Automated Cell Mechanical Characterization by On-Chip Sequential Squeezing: From Static to Dynamic

The mechanical properties of cells are harmless biomarkers for cell identification and disease diagnosis. Although many systems have been developed to evaluate the static mechanical properties of cells for biomedical research, their robustness, effectiveness, and cost do not meet clinical requirements or the experiments with a large number of cell samples. In this paper, we propose an approach for on-chip cell mechanical characterization by analyzing the dynamic behavior of cells as they pass through multiple constrictions. The proposed serpentine microfluidic channel consisted of 20 constrictions connected in series and divided into five rows for tracking cell dynamic behavior. Assisted by computer vision, the squeezing time of each cell through five rows of constrictions was automatically collected and filtered to evaluate the cell's mechanical deformability. We observed a decreasing passage time and increasing dynamic deformability of the cells as they passed through the multiple constrictions. The deformability increase rate of the HeLa cells was eight times greater than that of MEF cells. Moreover, the weak correlation between the deformability increase rate and the cell size indicated that cell recognition based on measuring the deformability increase rate could hardly be affected by the cell size variation. These findings showed that the deformability increase rate of the cell under on-chip sequential squeezing as a new index has great potential in cancer cell recognition.

Automated summary

This study proposed a new method for characterizing cell mechanical properties by analyzing the dynamic behavior of cells as they pass through multiple constrictions. Experimental results showed a decrease in passage time and an increase in dynamic deformability as cells passed through multiple constrictions. The deformability increase rate of HeLa cells was eight times higher than that of MEF cells, and the weak correlation between the deformability increase rate and cell size suggests that this new method has the potential for cancer cell recognition.