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Summary: 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 images 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, D2min, which reflects non-affine motion, shows promise as an indicator of metastatic potential.

Summary: The mammosphere assay has become widely employed to quantify stem-like cells in a population. However, the problem is there is no standard protocol employed by the field. Cell seeding densities of 1,000 to 100,000 cells/mL have been reported. These high densities lead to cellular aggregation. To address this, we have individually tracked 1,127 single MCF-7 and 696 single T47D human breast tumor cells by eye over the course of 14 days. This tracking has given us detailed information for the commonly used endpoints of 5, 7, and 14 days that is unclouded by cellular aggregation. This includes mean sphere sizes, sphere-forming efficiencies, and a well-defined minimum size for both lines. Importantly, we have correlated early cell division with eventual sphere formation. At 24 hr post seeding, we can predict the total spheres on day 14 with 98% accuracy in both lines. This approach removes cell aggregation and potentially shortens a 5- to 14-day assay to a 24 hours. : Biology Experimental Methods; Cancer Subject Areas: Biology Experimental Methods, Cancer