Cell Image Velocimetry (CIV): boosting the automated quantification of cell migration in wound healing assays

Abstract

Cell migration is commonly quantified by tracking the speed of the cell layer interface in wound healing assays. This quantification is often hampered by low signal to noise ratio, in particular when complex substrates are employed to emulate in vivo cell migration in geometrically complex environments. Moreover, information about the cell motion, readily available inside the migrating cell layers, is not usually harvested. We introduce Cell Image Velocimetry (CIV), a combination of cell layer segmentation and image velocimetry algorithms, to drastically enhance the quantification of cell migration by wound healing assays. The resulting software analyses the speed of the interface as well as the detailed velocity field inside the cell layers in an automated fashion. CIV is shown to be highly robust for images with low signal to noise ratio, low contrast and frame shifting and it is portable across various experimental settings. The modular design and parametrization of CIV is not restricted to wound healing assays and allows for the exploration and quantification of flow phenomena in any optical microscopy dataset. Here, we demonstrate the capabilities of CIV in wound healing assays over topographically engineered surfaces and quantify the relative merits of differently aligned gratings on cell migration.

Accelerated endothelial wound healing on microstructured substrates under flow

“…A modulation of the mechanical linkage between cells in a monolayer is essential for expedient wound healing by directed migration while preserving the sheet integrity [5,26]. Here, we demonstrated that flow-mediated wall shear stress and substrate topography cooperate to enable a dynamic control of cell-to-cell contact stability upon healing.…”

“…Quantitative analysis of wound healing was performed using the Cell Image Velocimetry (CIV) Matlab toolbox [26]. Reported values are mean values over the first 7.5h (30 frames) of the experiment, considering cells inside a region extending 250 µm into the cell layer.…”

An extended Cellular Potts Model analyzing a wound healing assay

“…In [90], a set of wound healing experiments of human umbilical vein endothelial cell (HUVEC) sheets seeded on bioengineered silicon substrates has been analyzed with Cell Image Velocimetry (CIV, a software able to combine an automated detection mechanism for tracing the migrating wound front with a flow detection tool for capturing transport processes inside the cell layer). In a complete agreement with the proposed CPM simulations, an alignment in the topography of the surface in the direction orthogonal to the wound edge has been observed to induce cell shape polarization and orientation and to lead to sustained cell migration, thereby allowing an optimal wound closure.…”

SEM++: A particle model of cellular growth, signaling and migration

“…The introduction of spatially varying SCE potentials, elliptically shaped potential functions or fixed bonds in-between SCEs could address these limitations. Ongoing work focuses on the coupling of particle model with experimental data [20,40,41] and the quantification of uncertainties in the force field following related work of our group on Molecular Dynamics [6].…”

“…It is therefore important for future works to carefully calibrate such migration models with experimental data. Data from wound healing assays [20,40] or the growth of blood vessels in the mouse retina [41] could be used to fit parameters for the models and to quantify their uncertainties [6].…”