Label-free cell tracking enables collective motion phenotyping in epithelial monolayers
Shuyao Gu,
Rachel M. Lee,
Zackery Benson,
Chenyi Ling,
Michele I. Vitolo,
Stuart S. Martin,
Joe Chalfoun,
Wolfgang Losert
Affiliations
Shuyao Gu
Department of Physics, University of Maryland, College Park, MD 20742, USA
Rachel M. Lee
Marlene and Stewart Greenebaum NCI Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD 21201, USA; Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA
Zackery Benson
Department of Physics, University of Maryland, College Park, MD 20742, USA
Chenyi Ling
Software and Systems Division, Information Technology Lab, NIST, Gaithersburg, MD 20899, USA
Michele I. Vitolo
Marlene and Stewart Greenebaum NCI Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD 21201, USA; Departments of Pharmacology and Physiology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
Stuart S. Martin
Marlene and Stewart Greenebaum NCI Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD 21201, USA; Departments of Pharmacology and Physiology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
Joe Chalfoun
Software and Systems Division, Information Technology Lab, NIST, Gaithersburg, MD 20899, USA
Wolfgang Losert
Department of Physics, University of Maryland, College Park, MD 20742, USA; Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA; Corresponding author
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.
