(233f) Characterization of the Nano-Scale Surface Roughness of Living Cancer Cells Using a Non-Invasive Interference-Based Approach

Probing the surface roughness of a living cell’s membrane at the nanometer scale can offer valuable insights into its physiological condition, including any diseases present and the cell’s response to treatments like anti-cancer drugs. While few methods can provide such information on live cells, atomic force microscopy (AFM) is commonly used. However, roughness characterization of cells using AFM comes with drawbacks such as limited temporal resolution and physical contact with the cell surface, which can deform delicate structures like microvilli and reveal structural aspects of the cytoskeleton rather than the membrane itself. In contrast, interference-based techniques, like reflection interference contrast microscopy (RICM), are known for their simplicity and high resolution on the nanometer/microsecond scales. RICM offers a unique non-invasive “view-from-below” perspective providing accurate information about the topography of microscopic objects near a flat and transparent surface. Here we demonstrate how RICM images of live cells can be analyzed to extract information about their nanoscale surface roughness, even when interference fringes aren't visible and fringe visibility analysis is not possible. This enables rapid and non-invasive quantification of membrane roughness at the single-cell level. Our approach relies on how a cell’s rough surface scatters reflected light, as measured in an experimental RICM interferogram, relative to the scattering pattern from the corresponding smooth object, as measured from calculated intensities using appropriate cell and RICM models. We illustrate the applicability of our approach by quantifying the surface roughness of prostate cancer cells with high (PC3) and low (LNCaP) metastatic potential. Our measurements of surface roughness of living cells are consistent with current literature. Further research using blood cells and other types of cancer cells will enable the development of a liquid biopsy analysis platform, with several label-free biomarkers retrieved from RICM images, including cell roughness, employed for high-throughput non-invasive identification/classification of different cells.