Electrophysiology-based Microfluidic Assessment of Cell Phenotypes to Predict and Treat Disease

Cellular systems often exhibit a degree of heterogeneity, which can have important consequences on biological function and disease. Typically, this heterogeneity is assessed by labeling the expressed surface proteins on cells by antibodies and then using flow cytometry to identify single-cells based on the fluorescence and light scattering signals. However, some bacteria, tumor, and stem cells often lack surface proteins that can be correlated to a specific phenotype. There is an emerging need for quantification based on label-free biophysical characteristics that can serve as the phenotype for correlation to biological function, disease progression and the efficacy of therapies. For instance, in the case of transplant therapies based on stem cells, the attachment of antibodies to identify them can affect their differentiation and transplant potential. Similarly, there is a need for label-free methods to predict susceptibility of microbiota to colonization by pathogenic bacteria, such as Clostridium difficile, which is the primary cause of antibiotic associated diarrhea. In this dissertation, we focus on label-free methods to analyze heterogeneous biological samples to elucidate biological function, such as the susceptibility of host microbiota to Clostridium difficile infection and the differentiation lineage of stem cells. We envision application of these label-free analytical and separation cues for controlling cellular compositions to aid precision medicine approaches.