How much information can be obtained from tracking the position of the leading edge in a scratch assay?
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.
Mathematical Sciences, Queensland University of Technology, Brisbane, Australia Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.
Stuart T
ST
Johnston
Mathematical Sciences, Queensland University of Technology, Brisbane, Australia Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia matthew.simpson@qut.edu.au.
Matthew J
MJ
Simpson
Mathematical Sciences, Queensland University of Technology, Brisbane, Australia Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.
D L Sean
DL
McElwain
D016475:3T3 Cells; D000818:Animals; D002449:Cell Aggregation; D002465:Cell Movement; D002478:Cells, Cultured; D003198:Computer Simulation; D003288:Contusions / Q000473:pathology* / Q000503:physiopathology*; D005347:Fibroblasts / Q000166:cytology* / Q000502:physiology*; D051379:Mice; D008954:Models, Biological; D015233:Models, Statistical; D012867:Skin / Q000293:injuries* / Q000473:pathology / Q000503:physiopathology; D014945:Wound Healing / Q000502:physiology*
cancer; cell motility; cell proliferation; edge detection; scratch assay