The conversion of epithelial cells to mesenchymal cells, or the epithelial-mesenchymal transition (EMT), is a phenotypic program critical to embryonic development and wound healing, however, this process can be aberrantly hijacked in pathologies such as fibrosis and cancer. During EMT, epithelial cells become less adherent and differentiated, gaining a motile phenotype and elongated morphology. In the context of cancer, EMT may contribute to malignant progression by imparting cells with an invasive capacity and enhanced drug resistance. Historically, EMT and cancer cell invasion have been investigated through the use of 2D monolayer culture, animal models, and patient histology. Nevertheless, these classical assays are often inadequate to resolve single cell heterogeneity and plasticity. Thus, there is a critical need for precision measurements and biomimetic microenvironments with which to elucidate cell migration and EMT. In this thesis, I first review the current state of multicellular invasion and plasticity in the context of biomaterials. Second, I apply single cell analyses to visualize cell-material interactions using a textured carbon nanomaterial (graphene oxide). Third, I utilize machine learning to classify epithelial and mesenchymal morphological phenotypes with single cell resolution in response to various biochemical stimuli. Fourth, I investigate the migration behaviors of cells undergoing EMT induction in reduced growth factor conditions, which result in the formation of collectively migrating multicellular clusters with branch-like morphologies. Finally, I show preliminary results on EMT in 3D silk-collagen hydrogels with tunable mechanical and biochemical properties. Overall, these emerging technologies permit new insights into the reciprocity between tumor progression, EMT, and the aberrant microenvironment, which could be applied for patient-specific biomarker discovery and drug testing for precision medicine.
Leggett, Susan Ellen,
"The Epithelial-Mesenchymal Transition in Engineered Microenvironments"
(2018).
Pathobiology Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://doi.org/10.26300/gef2-7y54
