Label-Free Automated Cell Tracking: Analysis of the Role of E-cadherin Expression in Collective Electrotaxis
- 181 Downloads
Collective cell migration plays an important role in wound healing, organogenesis, and the progression of metastatic disease. Analysis of collective migration typically involves laborious and time-consuming manual tracking of individual cells within cell clusters over several dozen or hundreds of frames. Herein, we develop a label-free, automated algorithm to identify and track individual epithelial cells within a free-moving cluster. We use this algorithm to analyze the effects of partial E-cadherin knockdown on collective migration of MCF-10A breast epithelial cells directed by an electric field. Our data show that E-cadherin knockdown in free-moving cell clusters diminishes electrotactic potential, with empty vector MCF-10A cells showing 16% higher directedness than cells with E-cadherin knockdown. Decreased electrotaxis is also observed in isolated cells at intermediate electric fields, suggesting an adhesion-independent role of E-cadherin in regulating electrotaxis. In additional support of an adhesion-independent role of E-cadherin, isolated cells with reduced E-cadherin expression reoriented within an applied electric field 60% more quickly than control. These results have implications for the role of E-cadherin expression in electrotaxis and demonstrate proof-of-concept of an automated algorithm that is broadly applicable to the analysis of collective migration in a wide range of physiological and pathophysiological contexts.
KeywordsCell–cell interactions Electrotaxis Guidance cues Image analysis
We thank the members of the Asthagiri group for helpful discussions. This work was supported by the National Institutes of Health Grant R01CA138899.
Conflict of Interest
Mark L. Lalli, Brooke Wojeski, and Anand R. Asthagiri declare that they have no conflict of interest.
No human or animal studies were carried out by the authors for this article.
- 1.Aftab, O., M. Fryknäs, U. Hammerling, R. Larsson, and M. G. Gustafsson. Detection of cell aggregation and altered cell viability by automated label-free video microscopy: A promising alternative to endpoint viability assays in high- throughput screening. J. Biomol. Screen. 20:372–381, 2015.CrossRefGoogle Scholar
- 4.Arora, A., and T. Qazi. Computer vision based tracking of biological cells: A review. Int. Conf. Adv. Res. Innov. 118–126, 2014.Google Scholar
- 5.Bengtsson, E., C. Wahlby, and J. Lindblad. Robust Cell Image segmentation methods. Pattern Recognit. Image Anal. 14:157–167, 2004.Google Scholar
- 18.Jaccard, N., N. Szita, and L. D. Griffin. Computer methods in biomechanics and biomedical engineering: Imaging and visualization segmentation of phase contrast microscopy images based on multi-scale local Basic Image Features histograms. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 1–9, 2015.Google Scholar
- 21.Latt, S. A. Optical studies of metaphase chromosome organization. Clin. Genet. 19:154–161, 1976.Google Scholar
- 30.Oka, H., H. Shiozaki, K. Kobayashi, M. Inoue, H. Tahara, T. Kobayashi, Y. Takatsuka, N. Matsuyoshi, S. Hirano, M. Takeichi, and T. Mori. Expression of E-cadherin cell adhesion molecules in human breast cancer tissues and its relationship to metastasis. Cancer Res. 53:1696–1701, 1993.Google Scholar
- 31.Olivier, N., M. A. Luengo-oroz, L. Duloquin, E. Faure, T. Savy, I. Veilleux, X. Solinas, D. Débarre, P. Bourgine, A. Santos, N. Peyriéras, and E. Beaurepaire. Cell Lineage Reconstr. Early. 70, 2007.Google Scholar
- 39.Schindelin, J., I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J.-Y. Tinevez, D. J. White, V. Hartenstein, K. Liceiri, P. Tomancak, and A. Cardona. Fiji: an open source platform for biological image analysis. Nat. Methods 9:676–682, 2012.CrossRefGoogle Scholar
- 41.Sommers, C., E. Gelmann, C. L. Sommers, E. W. Thompson, R. Kemlen, E. P. Gelmann, S. W. Byers, and A. Torn. Cell adhesion molecule uvomorulin expression in human breast cancer cell lines: Relationship to morphology and invasive capacities Uvomorulin Breast Cancer Cell Lines: Relationship and Invasive in Human to Morphology. Cell Growth Differ. 2:365–372, 1991.Google Scholar
- 43.Tsai, H. F., C. W. Huang, H. F. Chang, J. J. W. Chen, C. H. Lee, and J. Y. Cheng. Evaluation of EGFR and RTK signaling in the electrotaxis of lung adenocarcinoma cells under direct-current electric field stimulation. PLoS ONE 8:1–20, 2013.Google Scholar
- 45.Veit, W., C. Held, R. Palmisano, and T. Wittenberg. Segmentation of HeLa cells in phase-contrast images with and without DAPI stained cell nuclei. Biomed. Technol. 57:519–522, 2012.Google Scholar
- 47.Witta, S. E., R. M. Gemmill, F. R. Hirsch, C. D. Coldren, K. Hedman, L. Ravdel, B. Helfrich, R. Dziadziuszko, D. C. Chan, M. Sugita, Z. Chan, A. Baron, W. Franklin, H. A. Drabkin, L. Girard, A. F. Gazdar, J. D. Minna, and P. A. Bunn. Restoring E-cadherin expression increases sensitivity to epidermal growth factor receptor inhibitors in lung cancer cell lines. Cancer Res. 66:944–950, 2006.CrossRefGoogle Scholar