Rapid Biophysical Characterization of Cancer Cells by Quantitative Deformability Cytometry
Cells are complex, viscoelastic materials that undergo changes in their mechanical phenotype, or ‘mechanotype’, during diverse physiological and disease processes, such as malignant transformation. As cancer cells exhibit altered cell mechanical properties compared to their benign counterparts, cell mechanotype is an emerging hallmark of cancer and demonstrates potential to enhance cancer detection and classification. However, widespread adoption of cell mechanotype as a clinical biomarker for cancer requires standardized metrics for high throughput mechanotyping measurements.
This dissertation presents a microfluidic platform, quantitative deformability cytometry (q-DC), for rapid, calibrated measurements of single-cell mechanical properties. Cells are driven to deform through micron- scale constrictions at 100 cells/second, while changes in cell strain are tracked by a high-speed camera. The applied mechanical stresses induced by the driving pressure are determined using gel calibration particles, which enables calibrated measurements of elastic modulus and fluidity from the single-cell stress-strain relationships. Additional physical properties, such as cell size, strain, transit time, and creep time, are also measured for individual cells by q-DC. This dissertation highlights a comprehensive methodology for designing, analyzing, and reducing variability in q-DC measurements; the calibration method for measuring the applied stress in the microfluidic channels; and the influence of stress and strain in q-DC mechanotyping.
This dissertation also demonstrates how multiple physical phenotypes from q-DC can be used to distinguish human cancer cell lines and predict the ability of cells to invade through a matrix. A physical phenotyping model of invasion is trained and validated using breast and ovarian human cancer cell lines with both genetic and pharmacologic perturbations, which correctly predicts the invasion of five cancer cell samples; whereas one context is identified where the model does not accurately predict invasion. Taken together, this work lays the groundwork for calibration in high throughput mechanotyping methods, demonstrates the predictive power of multiple physical phenotypes for cell invasion, and incites deeper investigation into additional predictive markers for cancer cell invasion.