Abstract
The immune system plays a significant role in many immune and non-immune-mediated diseases' pathophysiology, it is imperative to holistically understand the many immune cell types, emphasizing their origin, function, and regulation. The origin of immune cells commences from the differentiation of the pluripotent hematopoietic stem cells (HSCs) via a process called hematopoiesis. The current literature describes hematopoiesis as a constantly occurring, step-wise lineage branching process, with each branching point signifying a commitment to a specific lineage. However, new data suggests that the classical HSC differentiation tree is more complex than known and suggests a paradigm shift depicting hematopoiesis. This thesis aims to address the unanswered questions about the immune cells and their progenitors. In the first part of this thesis, we dive deep into the ocean of immune cells and their progenitors – benchmarking their gene expression landscape. We then explore the relationship between the different cell types in the scope of their regulatory elements and further identify cell-specific genes and predict differentiation trajectories. In the second part of this thesis, we leverage the knowledge gained from the benchmarking and develop the requisite computational tools to identify immune cell types enriched in disease. We then apply our tool to several immune-mediated inflammatory diseases (IMIDs) and the highly infectious SARS-CoV-2 to shed light on COVID-19 disease in times of the growing pandemic.
Original language | English |
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Award date | 29 Jun 2022 |
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Print ISBNs | 978-94-6419-533-0 |
DOIs | |
Publication status | Published - 29 Jun 2022 |
Keywords
- Stem cells
- Immune cells
- network biology
- systems biology
- immune-mediated inflammatory disease
- machine learning
- computational biology
- SARS-CoV-2