Automated, contour-based tracking and analysis of cell behaviour over long time scales in environments of varying complexity and cell density.

Autor:	Baker RM; Department of Biomedical and Chemical Engineering, Syracuse University, Syracuse, NY 13244, USA Syracuse Biomaterials Institute, Syracuse University, Syracuse, NY 13244, USA., Brasch ME; Department of Biomedical and Chemical Engineering, Syracuse University, Syracuse, NY 13244, USA Syracuse Biomaterials Institute, Syracuse University, Syracuse, NY 13244, USA., Manning ML; Syracuse Biomaterials Institute, Syracuse University, Syracuse, NY 13244, USA Department of Physics, Syracuse University, Syracuse, NY 13244, USA., Henderson JH; Department of Biomedical and Chemical Engineering, Syracuse University, Syracuse, NY 13244, USA Syracuse Biomaterials Institute, Syracuse University, Syracuse, NY 13244, USA jhhender@syr.edu.
Jazyk:	angličtina
Zdroj:	Journal of the Royal Society, Interface [J R Soc Interface] 2014 Aug 06; Vol. 11 (97), pp. 20140386.
DOI:	10.1098/rsif.2014.0386
Abstrakt:	Understanding single and collective cell motility in model environments is foundational to many current research efforts in biology and bioengineering. To elucidate subtle differences in cell behaviour despite cell-to-cell variability, we introduce an algorithm for tracking large numbers of cells for long time periods and present a set of physics-based metrics that quantify differences in cell trajectories. Our algorithm, termed automated contour-based tracking for in vitro environments (ACTIVE), was designed for adherent cell populations subject to nuclear staining or transfection. ACTIVE is distinct from existing tracking software because it accommodates both variability in image intensity and multi-cell interactions, such as divisions and occlusions. When applied to low-contrast images from live-cell experiments, ACTIVE reduced error in analysing cell occlusion events by as much as 43% compared with a benchmark-tracking program while simultaneously tracking cell divisions and resulting daughter-daughter cell relationships. The large dataset generated by ACTIVE allowed us to develop metrics that capture subtle differences between cell trajectories on different substrates. We present cell motility data for thousands of cells studied at varying densities on shape-memory-polymer-based nanotopographies and identify several quantitative differences, including an unanticipated difference between two 'control' substrates. We expect that ACTIVE will be immediately useful to researchers who require accurate, long-time-scale motility data for many cells. (© 2014 The Author(s) Published by the Royal Society. All rights reserved.)
Databáze:	MEDLINE
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