Cargando…

Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOM™ assay data using the Fisher-Kolmogorov model

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 actin...

Descripción completa

Detalles Bibliográficos
Autores principales: Johnston, Stuart T., Shah, Esha T., Chopin, Lisa K., Sean McElwain, D. L., Simpson, Matthew J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4506581/
https://www.ncbi.nlm.nih.gov/pubmed/26188761
http://dx.doi.org/10.1186/s12918-015-0182-y
_version_ 1782381713279680512
author Johnston, Stuart T.
Shah, Esha T.
Chopin, Lisa K.
Sean McElwain, D. L.
Simpson, Matthew J.
author_facet Johnston, Stuart T.
Shah, Esha T.
Chopin, Lisa K.
Sean McElwain, D. L.
Simpson, Matthew J.
author_sort Johnston, Stuart T.
collection PubMed
description 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.
format Online
Article
Text
id pubmed-4506581
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-45065812015-07-19 Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOM™ assay data using the Fisher-Kolmogorov model Johnston, Stuart T. Shah, Esha T. Chopin, Lisa K. Sean McElwain, D. L. Simpson, Matthew J. BMC Syst Biol Methodology Article 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. BioMed Central 2015-07-19 /pmc/articles/PMC4506581/ /pubmed/26188761 http://dx.doi.org/10.1186/s12918-015-0182-y Text en © Johnston et al. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Johnston, Stuart T.
Shah, Esha T.
Chopin, Lisa K.
Sean McElwain, D. L.
Simpson, Matthew J.
Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOM™ assay data using the Fisher-Kolmogorov model
title Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOM™ assay data using the Fisher-Kolmogorov model
title_full Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOM™ assay data using the Fisher-Kolmogorov model
title_fullStr Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOM™ assay data using the Fisher-Kolmogorov model
title_full_unstemmed Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOM™ assay data using the Fisher-Kolmogorov model
title_short Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOM™ assay data using the Fisher-Kolmogorov model
title_sort estimating cell diffusivity and cell proliferation rate by interpreting incucyte zoom™ assay data using the fisher-kolmogorov model
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4506581/
https://www.ncbi.nlm.nih.gov/pubmed/26188761
http://dx.doi.org/10.1186/s12918-015-0182-y
work_keys_str_mv AT johnstonstuartt estimatingcelldiffusivityandcellproliferationratebyinterpretingincucytezoomassaydatausingthefisherkolmogorovmodel
AT shaheshat estimatingcelldiffusivityandcellproliferationratebyinterpretingincucytezoomassaydatausingthefisherkolmogorovmodel
AT chopinlisak estimatingcelldiffusivityandcellproliferationratebyinterpretingincucytezoomassaydatausingthefisherkolmogorovmodel
AT seanmcelwaindl estimatingcelldiffusivityandcellproliferationratebyinterpretingincucytezoomassaydatausingthefisherkolmogorovmodel
AT simpsonmatthewj estimatingcelldiffusivityandcellproliferationratebyinterpretingincucytezoomassaydatausingthefisherkolmogorovmodel