To lead or to herd: optimal strategies for 3D collective migration of cell clusters

Direct visualization of emergent metastatic features within an ex vivo model of the tumor microenvironment

Preventing tumor cells from acquiring metastatic properties would significantly reduce cancer mortality ^1-5 . However, due to the complex nature of this process, it remains one of the most poorly understood and untreatable aspects of cancer ^6-8 . Ischemia and hypoxia in solid tumors are requisite in metastasis formation - conditions that arise far from functional blood vessels and deep within tumor tissues ^9,10 . These secluded locations impede the observation of pre-metastatic tumor cells and their interactions with stromal cells, which are also critical in the initiation of this process ^11,12 . Thus, the initiation of metastasis has been incredibly difficult to model in the lab and to observe in vivo . We present an ex vivo model of the tumor microenvironment, called 3MIC, which overcomes these experimental challenges and enables the observation of ischemic tumor cells in their native 3D context with high spatial and temporal resolutions. The 3MIC recreates ischemic conditions in the tumor microenvironment and facilitates the co-culture of different cell types. Using live microscopy, we showed that ischemia, but not hypoxia alone, increases the motility and invasive properties of cells derived from primary tumors. These changes are phenotypic and can occur without clonal selection. We directly observed how interactions with stromal cells such as macrophages increased tumor invasion in conjunction with the effects of an ischemic microenvironment. Finally, we tested the effects of chemotherapy drugs under different metabolic microenvironments and found that ischemic tumor cells are more resistant to paclitaxel, possibly due to a metabolic resistance mechanism. Overall, the 3MIC is a cost-effective system that allows for the dissection of the complexity of the tumor microenvironment and direct observation of the emergence of metastasis, as well as the testing of treatments that may halt this process.

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

Simple Summary

In this study, we normalized trajectories containing both mesenchymal and epithelial cells to remove the effect of cell location on clustering, and performed a dimensionality reduction on the time series data before clustering. When the clustering results were superimposed on the trajectories prior to normalization, the results still showed similarities in location, indicating that this method can find cells with similar migration patterns. These data highlight the reliability of this method in identifying consistent migration patterns in collective cell migration.

Abstract

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.

Computational models of migration modes improve our understanding of metastasis

Tumor cells migrate through changing microenvironments of diseased and healthy tissue, making their migration particularly challenging to describe. To better understand this process, computational models have been developed for both the ameboid and mesenchymal modes of cell migration. Here, we review various approaches that have been used to account for the physical environment's effect on cell migration in computational models, with a focus on their application to understanding cancer metastasis and the related phenomenon of durotaxis. We then discuss how mesenchymal migration models typically simulate complex cell–extracellular matrix (ECM) interactions, while ameboid migration models use a cell-focused approach that largely ignores ECM when not acting as a physical barrier. This approach greatly simplifies or ignores the mechanosensing ability of ameboid migrating cells and should be reevaluated in future models. We conclude by describing future model elements that have not been included to date but would enhance model accuracy.