Quantifying Biophysical Heterogeneity by Single Cell Microfluidic Impedance Cytometry

Heterogeneity in biophysical properties, which is inherent to the functional and structural organization of biosystems, presents challenges to cell biologists and clinicians seeking to associate biological function and disease with particular markers. Current methods to quantify heterogeneity focus on biochemical properties, as quantified by single-cell flow cytometry after fluorescent staining for their characteristic cell surface proteins or by label-free Raman spectral methods. Cellular biophysical metrics, on the other hand, have often been restricted to size-based differences that do not provide sufficient functional information on the biosystem. Frequency-resolved impedance cytometry in microfluidic systems is emerging as a tool for multiparametric and high-throughput biophysical stratification of phenotypes in a label-free manner. However, there is a need to standardize the metrics for enabling facile recognition and automated fitting to quantify subpopulations in heterogeneous biological samples. This will be explored for impedance cytometry in this dissertation based on red blood cells of modulated electrical physiology as standard particles (Chapter 1), and using machine learning methods to automate the recognition of drug-induced apoptotic subpopulations of pancreatic cancer (Chapter 2) and for classifying drug-induced transformations of multicellular pancreatic tumors (Chapter 3).