Analysis and modelling of motility of cell populations with MotoCell

BackgroundCell motility plays a central role in development, wound-healing and tumour invasion. Cultures of eucariotic cells are a complex system where most cells move according to 'random' patterns, but may also be induced to a more coordinate migration by means of specific stimuli, such as the presence of chemical attractants or the introduction of a mechanical stimulus. Various tools have been developed that work by keeping track of the paths followed by specific objects and by performing statistical analysis on the recorded path data. The available tools include desktop applications or macros running within a commercial package, which address specific aspects of the process.ResultsAn online application, MotoCell, was developed to evaluate the motility of cell populations maintained in various experimental conditions. Statistical analysis of cell behaviour consists of the evaluation of descriptive parameters such as average speed and angle, directional persistence, path vector length, calculated for the whole population as well as for each cell and for each step of the migration; in this way the behaviour of a whole cell population may be assessed as a whole or as a sum of individual entities. The directional movement of objects may be studied by eliminating the modulo effect in circular statistics analysis, able to evaluate linear dispersion coefficient (R) and angular dispersion (S) values together with average angles. A case study is provided where the system is used to characterize motility of RasV12 transformed NIH3T3 fibroblasts.ConclusionHere we describe a comprehensive tool which takes care of all steps in cell motility analysis, including interactive cell tracking, path editing and statistical analysis of cell movement, all within a freely available online service. Although based on a standard web interface, the program is very fast and interactive and is immediately available to a large number of users, while exploiting the web approach in a very effective way. The ability to evaluate the behaviour of single cells allows to draw the attention on specific correlations, such as linearity of movement and deviation from the expected direction. In addition to population statistics, the analysis of single cells allows to group the cells into subpopulations, or even to evaluate the behaviour of each cell with respect to a variable reference, such as the direction of a wound or the position of the closest cell.

[1]  A. Kashina,et al.  Cell biology of embryonic migration. , 2008, Birth defects research. Part C, Embryo today : reviews.

[2]  Michael A. Stephens,et al.  Tests of fit for the von Mises distribution , 1985 .

[3]  J. Parsons,et al.  Cell Migration--Movin' On , 1999, Science.

[4]  Erik Sahai,et al.  Illuminating the metastatic process , 2007, Nature Reviews Cancer.

[5]  G. Borisy,et al.  Cell Migration: Integrating Signals from Front to Back , 2003, Science.

[6]  Petros Koumoutsakos,et al.  A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cells. , 2007, Journal of structural biology.

[7]  T. Pollard,et al.  Cellular Motility Driven by Assembly and Disassembly of Actin Filaments , 2003, Cell.

[8]  P. E. McHugh,et al.  Mathematical Models of Cell Motility , 2007, Cell Biochemistry and Biophysics.

[9]  G. S. Watson,et al.  Goodness-of-fit tests on a circle. II , 1961 .

[11]  S. R. Jammalamadaka,et al.  Directional Statistics, I , 2011 .

[12]  Lennart Martens,et al.  Cell_motility: a cross-platform, open source application for the study of cell motion paths , 2006, BMC Bioinformatics.

[13]  D. Fletcher,et al.  An introduction to cell motility for the physical scientist , 2004, Physical biology.

[14]  D. Stupack The biology of integrins. , 2007, Oncology.

[15]  B. Heller Circular Statistics in Biology, Edward Batschelet. Academic Press, London & New York (1981), 371, Price $69.50 , 1983 .

[16]  D. Wilkie,et al.  Rayleigh Test for Randomness of Circular Data , 1983 .

[17]  Edward A. Codling,et al.  Random walk models in biology , 2008, Journal of The Royal Society Interface.

[18]  O. Rudenko,et al.  An automatic procedure for evaluation of single cell motility , 2006, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[19]  George Oster,et al.  Polymer Motors: Pushing out the Front and Pulling up the Back , 2003, Current Biology.

[20]  Michael D. Abràmoff,et al.  Image processing with ImageJ , 2004 .