Abstract
Tumor cell heterogeneity drives disease progression and response to therapy, and therefore, there is a need for single-cell analysis methods. In this paper, we present an integrated, scalable method to analyze enzymatic activity in many individual cancer cells at once. The reported method uses dielectrophoresis (DEP) to selectively capture tumor cells at wireless electrodes aligned to an overlying array of cell-sized micropockets. Following hydrodynamic transfer of the captured cells into microfluidic chambers, the chambers are fluidically isolated and sealed with a hydrophobic ionic liquid, which possesses sufficient conductivity to allow for subsequent electrical lysis of the cells to access their contents for enzymatic assay. The wireless electrodes have an interlocking spiral design that ensures successful electrical lysis regardless of the location of the cell within the chamber. Here, breast cancer cells are assessed for β-galactosidase through its activation of a fluorogenic substrate. A key point is that the fluorogenic assay solution was optimized to allow for dielectrophoretic cell capture, thereby obviating the need for a solution exchange step. Our approach has several distinct advantages including a high rate of single-cell capture, a capture efficiency that is independent of the dimensions of the reaction chambers, no need for mechanical closure of reaction volumes, and no observed cross-talk. In this study, first, the steps of cell capture, transfer, and lysis are established on this platform in the presence of the optimized assay solution. We then quantify the increase in fluorescence intensity obtained over the duration of the enzymatic assay of individual cells. Finally, this method is applied to the analysis of β-galactosidase activity in 258 individual MDA-MB-231 breast cancer cells, revealing heterogeneity in expression of this enzyme in this cell line. We expect that the adaptability of this method will allow for expanded studies of single-cell enzymatic expression and activity. This will in turn open avenues of research into cancer cell heterogeneity in metabolism, invasiveness, and drug response. The ability to study these features of cancer at the single-cell level raises the possibility for treatment plans tailored to target the specific combinations of cell subpopulations present in tumors. Furthermore, we expect that this method can be adapted to uses outside of cancer research, such as studies of neuron metabolism, pathogenesis in bacteria, and stem cell development.