Date of Award:
Doctor of Philosophy (PhD)
With their self-renewal and cellular differentiation capacities, stem cells could be the ideal model for exploring the mechanisms that regulate cell differentiation, organ maintenance, novel cellular therapies, etc. This research focuses on the exploration of better engineering methods to replace the invasive and inconvenient traditional methods to characterize the stem cell differentiation process and heterogeneity of cancer stem cells.
First, label-free Raman spectroscopy was applied to characterize the gradual change during the differentiation process of live human neural stem cells in in vitro cultures. Eight key spectral biomarkers were selected and based on those biomarkers, three machine learning-based analysis methods were used to predict cell types. Among those methods, the artificial neural network had the highest predictive accuracy compared to the logistic regression model and linear discriminant analysis. In addition, our mathematical model could also classify cell types at the single-cell level. Next, the brain cancer stem cells were studied with microfluidic devices to identify their heterogeneity and migration behavior. The microfluidic devices were fabricated to recapitulate the 3D-confined interstitial and chemotactic environment. The invasion speed of brain cancer stem cells of glioblastoma multiforme (GBM) was accelerated with the increase of growth factors gradient (EGF and basic-FGF). Furthermore, this platform could also differentiate different cell subtypes of GBM based on their chemotactic invasion and be used for drug screening (e.g. resveratrol). Third, using our microfluidic device, one type of cell migration modes—blebbing migration, was specifically explored. GBM cells were cultured under the chemotactic environment to study the relationship between ion channels (NHE-1 and NKCC-1) and blebbing dynamics. The blebbing dynamics were significantly influenced by inhibitors (NKCC1, NHE1, and Myosin). The ion channel regulations (i.e., polarity and direction) were found to relate to blebbing dynamics, which in turn were affected by gradient conditions. Lastly, the two shapes of sub-micron particles of ZnO were fabricated and their bio-imaging and potential anti-cancer properties were shown.
Geng, Junnan, "Characterizing the Variability and Heterogeneity of Stem Cells by Engineering Methods" (2021). All Graduate Theses and Dissertations. 8335.
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