(190ac) Label-Free Interference-Based Single-Cell Phenotyping of Highly Metastatic Cancer Cells in Liquid Biopsy Applications | AIChE

(190ac) Label-Free Interference-Based Single-Cell Phenotyping of Highly Metastatic Cancer Cells in Liquid Biopsy Applications

Authors 

Contreras-Naranjo, J. C. - Presenter, Texas A&M University
Jayaraman, A., Texas A&M University
Ugaz, V., Texas A&M University
Personalized medicine enabled by liquid biopsy of bodily fluids for early disease diagnosis, treatment monitoring, and patient prognosis, offers great potential but faces important technological challenges depending on the targeted biomarker (e.g., cells, exosomes, and cell-free nucleic acids). Identification of circulating tumor cells (CTCs) isolated from blood in a liquid biopsy can be time-consuming and costly because it typically involves complex workflows with the use of specific antibodies to distinguish them from a background of blood cells. Here we describe an alternative single-cell analysis approach using a label-free interference-based technique, reflection interference contrast microscopy (RICM). RICM’s unique non-invasive “view from below” perspective generates interferograms that embed detailed topographical information, down to the nanometer-scale, of single cells as they settle and interact with a glass surface. This label-free phenotyping approach was implemented in populations of white blood cells and cancer cells of low (LNCaP) and high (PC3) metastatic potential to find characteristic dynamic behaviors that can be used for identification of highly metastatic cancer cells. The computational analysis of RICM interferograms, using fast custom-developed algorithms, revealed the dynamics of filopodia, thin ~200-400 nm “finger-like” plasma membrane protrusions used by cells to probe their environment, as an important label-free biomarker for identification of the highly metastatic PC3 cells. This finding is in agreement with PC3 cells overexpressing filopodia-associated proteins, and the fact that filopodia have emerged as important contributors to cancer metastasis. Thus, RICM offers an attractive label-free alternative to conventional affinity-based approaches for cancer cell phenotyping at the single-cell level. Our results illustrate RICM’s potential for quantitative analysis of cell-substrate interactions, including filopodia dynamics as a label-free biomarker, to enable label-free identification of CTCs in liquid biopsy applications.