have embraced this technology quickly as it has improved over the past few years, and there are now many single-cell RNA sequencing datasets accessible. A surge in the development of computational analysis techniques has occurred along with the invention of technologies for generating single-cell RNA sequencing data. In my thesis, I examine computational methods and tools for single-cell RNA sequencing data analysis in 3 distinct projects. In the fetal brain project, I tried to decipher the complexity of the human brain and its development, and the link between development and neuropsychiatric diseases at early fetal brain development. I provide a unique resource of fetal brain development across a number of functionally distinct brain regions in a brain region-specific manner at single nuclei resolution. In total, I retrieved 50,937 single nuclei from four individual time points (Early; gestational weeks 18 and 19, and late; gestational weeks 23 and 24) and four distinct brain regions (cortical plate, hippocampus, thalamus, and striatum). In my dissertation, I also tried to investigate the underlying mechanisms of Parkinsons disease (PD), the second-most prevalent neurodegenerative disorder, characterized by the loss of dopaminergic neurons (mDA) in the midbrain. I examined the disease process using single cells of mDA neurons developed from human induced pluripotent stem cells (hiPSCs) expressing the ILE368ASN mutation in the PINK1 gene, at four different maturation time points. Differential expression analysis resulted in a potential core network of PD development which linked known genetic risk factors of PD to mitochondrial and ubiquitination processes. In the final part of my thesis, I perform an analysis of a dataset from brain biopsies from patients with Intracerebral hemorrhage (ICH) stroke. In this project, I tried to investigate the dynamic spectrum of polarization of the immune cells to pro/anti-inflammatory states. I also tried to identify markers that potentially can be used to predict the outcome of the ICH patients. Overall, my thesis discusses a wide range of single-cell RNA sequencing tools and methods, as well as how to make sense of real datasets using already-developed tools. These discoveries may eventually lead to a more thorough understanding of Parkinson’s disease, ICH stroke but also psychiatric diseases and may facilitate the creation of novel treatments.