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

BMC Cell Biology

Software

Denis Krndija¹  Götz von Wichert¹  Thomas Seufferlein²  Thomas M Gress³  Malte Buchholz³  Martin Schmucker⁴  Johann M Kraus⁴  Hans A Kestler⁵  Johannes Huth⁶ 

[1] Clinic of Internal Medicine I, Medical Centre Ulm University, Albert-Einstein-Allee 23, D-89081, Ulm, Germany;Clinic of Internal Medicine I, Medical Centre Ulm University, Albert-Einstein-Allee 23, D-89081, Ulm, Germany;Department of Internal Medicine I, Martin-Luther-University, Halle-Wittenberg, Germany;Department of Gastroenterology and Endocrinology, University Hospital of Marburg, Germany;Research group of Bioinformatics and Systems Biology, Institute of Neural Information Processing, Ulm University, Albert-Einstein-Allee 11, D-89081, Ulm, Germany;Research group of Bioinformatics and Systems Biology, Institute of Neural Information Processing, Ulm University, Albert-Einstein-Allee 11, D-89081, Ulm, Germany;Clinic of Internal Medicine I, Medical Centre Ulm University, Albert-Einstein-Allee 23, D-89081, Ulm, Germany;Research group of Bioinformatics and Systems Biology, Institute of Neural Information Processing, Ulm University, Albert-Einstein-Allee 11, D-89081, Ulm, Germany;Department of Gastroenterology and Endocrinology, University Hospital of Marburg, Germany;

关键词: Kalman Filter;    Video Sequence;    Migration Rate;    Video File;    Manual Tracking;   

DOI  :  10.1186/1471-2121-11-24

 received in 2009-08-07, accepted in 2010-04-08,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundCell 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.ResultsWe 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.ConclusionWe 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.

【 授权许可】

Unknown   
© Huth et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.