AI-enabled Analysis of collective cell dynamics driven by electric fields

Collective cell motion is critical for the development and healing of organisms. Electric fields are known to impact this collective behavior and may impact both individual cells and their interactions. Particle image velocimetry (PIV) has been widely used to shed light on collective rearrangements. In PIV-based analysis, dynamic information is collected based on small segments of the image, regardless of the local cell density. We present an AI-based cell segmentation and tracking workflow that can identify the motion of individual cells in a group from phase contrast images, and demonstrate its capability of providing detailed cell scale information on the link between individual and collective behavior. AI-based cell segmentation and tracking show that a weak electric field is sufficient to guide the direction of motion of cells without changing their speed, whereas a strong electric field also increases the speed of a subset of cells in the sheet. The analysis also reveals a significant delay of the cell sheet in its response to a switching of the electric field, when the field is weak.