Label-free Cell Tracking Enables Collective Motion Phenotyping in Epithelial Monolayers

Abstract

SummaryCollective cell migration is an umbrella term for a rich variety of cell behaviors, whose distinct character is essential 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 images with nucleus staining. Tracking, based on the Crocker-Grier algorithm, quantifies nuclei movement and allows for robust downstream analysis of collective motion. Since 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, D2min, which reflects non-affine motion, shows promise as an indicator of metastatic potential.Graphical AbstractHighlightsVersatile AI-based algorithm can robustly identify individual cells and track their motion from phase contrast images.Analysis of motion of cells relative to nearby neighbors distinguishes weakly tumorigenic (KRas) and metastatic (KRas/PTEN-/-) cell lines.