Quantifying cancer cell motility in an in vitro system

Abstract

Cell motility plays an important role in development, wound healing and cancer progression. A fundamental unresolved challenge in the field is to obtain reliable measures of motility metrics from single cells and then derive statistically meaningful data on cell population level motility behavior. Currently available tools are limited, for instance, they track cells as unrelated objects (i.e., do not consider cell division), lack ability for high-throughput dynamic parameter extraction, or employ inaccurate tracking algorithms. To extract dynamic morphology and motility parameters at the single cell level we have developed CellAnimation, an open-source high-throughput microscopy framework written in MATLAB which is currently being used in several labs at Vanderbilt and elsewhere. We have also developed a novel cell tracking algorithm which supports mitotic event detection and ancestry recording and we have shown that it outperforms the current state-of-the-art. We applied CellAnimation to investigate the differences in motility between LNCaP-34 and LNCaP-17 prostate cancer cell lines, selected for difference in the levels of expression of hepsin, a type II transmembrane serine protease. Hepsin is overexpressed in over 90% of prostate cancers and correlates with tumor progression. Our lab has previously shown that hepsin cleaves laminin-332, an important protein component of the basement membrane that curbs cancer invasion and progression. Automated cell tracking and data analysis demonstrated that hepsin overexpression promotes increased cell speed and displacement but path tortuosity stays the same; net speed increase was accompanied by a switch in integrin use and a more mesenchymal morphology.