Abstract
Cellular contacts modify the way cells migrate in a cohesive group with respect to a free single cell. The resulting motion is persistent and correlated, with cells’ velocities self-aligning in time. The presence of a dense agglomerate of cells makes the application of single particle tracking techniques to define cells dynamics difficult, especially in the case of phase contrast images. Here, we propose an original pipeline for the analysis of phase contrast images of the wound healing scratch assay acquired in time-lapse, with the aim of extracting single particle trajectories describing the dynamics of the wound closure. In such an approach, the membrane of the cells at the border of the wound is taken as a unicum, i.e., the wound edge, and the dynamics is described by the stochastic motion of an ensemble of points on such a membrane, i.e., pseudo-particles. For each single frame, the pipeline of analysis includes: first, a texture classification for separating the background from the cells and for identifying the wound edge; second, the computation of the coordinates of the ensemble of pseudo-particles, chosen to be uniformly distributed along the length of the wound edge. We show the results of this method applied to a glioma cell line (T98G) performing a wound healing scratch assay without external stimuli. We discuss the efficiency of the method to assess cell motility and possible applications to other experimental layouts, such as single cell motion. The pipeline is developed in the Python language and is available upon request.
Highlights
In this paper, we propose a pipeline for the segmentation and analysis of phase contrast images acquired in time-lapse in the wound healing scratch assay, to overcome some limitations of standard approaches due to the change in shape and density of the cells during migration.Cellular migration is a fundamental process for animal’s physiology during both the period of development and that of maturity
The trends of the N single particle tracking (SPT) obtained through the texture analysis for the experiment under study are compared with the ones obtained by using the professional tool ImageJ in
We present an original method to extract a 2D discrete representation of the wound edge in phase contrast images acquired in time-lapse by texture analysis, and we compare the results with the ones obtained by using the professional tool ImageJ and by Otsu thresholding
Summary
We propose a pipeline for the segmentation and analysis of phase contrast images acquired in time-lapse in the wound healing scratch assay, to overcome some limitations of standard approaches due to the change in shape and density of the cells during migration. Cellular migration is a fundamental process for animal’s physiology during both the period of development and that of maturity. Cells migrate to shape organs and tissues and, in the case of damage, regenerate them. Motility is a primary skill in cancer metastatic processes and in the immune responses [1,2]. The capability to migrate is a highly regulated process in which cells respond to external and internal mechanical, electrical, and chemical stimuli by complex physiological processes that promote, enhance, or suppress cell motility [3,4]. Cells can be induced to move in a particular direction by positive and negative guidance signals, while in the absence of external guidance, cells move randomly [5,6]
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