Abstract

Collective 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 Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=67 SRC="FIGDIR/small/472148v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@82a35corg.highwire.dtl.DTLVardef@b31442org.highwire.dtl.DTLVardef@f703aborg.highwire.dtl.DTLVardef@1120d98_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIVersatile AI-based algorithm can robustly identify individual cells and track their motion from phase contrast images. C_LIO_LIAnalysis of motion of cells relative to nearby neighbors distinguishes weakly tumorigenic (KRas) and metastatic (KRas/PTEN-/-) cell lines. C_LI