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Significantly improved precision of cell migration analysis in time-lapse video microscopy through use of a fully automated tracking system

Johannes Huth12, Malte Buchholz2, Johann M Kraus1, Martin Schmucker1, Götz von Wichert3, Denis Krndija3, Thomas Seufferlein34, Thomas M Gress2 and Hans A Kestler13*

Author affiliations

1 Research group of Bioinformatics and Systems Biology, Institute of Neural Information Processing, Ulm University, Albert-Einstein-Allee 11, D-89081 Ulm, Germany

2 Department of Gastroenterology and Endocrinology, University Hospital of Marburg, Germany

3 Clinic of Internal Medicine I, Medical Centre Ulm University, Albert-Einstein-Allee 23, D-89081 Ulm, Germany

4 Department of Internal Medicine I, Martin-Luther-University, Halle-Wittenberg, Germany

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Citation and License

BMC Cell Biology 2010, 11:24  doi:10.1186/1471-2121-11-24

Published: 8 April 2010



Cell motility is a critical parameter in many physiological as well as pathophysiological processes. In time-lapse video microscopy, manual cell tracking remains the most common method of analyzing migratory behavior of cell populations. In addition to being labor-intensive, this method is susceptible to user-dependent errors regarding the selection of "representative" subsets of cells and manual determination of precise cell positions.


We have quantitatively analyzed these error sources, demonstrating that manual cell tracking of pancreatic cancer cells lead to mis-calculation of migration rates of up to 410%. In order to provide for objective measurements of cell migration rates, we have employed multi-target tracking technologies commonly used in radar applications to develop fully automated cell identification and tracking system suitable for high throughput screening of video sequences of unstained living cells.


We demonstrate that our automatic multi target tracking system identifies cell objects, follows individual cells and computes migration rates with high precision, clearly outperforming manual procedures.