Quantitative Analysis of Collective Migration by Single-Cell Tracking Aimed at Understanding Cancer Metastasis.

Metastasis is a major complication of cancer treatments. Studies of the migratory behavior of cells are needed to investigate and control metastasis. Metastasis is based on the epithelial-mesenchymal transition, in which epithelial cells acquire mesenchymal properties and the ability to leave the population to invade other regions of the body. In collective migration, highly migratory "leader" cells are found at the front of the cell population, as well as cells that "follow" these leader cells. However, the interactions between these cells are not well understood. We examined the migration properties of leader-follower cells during collective migration at the single-cell level. Different mixed ratios of "leader" and "follower" cell populations were compared. Collective migration was quantitatively analyzed from two perspectives: cell migration within the colony and migration of the entire colony. Analysis of the effect of the cell mixing ratio on migration behavior showed that a small number of highly migratory cells enhanced some of the migratory properties of other cells. The results provide useful insights into the cellular interactions in collective cell migration of cancer cell invasion.

Time-Series Clustering of Single-Cell Trajectories in Collective Cell Migration.

Collective invasion drives multicellular cancer cells to spread to surrounding normal tissues. To fully comprehend metastasis, the methodology of analysis of individual cell migration in tissue should be well developed. Extracting and classifying cells with similar migratory characteristics in a colony would facilitate an understanding of complex cell migration patterns. Here, we used electrospun fibers as the extracellular matrix for the in vitro modeling of collective cell migration, clustering of mesenchymal and epithelial cells based on trajectories, and analysis of collective migration patterns based on trajectory similarity. We normalized the trajectories to eliminate the effect of cell location on clustering and used uniform manifold approximation and projection to perform dimensionality reduction on the time-series data before clustering. When the clustering results were superimposed on the trajectories before normalization, the results still exhibited positional similarity, thereby demonstrating that this method can identify cells with similar migration patterns. The same cluster contained both mesenchymal and epithelial cells, and this result was related to cell location and cell division. These data highlight the reliability of this method in identifying consistent migration patterns during collective cell migration. This provides new insights into the epithelial-mesenchymal interactions that affect migration patterns.