Image sequences taken by the time-lapse method are used for migration analysis. To determine the velocity and direction of migration in each cell, we need to determine the center coordinate of the cell. The way to determine the center coordinate in the previous study is to calculate the best-fit ellipsis or center of gravity from the cell contour [30 (link),31 (link),32 (link)]. However, there are two difficulties in yielding the cell contour automatically using a DIC image. One is setting the contour in the unclear part of the cell, and the other is setting the border of the connected cell. To overcome these obstacles, the gravity center of the nucleus fluorescent area was used as the center coordinate of the cell in this study.
The custom algorithm was developed by written python for cell tracing. First, the fluorescence image of the nucleus was binarized, and the noise was removed by filtering the area of fluorescence. To link the cell in each image, a particle tracking velocimetry (PTV) method, one of the velocity vector analysis methods, was applied. In the PTV method, when the flame rate of the camera is high enough compared with a particle velocity in the fluid, a velocity vector of a luminescent particle can be obtained by only linking the closest luminescent particle. In this study, the position of the center of gravity of the fluorescent region of the nucleus was treated in the same way as the luminescent particles in the PTV method, and the vector data of the cells were analyzed and used to explain cell migration (Figure 8 a). The flame rate of the cell migration in this study was four images/hour, which was high enough compared with the scale of cell migration.
The vector data of cells were sorted in four directions as shown inFigure 8 b, and then, the numbers of cells in each direction and the total number of cells were counted. The migration ratio of cells was obtained by dividing the cell count in one direction by the total cell count in all directions. The average velocity in each direction was also calculated from vector data of cells. Migration direction and the average velocity of cells were regarded to be the characteristics of cell migration.
To ensure the accuracy of the PTV method, vector data of cells were also obtained by manually tracing the coordinate of the nucleus gravity center and were used as a comparison target. This manual analysis method is called a manual method in this study.
The custom algorithm was developed by written python for cell tracing. First, the fluorescence image of the nucleus was binarized, and the noise was removed by filtering the area of fluorescence. To link the cell in each image, a particle tracking velocimetry (PTV) method, one of the velocity vector analysis methods, was applied. In the PTV method, when the flame rate of the camera is high enough compared with a particle velocity in the fluid, a velocity vector of a luminescent particle can be obtained by only linking the closest luminescent particle. In this study, the position of the center of gravity of the fluorescent region of the nucleus was treated in the same way as the luminescent particles in the PTV method, and the vector data of the cells were analyzed and used to explain cell migration (
The vector data of cells were sorted in four directions as shown in
To ensure the accuracy of the PTV method, vector data of cells were also obtained by manually tracing the coordinate of the nucleus gravity center and were used as a comparison target. This manual analysis method is called a manual method in this study.
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