title: Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOM™ assay data using the Fisher-Kolmogorov model.
abstract: BACKGROUND
  Standard methods for quantifying IncuCyte ZOOM(™) assays involve measurements that quantify how rapidly the initially-vacant area becomes re-colonised with cells as a function of time. Unfortunately, these measurements give no insight into the details of the cellular-level mechanisms acting to close the initially-vacant area. We provide an alternative method enabling us to quantify the role of cell motility and cell proliferation separately. To achieve this we calibrate standard data available from IncuCyte ZOOM(™) images to the solution of the Fisher-Kolmogorov model.
  
  
  RESULTS
  The Fisher-Kolmogorov model is a reaction-diffusion equation that has been used to describe collective cell spreading driven by cell migration, characterised by a cell diffusivity, D, and carrying capacity limited proliferation with proliferation rate, λ, and carrying capacity density, K. By analysing temporal changes in cell density in several subregions located well-behind the initial position of the leading edge we estimate λ and K. Given these estimates, we then apply automatic leading edge detection algorithms to the images produced by the IncuCyte ZOOM(™) assay and match this data with a numerical solution of the Fisher-Kolmogorov equation to provide an estimate of D. We demonstrate this method by applying it to interpret a suite of IncuCyte ZOOM(™) assays using PC-3 prostate cancer cells and obtain estimates of D, λ and K. Comparing estimates of D, λ and K for a control assay with estimates of D, λ and K for assays where epidermal growth factor (EGF) is applied in varying concentrations confirms that EGF enhances the rate of scratch closure and that this stimulation is driven by an increase in D and λ, whereas K is relatively unaffected by EGF.
  
  
  CONCLUSIONS
  Our approach for estimating D, λ and K from an IncuCyte ZOOM(™) assay provides more detail about cellular-level behaviour than standard methods for analysing these assays. In particular, our approach can be used to quantify the balance of cell migration and cell proliferation and, as we demonstrate, allow us to quantify how the addition of growth factors affects these processes individually.
authors.affiliation: School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, 4001, Australia. s17.johnston@qut.edu.au.
authors.forename: Stuart T
authors.initials: ST
authors.lastname: Johnston
authors.affiliation: Ghrelin Research Group, Translational Research Institute, Institute of Health and Biomedical Innovation and APCRC-Q, QUT, 37 Kent St, Woolloongabba, 4102, Australia. esha.shah@hdr.qut.edu.au.
authors.forename: Esha T
authors.initials: ET
authors.lastname: Shah
authors.affiliation: Ghrelin Research Group, Translational Research Institute, Institute of Health and Biomedical Innovation and APCRC-Q, QUT, 37 Kent St, Woolloongabba, 4102, Australia. l.chopin@qut.edu.au.
authors.forename: Lisa K
authors.initials: LK
authors.lastname: Chopin
authors.affiliation: School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, 4001, Australia. s.mcelwain@qut.edu.au.
authors.forename: D L
authors.initials: DL
authors.lastname: Sean McElwain
authors.affiliation: School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, 4001, Australia. matthew.simpson@qut.edu.au.|Ghrelin Research Group, Translational Research Institute, Institute of Health and Biomedical Innovation and APCRC-Q, QUT, 37 Kent St, Woolloongabba, 4102, Australia. matthew.simpson@qut.edu.au.
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Simpson
mesh_terms: D045744:Cell Line, Tumor; D002465:Cell Movement; D049109:Cell Proliferation; D004058:Diffusion; D006801:Humans; D008954:Models, Biological