ArticleJournal of the Royal Society, Interface2014
How much information can be obtained from tracking the position of the leading edge in a scratch assay?
Article in Journal of the Royal Society, Interface, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 79 citations in OpenAlex.
- Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning for biological systems.PLoS computational biology · 2026Article
- A novel method for evaluating and visualizing scratch wound healing assays using level-set and image sector analysis.PNAS nexus · 2025Article
- Quantifying collective motion patterns in mesenchymal cell populations using topological data analysis and agent-based modeling.Mathematical biosciences · 2024Article
- AAA + ATPase Thorase inhibits mTOR signaling through the disassembly of the mTOR complex 1.Nature communications · 2022Article
- Mathematical Modeling Can Advance Wound Healing Research.Advances in wound care · 2021Review
- Learning differential equation models from stochastic agent-based model simulations.Journal of the Royal Society, Interface · 2021Article
- The Frequent Sampling of Wound Scratch Assay Reveals the "Opportunity" Window for Quantitative Evaluation of Cell Motility-Impeding Drugs.Frontiers in cell and developmental biology · 2021Article
- Predicting population extinction in lattice-based birth-death-movement models.Proceedings. Mathematical, physical, and engineering sciences · 2020Article
- Learning partial differential equations for biological transport models from noisy spatio-temporal data.Proceedings. Mathematical, physical, and engineering sciences · 2020Article
- In vitro cell migration quantification method for scratch assays.Journal of the Royal Society, Interface · 2019Article
- In Vitro Scratch Assay to Demonstrate Effects of Arsenic on Skin Cell Migration.Journal of visualized experiments : JoVE · 2019Article
- Characterisation of impaired wound healing in a preclinical model of induced diabetes using wide-field imaging and conventional immunohistochemistry assays.International wound journal · 2019Article
- Article
- Extended live-tracking and quantitative characterization of wound healing and cell migration with SiR-Hoechst.Experimental cell research · 2018Article
- Quantifying the efficacy of first aid treatments for burn injuries using mathematical modelling and in vivo porcine experiments.Scientific reports · 2017Article
- Co-operation, Competition and Crowding: A Discrete Framework Linking Allee Kinetics, Nonlinear Diffusion, Shocks and Sharp-Fronted Travelling Waves.Scientific reports · 2017Article
- Using approximate Bayesian computation to quantify cell-cell adhesion parameters in a cell migratory process.NPJ systems biology and applications · 2017Article
- Article
- Bursts of activity in collective cell migration.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- A simple non-perturbing cell migration assay insensitive to proliferation effects.Scientific reports · 2016Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.