Label-free cell tracking enables collective motion phenotyping in epithelial monolayers

Collective cell migration is an umbrella term for a rich variety of cell behaviors, whose distinct character is important for biological function, notably for cancer metastasis. One essential feature of collective behavior is the motion of cells relative to their immediate neighbors. We introduce an AI-based pipeline to segment and track cell nuclei from phase-contrast images. Nuclei segmentation is based on a U-Net convolutional neural network trained on image - with nucleus staining. Tracking, based on the Crocker-Grier algorithm, quantifies nuclei movement and allows for robust downstream analysis of collective motion. Because the AI algorithm required no new training data, our approach promises to be applicable to and yield new insights for vast libraries of existing collective motion images. In a systematic analysis of a cell line panel with oncogenic mutations, we find that the collective rearrangement metric, D-min(2), which reflects non-affine motion, shows promise as an indicator of metastatic potential.

Automated summary

Collective cell migration, a crucial factor in biological functions such as cancer metastasis, involves diverse cell behaviors. Researchers have developed an AI-based pipeline to segment and track cell nuclei, enabling quantification and analysis of collective motion. This approach, which does not require additional training data, has the potential to provide new insights into existing libraries of collective motion images and may serve as an indicator of metastatic potential.