Correlation and Differentiation Based Algorithms for Cell Mobility Quantification

Abstract

Quantification of cell mobility in confluent cell cultures or on coverslips with high cell density is a challenge, because tracking of individual cells is not readily possible. Two procedures have been developed and tested to solve this problem, namely a differential (MoveDif) and a correlation (MoveCor) algorithm. Both compare successive image frames and calculate a difference parameter between them. Image preprocessing was required to neutralize brightness changes and inhomogeneous illumination. Additionally, structures and membranes in the images had to be enhanced and standardized by edge-detection filters. We showed that both algorithms had advantages in different experimental setups, depending of the complexity of the cellular movement. Besides the determination of global cell mobility, the membrane contractility and movement of single cells, small cell groups and intracellular structures could be quantified, if individual cells were separated and magnified. The discriminatory potency and sensitivity of both algorithms were tested by drug induced changes of cellular movement speeds and their robustness against brightness changes were analyzed too. To demonstrate the mobility effects of ketamine on dexamethasone-treated hippocampal neurons, we found that the correlation algorithm MoveCor could quantify the ketamine-dependent increase in cell movement speed after prior treatment with dexamethasone had reduced it distinctively. We also could measure how staurosporine slowed down the mobility of acidic intracellular organelles in highly magnified glial cells from chicken telencephalon. The differentiation algorithm MoveDif was employed to demonstrate that the contractility of pericyte membranes slowed down after induction of oxygen glucose deprivation. The limitations of both methods were given by their inability to produce absolute mobility values but these drawbacks can be solved by appropriate image preprocessing.