Quantifying the roles of random motility and directed motility using advection-diffusion theory for a 3T3 fibroblast cell migration assay stimulated with an electric field

BackgroundDirected cell migration can be driven by a range of external stimuli, such as spatial gradients of: chemical signals (chemotaxis); adhesion sites (haptotaxis); or temperature (thermotaxis). Continuum models of cell migration typically include a diffusion term to capture the undirected component of cell motility and an advection term to capture the directed component of cell motility. However, there is no consensus in the literature about the form that the advection term takes. Some theoretical studies suggest that the advection term ought to include receptor saturation effects. However, others adopt a much simpler constant coefficient. One of the limitations of including receptor saturation effects is that it introduces several additional unknown parameters into the model. Therefore, a relevant research question is to investigate whether directed cell migration is best described by a simple constant tactic coefficient or a more complicated model incorporating saturation effects.ResultsWe study directed cell migration using an experimental device in which the directed component of the cell motility is driven by a spatial gradient of electric potential, which is known as electrotaxis. The electric field (EF) is proportional to the spatial gradient of the electric potential. The spatial variation of electric potential across the experimental device varies in such a way that there are several subregions on the device in which the EF takes on different values that are approximately constant within those subregions. We use cell trajectory data to quantify the motion of 3T3 fibroblast cells at different locations on the device to examine how different values of the EF influences cell motility. The undirected (random) motility of the cells is quantified in terms of the cell diffusivity, D, and the directed motility is quantified in terms of a cell drift velocity, v. Estimates D and v are obtained under a range of four different EF conditions, which correspond to normal physiological conditions. Our results suggest that there is no anisotropy in D, and that D appears to be approximately independent of the EF and the electric potential. The drift velocity increases approximately linearly with the EF, suggesting that the simplest linear advection term, with no additional saturation parameters, provides a good explanation of these physiologically relevant data.ConclusionsWe find that the simplest linear advection term in a continuum model of directed cell motility is sufficient to describe a range of different electrotaxis experiments for 3T3 fibroblast cells subject to normal physiological values of the electric field. This is useful information because alternative models that include saturation effects involve additional parameters that need to be estimated before a partial differential equation model can be applied to interpret or predict a cell migration experiment.

[1]  Matthew J. Simpson,et al.  Multi-species simple exclusion processes , 2009 .

[2]  Min Zhao,et al.  Electrical fields in wound healing-An overriding signal that directs cell migration. , 2009, Seminars in cell & developmental biology.

[3]  Kevin J Painter,et al.  Modelling the movement of interacting cell populations. , 2003, Journal of theoretical biology.

[4]  I. Schwab,et al.  DC electric fields induce rapid directional migration in cultured human corneal epithelial cells. , 2000, Experimental eye research.

[5]  Francis Lin,et al.  A receptor-electromigration-based model for cellular electrotactic sensing and migration. , 2011, Biochemical and biophysical research communications.

[6]  Matthew J Simpson,et al.  Are in vitro estimates of cell diffusivity and cell proliferation rate sensitive to assay geometry? , 2014, Journal of theoretical biology.

[7]  M. Wechselberger,et al.  Existence of travelling wave solutions for a model of tumour invasion , 2013 .

[8]  R T Tranquillo,et al.  Measurement of the chemotaxis coefficient for human neutrophils in the under-agarose migration assay. , 1988, Cell motility and the cytoskeleton.

[9]  Wang Jin,et al.  Reproducibility of scratch assays is affected by the initial degree of confluence: Experiments, modelling and model selection. , 2016, Journal of theoretical biology.

[10]  D A Lauffenburger,et al.  Analysis of the roles of microvessel endothelial cell random motility and chemotaxis in angiogenesis. , 1991, Journal of theoretical biology.

[11]  Ji-Yen Cheng,et al.  Simultaneous chemical and electrical stimulation on lung cancer cells using a multichannel-dual-electric-field chip , 2014 .

[12]  Cheng-Wey Wei,et al.  Direct-write laser micromachining and universal surface modification of PMMA for device development , 2004 .

[13]  Matthew J Simpson,et al.  Quantifying the roles of cell motility and cell proliferation in a circular barrier assay , 2013, Journal of The Royal Society Interface.

[14]  Stuart K. Williams,et al.  Migration of individual microvessel endothelial cells: stochastic model and parameter measurement. , 1991, Journal of cell science.

[15]  Matthew J Simpson,et al.  Experimental and Modelling Investigation of Monolayer Development with Clustering , 2013, Bulletin of mathematical biology.

[16]  H. Green,et al.  QUANTITATIVE STUDIES OF THE GROWTH OF MOUSE EMBRYO CELLS IN CULTURE AND THEIR DEVELOPMENT INTO ESTABLISHED LINES , 1963, The Journal of cell biology.

[17]  Graeme J. Pettet,et al.  Folds, canards and shocks in advection–reaction–diffusion models , 2010 .

[18]  Maria E. Mycielska,et al.  Cellular mechanisms of direct-current electric field effects: galvanotaxis and metastatic disease , 2004, Journal of Cell Science.

[19]  Kai-Yin Lo,et al.  Correlation between cell migration and reactive oxygen species under electric field stimulation. , 2015, Biomicrofluidics.

[20]  K R Robinson,et al.  The responses of cells to electrical fields: a review , 1985, The Journal of cell biology.

[21]  Philip K Maini,et al.  Traveling wave model to interpret a wound-healing cell migration assay for human peritoneal mesothelial cells. , 2004, Tissue engineering.

[22]  D. L. Sean McElwain,et al.  Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOM™ assay data using the Fisher-Kolmogorov model , 2015, BMC Systems Biology.

[23]  L. Segel,et al.  Model for chemotaxis. , 1971, Journal of theoretical biology.

[24]  H. Byrne,et al.  Biphasic behaviour in malignant invasion. , 2006, Mathematical medicine and biology : a journal of the IMA.

[25]  Graeme J. Pettet,et al.  Tactically-driven nonmonotone travelling waves , 2008 .

[26]  J Norbury,et al.  Lotka-Volterra equations with chemotaxis: walls, barriers and travelling waves. , 2000, IMA journal of mathematics applied in medicine and biology.

[27]  Graeme J. Pettet,et al.  Existence of Traveling Wave Solutions for a Model of Tumor Invasion , 2014, SIAM J. Appl. Dyn. Syst..

[28]  R T Tranquillo,et al.  A stochastic model for leukocyte random motility and chemotaxis based on receptor binding fluctuations , 1988, The Journal of cell biology.

[29]  Kerry A Landman,et al.  Multi-scale modeling of a wound-healing cell migration assay. , 2007, Journal of theoretical biology.

[30]  Evgeniy Khain,et al.  Modeling chemotaxis of adhesive cells: stochastic lattice approach and continuum description , 2014 .

[31]  Richard Nuccitelli,et al.  A role for endogenous electric fields in wound healing. , 2003, Current topics in developmental biology.

[32]  R. M. Ford,et al.  Measurement of bacterial random motility and chemotaxis coefficients: II. Application of single‐cell‐based mathematical model , 1991, Biotechnology and bioengineering.

[33]  M. Yen,et al.  A transparent cell-culture microchamber with a variably controlled concentration gradient generator and flow field rectifier. , 2008, Biomicrofluidics.

[34]  Jonathan A. Sherratt,et al.  Models of epidermal wound healing , 1990, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[35]  R. M. Ford,et al.  Measurement of bacterial random motility and chemotaxis coefficients: I. Stopped‐flow diffusion chamber assay , 1991, Biotechnology and bioengineering.

[36]  D. Garzón-Alvarado,et al.  Mathematical model of electrotaxis in osteoblastic cells. , 2012, Bioelectrochemistry.

[37]  Douglas A Lauffenburger,et al.  Quantitative analysis of gradient sensing: towards building predictive models of chemotaxis in cancer. , 2012, Current opinion in cell biology.

[38]  Meng-Hua Yen,et al.  Crack-free direct-writing on glass using a low-power UV laser in the manufacture of a microfluidic chip , 2005 .

[39]  V. S. Vaidhyanathan,et al.  Transport phenomena , 2005, Experientia.

[40]  Matthew J. Simpson,et al.  Lattice-free models of directed cell motility , 2016 .

[41]  Matthew J Simpson,et al.  Looking inside an invasion wave of cells using continuum models: proliferation is the key. , 2006, Journal of theoretical biology.

[42]  James D. Murray Mathematical Biology: I. An Introduction , 2007 .

[43]  J. Sherratt,et al.  A two parameter family of travelling waves with a singular barrier arising from the modelling of extracellular matrix mediated cellular invasion , 1999 .

[44]  E. T. Gawlinski,et al.  A reaction-diffusion model of cancer invasion. , 1996, Cancer research.

[45]  Edward A. Codling,et al.  Random walk models in biology , 2008, Journal of The Royal Society Interface.

[46]  Barry D. Hughes,et al.  Modelling Directional Guidance and Motility Regulation in Cell Migration , 2006, Bulletin of mathematical biology.