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Automated cell tracking and analysis in phase-contrast videos (iTrack4U): development of Java software based on combined mean-shift processes.

Cordelières FP, Petit V, Kumasaka M, Debeir O, Letort V, Gallagher SJ, Larue L - PLoS ONE (2013)

Bottom Line: Compared to manual tracking, it saves considerable amount of time to generate and analyze the variables characterizing cell migration, since they are automatically computed with iTrack4U.Another major interest of iTrack4U is the standardization and the lack of inter-experimenter differences.Finally, iTrack4U is adapted for phase contrast and fluorescent cells.

View Article: PubMed Central - PubMed

Affiliation: Institut Curie, CNRS UMR3348, plate-forme IBISA d'imagerie cellulaire et tissulaire, Orsay, France.

ABSTRACT
Cell migration is a key biological process with a role in both physiological and pathological conditions. Locomotion of cells during embryonic development is essential for their correct positioning in the organism; immune cells have to migrate and circulate in response to injury. Failure of cells to migrate or an inappropriate acquisition of migratory capacities can result in severe defects such as altered pigmentation, skull and limb abnormalities during development, and defective wound repair, immunosuppression or tumor dissemination. The ability to accurately analyze and quantify cell migration is important for our understanding of development, homeostasis and disease. In vitro cell tracking experiments, using primary or established cell cultures, are often used to study migration as cells can quickly and easily be genetically or chemically manipulated. Images of the cells are acquired at regular time intervals over several hours using microscopes equipped with CCD camera. The locations (x,y,t) of each cell on the recorded sequence of frames then need to be tracked. Manual computer-assisted tracking is the traditional method for analyzing the migratory behavior of cells. However, this processing is extremely tedious and time-consuming. Most existing tracking algorithms require experience in programming languages that are unfamiliar to most biologists. We therefore developed an automated cell tracking program, written in Java, which uses a mean-shift algorithm and ImageJ as a library. iTrack4U is a user-friendly software. Compared to manual tracking, it saves considerable amount of time to generate and analyze the variables characterizing cell migration, since they are automatically computed with iTrack4U. Another major interest of iTrack4U is the standardization and the lack of inter-experimenter differences. Finally, iTrack4U is adapted for phase contrast and fluorescent cells.

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Geometric characteristics of cell trajectories associated with distances and extracted by manual and automatic tracking.Cells were imaged every four minutes for 12 hours and experiments were repeated three times. The same 40 independent cells were tracked manually (M) and automatically (A). The following variables were extracted from the manually and automatically retrieved sets of coordinates:.A. Total distance of migration by WM852 human melanoma cells. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.0774).B. Euclidian distance (start-end distance) of WM852 human melanoma cells. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.9672).C. Persistence of migration by WM852 human melanoma cells. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.5012).D. Definition of migration variables used in this figure. Total distance = dttl, Euclidian distance = dS-E, persistence = dttl / dS-E, minimum travelled distance = dmin, maximum travelled distance = dmax.E. Average distance of migration by WM852 human melanoma cells. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.0774 and p = 0.3913 for the average distance and standard deviation, respectively). F. Extreme values (minimum and maximum distances) of migration for WM852 human melanoma cells. Manual and automatic methods were not statistically significant for the maximum distance (standard unpaired t-test, p = 0.2611). A significant difference for the minimum distance has no real meaning, as explained in the text (standard unpaired t-test, p = 0.001).
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pone-0081266-g003: Geometric characteristics of cell trajectories associated with distances and extracted by manual and automatic tracking.Cells were imaged every four minutes for 12 hours and experiments were repeated three times. The same 40 independent cells were tracked manually (M) and automatically (A). The following variables were extracted from the manually and automatically retrieved sets of coordinates:.A. Total distance of migration by WM852 human melanoma cells. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.0774).B. Euclidian distance (start-end distance) of WM852 human melanoma cells. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.9672).C. Persistence of migration by WM852 human melanoma cells. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.5012).D. Definition of migration variables used in this figure. Total distance = dttl, Euclidian distance = dS-E, persistence = dttl / dS-E, minimum travelled distance = dmin, maximum travelled distance = dmax.E. Average distance of migration by WM852 human melanoma cells. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.0774 and p = 0.3913 for the average distance and standard deviation, respectively). F. Extreme values (minimum and maximum distances) of migration for WM852 human melanoma cells. Manual and automatic methods were not statistically significant for the maximum distance (standard unpaired t-test, p = 0.2611). A significant difference for the minimum distance has no real meaning, as explained in the text (standard unpaired t-test, p = 0.001).

Mentions: Once the sequence of coordinates of a selected cell is computed, the software automatically computes different variables that characterize cell trajectory and motility (as detailed below and in the user's guide [File S1]). To illustrate the results, we analyzed various cell lines, including melanocytes and melanoma, both manually and automatically. The variables describing the migration of the WM852 human melanoma cell line are presented in Figures 3 and 4. The total distance of migration, and the Euclidian distance between the start and end of the track, were evaluated for each WM852 cell and averaged (Figure 3A,B). The persistence of cell migration is defined as the ratio of the total distance over the Euclidian start-end distance (Figure 3C,D). For these variables, the difference between the results extracted by manual and automatic methods was not statistically significant.


Automated cell tracking and analysis in phase-contrast videos (iTrack4U): development of Java software based on combined mean-shift processes.

Cordelières FP, Petit V, Kumasaka M, Debeir O, Letort V, Gallagher SJ, Larue L - PLoS ONE (2013)

Geometric characteristics of cell trajectories associated with distances and extracted by manual and automatic tracking.Cells were imaged every four minutes for 12 hours and experiments were repeated three times. The same 40 independent cells were tracked manually (M) and automatically (A). The following variables were extracted from the manually and automatically retrieved sets of coordinates:.A. Total distance of migration by WM852 human melanoma cells. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.0774).B. Euclidian distance (start-end distance) of WM852 human melanoma cells. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.9672).C. Persistence of migration by WM852 human melanoma cells. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.5012).D. Definition of migration variables used in this figure. Total distance = dttl, Euclidian distance = dS-E, persistence = dttl / dS-E, minimum travelled distance = dmin, maximum travelled distance = dmax.E. Average distance of migration by WM852 human melanoma cells. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.0774 and p = 0.3913 for the average distance and standard deviation, respectively). F. Extreme values (minimum and maximum distances) of migration for WM852 human melanoma cells. Manual and automatic methods were not statistically significant for the maximum distance (standard unpaired t-test, p = 0.2611). A significant difference for the minimum distance has no real meaning, as explained in the text (standard unpaired t-test, p = 0.001).
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC3842324&req=5

pone-0081266-g003: Geometric characteristics of cell trajectories associated with distances and extracted by manual and automatic tracking.Cells were imaged every four minutes for 12 hours and experiments were repeated three times. The same 40 independent cells were tracked manually (M) and automatically (A). The following variables were extracted from the manually and automatically retrieved sets of coordinates:.A. Total distance of migration by WM852 human melanoma cells. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.0774).B. Euclidian distance (start-end distance) of WM852 human melanoma cells. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.9672).C. Persistence of migration by WM852 human melanoma cells. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.5012).D. Definition of migration variables used in this figure. Total distance = dttl, Euclidian distance = dS-E, persistence = dttl / dS-E, minimum travelled distance = dmin, maximum travelled distance = dmax.E. Average distance of migration by WM852 human melanoma cells. Manual and automatic methods were not statistically significant (standard unpaired t-test, p = 0.0774 and p = 0.3913 for the average distance and standard deviation, respectively). F. Extreme values (minimum and maximum distances) of migration for WM852 human melanoma cells. Manual and automatic methods were not statistically significant for the maximum distance (standard unpaired t-test, p = 0.2611). A significant difference for the minimum distance has no real meaning, as explained in the text (standard unpaired t-test, p = 0.001).
Mentions: Once the sequence of coordinates of a selected cell is computed, the software automatically computes different variables that characterize cell trajectory and motility (as detailed below and in the user's guide [File S1]). To illustrate the results, we analyzed various cell lines, including melanocytes and melanoma, both manually and automatically. The variables describing the migration of the WM852 human melanoma cell line are presented in Figures 3 and 4. The total distance of migration, and the Euclidian distance between the start and end of the track, were evaluated for each WM852 cell and averaged (Figure 3A,B). The persistence of cell migration is defined as the ratio of the total distance over the Euclidian start-end distance (Figure 3C,D). For these variables, the difference between the results extracted by manual and automatic methods was not statistically significant.

Bottom Line: Compared to manual tracking, it saves considerable amount of time to generate and analyze the variables characterizing cell migration, since they are automatically computed with iTrack4U.Another major interest of iTrack4U is the standardization and the lack of inter-experimenter differences.Finally, iTrack4U is adapted for phase contrast and fluorescent cells.

View Article: PubMed Central - PubMed

Affiliation: Institut Curie, CNRS UMR3348, plate-forme IBISA d'imagerie cellulaire et tissulaire, Orsay, France.

ABSTRACT
Cell migration is a key biological process with a role in both physiological and pathological conditions. Locomotion of cells during embryonic development is essential for their correct positioning in the organism; immune cells have to migrate and circulate in response to injury. Failure of cells to migrate or an inappropriate acquisition of migratory capacities can result in severe defects such as altered pigmentation, skull and limb abnormalities during development, and defective wound repair, immunosuppression or tumor dissemination. The ability to accurately analyze and quantify cell migration is important for our understanding of development, homeostasis and disease. In vitro cell tracking experiments, using primary or established cell cultures, are often used to study migration as cells can quickly and easily be genetically or chemically manipulated. Images of the cells are acquired at regular time intervals over several hours using microscopes equipped with CCD camera. The locations (x,y,t) of each cell on the recorded sequence of frames then need to be tracked. Manual computer-assisted tracking is the traditional method for analyzing the migratory behavior of cells. However, this processing is extremely tedious and time-consuming. Most existing tracking algorithms require experience in programming languages that are unfamiliar to most biologists. We therefore developed an automated cell tracking program, written in Java, which uses a mean-shift algorithm and ImageJ as a library. iTrack4U is a user-friendly software. Compared to manual tracking, it saves considerable amount of time to generate and analyze the variables characterizing cell migration, since they are automatically computed with iTrack4U. Another major interest of iTrack4U is the standardization and the lack of inter-experimenter differences. Finally, iTrack4U is adapted for phase contrast and fluorescent cells.

Show MeSH
Related in: MedlinePlus