How much information can be obtained from tracking the position of the leading edge in a scratch assay?

Abstract

Moving cell fronts are an essential feature of wound healing, development and disease. The rate at which a cell front moves is driven, in part, by the cell motility, quantified in terms of the cell diffusivity D, and the cell proliferation rate λ. Scratch assays are a commonly reported procedure used to investigate the motion of cell fronts where an initial cell monolayer is scratched, and the motion of the front is monitored over a short period of time, often less than 24 h. The simplest way of quantifying a scratch assay is to monitor the progression of the leading edge. Use of leading edge data is very convenient because, unlike other methods, it is non-destructive and does not require labelling, tracking or counting individual cells among the population. In this work, we study short-time leading edge data in a scratch assay using a discrete mathematical model and automated image analysis with the aim of investigating whether such data allow us to reliably identify D and λ. Using a naive calibration approach where we simply scan the relevant region of the (D, λ) parameter space, we show that there are many choices of D and λ for which our model produces indistinguishable short-time leading edge data. Therefore, without due care, it is impossible to estimate D and λ from this kind of data. To address this, we present a modified approach accounting for the fact that cell motility occurs over a much shorter time scale than proliferation. Using this information, we divide the duration of the experiment into two periods, and we estimate D using data from the first period, whereas we estimate λ using data from the second period. We confirm the accuracy of our approach using in silico data and a new set of in vitro data, which shows that our method recovers estimates of D and λ that are consistent with previously reported values except that that our approach is fast, inexpensive, non-destructive and avoids the need for cell labelling and cell counting.

A new and accurate continuum description of moving fronts

“…Processes that involve moving fronts are common in cell biology and ecology [1,2,4,5,6,7,9,10,11,12], and lattice-based random walks are widely employed to describe these processes [6,16,18,19,20,21,45]. Due to the stochastic nature of random walks, it can be computationally intractable to perform sufficiently many realisations of a random .…”

“…Note that scratch assays are two-dimensional processes, as observed in the experimental images of a scratch assay for a 3T3 fibroblast population in Figures 6(c)-(d), and the corresponding schematics for this experiment in Figures 6(e)-(f). Full experimental details are given in [6].…”

“…Alternatively, the C&G description presented here could be calibrated to experimental data from the cell biology literature. For example, lattice-based random walks have been calibrated to in vitro cell biology experiments to obtain estimates of cell diffusivity and cell proliferation rates [6,21]. However, the calibration of stochastic models to experimental data is computationally expensive [6,48].…”

“…Lattice-based random walk models that include crowding, birth, death, movement and agent-agent adhesion are commonly used to describe processes that involve moving fronts [6,16,17,18,19,20,21]. For example, these random walk models have been used to interpret in vitro cell biology experiments by considering the position of the leading edge of the cell front or the cell density profile [6,19,21].…”

“…For example, these random walk models have been used to interpret in vitro cell biology experiments by considering the position of the leading edge of the cell front or the cell density profile [6,19,21]. Illien et al [18] consider random walks in the context of single-file diffusion models of active transport, that is, transport that requires energy due to an opposing force.…”