Journal: PLoS Biology
Article Title: A critical-like collective state leads to long-range cell communication in Dictyostelium discoideum aggregation
doi: 10.1371/journal.pbio.1002602
Figure Lengend Snippet: (A) Screenshot of simulation for n = 1,000 cells at different time points: prestreaming (left), streaming (center), and after aggregation (right). Red (yellow) points represent nonfiring (firing) cells. See Supporting information for the full movie. (B) Kymograph of nonconnected C nc and connected C c directional correlations for simulation of n = 1,000 cells. Directional correlation profiles C ( r ) were calculated for every time frame and displayed depending on distance r . For cell size, we used the volume exclusion length of 3 μm. (see ). (C) Correlation length versus time for different numbers of cells ( n = 1,200, 1,000, 800, and 600). Data were smoothed with a moving average filter spanning ten consecutive frames. (D) Susceptibility χ plotted with respect to nearest-neighbor (NN) distance for different numbers of cells and with respect to time (inset). Nearest-neighbor distance was rescaled by the volume exclusion length (see ). The peak in susceptibility becomes higher the larger the number of cells, and NN distances decrease accordingly. Profiles in the inset were smoothed with a moving average filter spanning ten points. (E) Comparison of correlation profiles for streaming phase (50-min time window). Connected correlations, calculated for different numbers of cells and normalized so that the correlation length was equal to one, were plotted as a function of distance in units of their respective correlation lengths. The four profiles collapse onto a single curve, independently of the number of cells. (F) Average correlation length versus neighborhood radius for different numbers of simulated cells. L corresponds to the size of the images (389 μm). ξ 0 represents the average correlation length during streaming phase (50-min window). Error bars represent standard errors. See Supporting information for a full explanation of the model and and Data for MATLAB code and data, respectively.
Article Snippet: Images of TRED channels were segmented by using the MATLAB function imextendedmax , which outputs a binary image given by the computation of the local maxima of the input image.
Techniques: Comparison
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