1. Single-Cell Tracking of Breast Cancer Cells Enables Prediction of Sphere Formation from Early Cell Divisions
- Author
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Rachel M. Lee, Patrick C. Bailey, Keyata Thompson, Kristi R. Chakrabarti, Michele Vitolo, Eleanor C Ory, Cornell J. Lee, Stuart S. Martin, and Stephen J.P. Pratt
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Biology Experimental Methods ,0301 basic medicine ,education.field_of_study ,Multidisciplinary ,Cell division ,Cell ,Population ,Biology ,Sphere formation ,Article ,Cell aggregation ,Cell biology ,03 medical and health sciences ,030104 developmental biology ,0302 clinical medicine ,medicine.anatomical_structure ,030220 oncology & carcinogenesis ,medicine ,lcsh:Q ,Seeding ,Breast cancer cells ,Cell tracking ,lcsh:Science ,education ,Cancer - Abstract
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., Graphical Abstract, Highlights • Single-cell tracking removes confounding aggregation from the mammosphere assay • Tracking reveals sphere-forming efficiencies much higher than commonly reported • True clonal spheres are smaller than commonly reported • At 24 hours, tracking can predict total day 14 spheres with 98% accuracy, Biology Experimental Methods; Cancer
- Published
- 2018
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