Abstract:
Regenerative medicine relies on the precise control of stem cell differentiation. While mesenchymal stem cells (MSCs) and human embryonic stem cells (hESCs) hold great promise, current differentiation methods struggle with efficiency, reproducibility, and a limited understanding of complex regulatory networks. Traditional genetic modification often yields unpredictable outcomes, and wet-lab methods are time and resource-intensive. This thesis presents a novel computational framework that systematically guides stem cell differentiation towards specific lineages without genetic modification. By integrating single-cell RNA sequencing (scRNA-seq) and RNA velocity, the framework estimates the "poising levels" of MSCs and hESCs by capturing gene expression dynamics. Pathway enrichment scores from UniPath (a normalization-free gene-set enrichment tool) are combined with probabilistic graphical models to identify key signaling pathways influencing lineage decisions. A unique feature includes modeling bifurcations using relative RNA velocities of marker genes, enabling a pathway-centric view that accounts for cell variability. We applied this framework to analyze human gastrulation using public scRNA-seq datasets, mapping developmental trajectories and identifying critical pathways (e.g., Wnt, BMP, TGFβ, FGF, Retinoic Acid) and transcription factors (e.g., ZSCAN10, STAT3, OTX2, SOX5, RUNX2) involved in ectoderm, mesoderm, and endoderm differentiation. The framework also revealed regulatory networks in endoderm-derived liver/pancreas and MSC-derived adipocyte, cartilage, and osteocyte differentiation. Bayesian Network inference and Random Forest analysis uncovered causal links between pathway activities and cell fates. Consistency with established developmental biology supports the validity of our computational predictions. This work offers a scalable and reproducible approach for stem cell engineering, advancing regenerative medicine.
