Introduction: Migration mis-regulation is a hallmark of cancer, and remains an important problem in cancer biology. We postulate the needs for better in vitro models to understand the details of cell-matrix interactions. Here, we utilized multiphoton excited (MPE) photochemistry to fabricate models to systematically study migration dynamics operative in breast and ovarian cancer. Gradients are a convenient means to modulate concentration and also have been implicated in metastases.
Methods: We specifically pattern sub-micron structured gradients from laminin and fibronectin whose up-regulation is associated with increased metastasis and poor prognosis. We developed a new continuous linear bi-directional gradient design, permitting exploration of the underlying cell-matrix interactions of migration, including speed, directness, and f-actin cytoskeleton alignment as a function of concentration. These new models provide both contact guidance and ECM binding cues, and provide a more relevant environment than possible with existing technologies such as flow chambers or 2D printed surfaces.
Results: We found an overall increase in these processes with increasing concentration on both laminin and fibronectin gradients for a series of ovarian and breast cancer lines. Moreover, directness was higher for more metastatic cells, indicating that epithelial or mesenchymal state of the cell type governs the dynamics. However, the specifics of the speed and directedness depend on both the cell type and protein, thus we found that we must consider these processes collectively to obtain a self-consistent picture of the migration. For this purpose, we performed a linear discriminate analysis (LDA) and successfully classified the different cell types on the two protein gradients without molecular biology analysis.
Conclusions: The bi-gradient structures are versatile tools to performing detailed studies of cell migration, specifically haptotxis. We further suggest the can be used in assessing efficacy of drug treatments targeted at specific matrix components.