Biophysical informatics approach for quantifying phenotypic heterogeneity in cancer cell migration in confined microenvironments

Motivation: Cancer cell heterogeneity can manifest genetically and phenotypically. Bioinformatics methods have been used to analyze complex genomics and transcriptomics data, but have not been well-established for analyzing biophysical data of phenotypically heterogeneous tumor cells. Here, we take an informatics approach to analyze the biophysical data of MDA-MB-231 cells, a widely used breast cancer cell line, during their spontaneous migration through confined environments. Experimentally, we vary the constriction microchannel geometries (wide channel, short constriction, and long constriction) and apply drug treatments. We find that the cells in the short constriction are similar in morphology to the cells in the wide channel. However, their fluorescence profiles are comparable to those in the long constriction. We demonstrate that the cell migratory phenotype is correlated more to mitochondria in a non-confined environment and more to actin in a confined environment. We demonstrate that the cells' migratory phenotypes are altered by ciliobrevin D, a dynein inhibitor, in both confined and non-confined environments. Overall, our approach elucidates phenotypic heterogeneity in cancer cells under confined microenvironments at single-cell resolution. Results: Here, we apply a bioinformatics approach to a single-cell invasion assay. We demonstrate that this method can determine distinctions in morphology, cytoskeletal activities, and mitochondrial activities under various geometric constraints and for cells of different speeds. Our approach can be readily adapted to various heterogeneity studies for different types of input biophysical data. In addition, this approach can be applied to studies related to biophysical changes due to differences in external stimuli, such as treatment effects on cellular and subcellular activities, at single-cell resolution. Finally, as similar bioinformatics methods have been widely applied in studies of genetic heterogeneity, biophysical information extracted using this approach can be analyzed together with the genetic data to relate genetic and phenotypic heterogeneity.