Quantitative and unbiased analysis of directional persistence in cell migration

The mechanism by which cells control directional persistence during migration is a major question. However, the common index measuring directional persistence, namely the ratio of displacement to trajectory length, is biased, particularly by cell speed. An unbiased method is to calculate direction autocorrelation as a function of time. This function depends only on the angles of the vectors tangent to the trajectory. This method has not been widely used, because it is more difficult to compute. Here we discuss biases of the classical index and introduce a custom-made open-source computer program, DiPer, which calculates direction autocorrelation. In addition, DiPer also plots and calculates other essential parameters to analyze cell migration in two dimensions: it displays cell trajectories individually and collectively, and it calculates average speed and mean square displacements (MSDs) to assess the area explored by cells over time. This user-friendly program is executable through Microsoft Excel, and it generates plots of publication-level quality. The protocol takes ∼15 min to complete. We have recently used DiPer to analyze cell migration of three different mammalian cell types in 2D cultures: the mammary carcinoma cell line MDA-MB-231, the motile amoeba Dictyostelium discoideum and fish-scale keratocytes. DiPer can potentially be used not only for random migration in 2D but also for directed migration and for migration in 3D (direction autocorrelation only). Moreover, it can be used for any types of tracked particles: cellular organelles, bacteria and whole organisms.

[1]  Douglas A. Chapnick,et al.  The Development of a Novel High Throughput Computational Tool for Studying Individual and Collective Cellular Migration , 2013, PloS one.

[2]  Richard B. Dickinson,et al.  Optimal estimation of cell movement indices from the statistical analysis of cell tracking data , 1993 .

[3]  Dunn Ga,et al.  Characterising a kinesis response: time averaged measures of cell speed and directional persistence. , 1983 .

[4]  Robert H. Insall,et al.  Understanding eukaryotic chemotaxis: a pseudopod-centred view , 2010, Nature Reviews Molecular Cell Biology.

[5]  Liang Li,et al.  Persistent Cell Motion in the Absence of External Signals: A Search Strategy for Eukaryotic Cells , 2008, PloS one.

[6]  Dennis Bray,et al.  Cell Movements: From Molecules to Motility , 1992 .

[7]  Migration of Cells in a Social Context , 2013 .

[8]  G. Uhlenbeck,et al.  On the Theory of the Brownian Motion , 1930 .

[9]  Arthur Getis,et al.  A History of the Concept of Spatial Autocorrelation: A Geographer's Perspective , 2008 .

[10]  Nadine Peyriéras,et al.  Inhibitory signalling to the Arp2/3 complex steers cell migration , 2013, Nature.

[11]  R. Preuss,et al.  Anomalous dynamics of cell migration , 2008, Proceedings of the National Academy of Sciences.

[12]  Kenneth M. Yamada,et al.  Random versus directionally persistent cell migration , 2009, Nature Reviews Molecular Cell Biology.

[13]  Anne Straube,et al.  Directional persistence of migrating cells requires Kif1C-mediated stabilization of trailing adhesions. , 2012, Developmental cell.

[14]  C. H. Green,et al.  Organization and patterns of inter- and intraspecific variation in the behaviour of Drosophila larvae , 1983, Animal Behaviour.

[15]  P. Mazur On the theory of brownian motion , 1959 .

[16]  Pascal Silberzan,et al.  Automated velocity mapping of migrating cell populations (AVeMap) , 2012, Nature Methods.

[17]  Alka A. Potdar,et al.  Human Mammary Epithelial Cells Exhibit a Bimodal Correlated Random Walk Pattern , 2010, PloS one.

[18]  Erik Meijering,et al.  Methods for cell and particle tracking. , 2012, Methods in enzymology.

[19]  Yoshinori Hayakawa,et al.  A Quorum-Sensing Factor in Vegetative Dictyostelium Discoideum Cells Revealed by Quantitative Migration Analysis , 2011, PloS one.

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