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Scratch assay microscopy: A reaction-diffusion equation approach for common instruments and data.

Alessio Gnerucci1, Paola Faraoni2, Elettra Sereni2

  • 1Department of Physics and Astronomy, University of Florence, Via Sansone, 1, 50019, Sesto Fiorentino, Florence, Italy.

Mathematical Biosciences
|October 4, 2020
PubMed
Summary

This study presents a novel model to distinguish cell migration and proliferation using scratch assays. The model successfully disentangles these processes, offering improved insights into cell behavior.

Keywords:
Cell migrationCell proliferationMicroscopyReaction–diffusion equationScratch assay

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Area of Science:

  • Cell Biology
  • Biophysics
  • Mathematical Modeling

Background:

  • Scratch assays are standard in vitro methods for studying cell migration and proliferation.
  • Existing models often fail to differentiate between these two cellular processes.

Purpose of the Study:

  • To adapt a reaction-diffusion model for analyzing scratch assay data from common microscopy.
  • To develop a method capable of distinguishing cell migration and proliferation.
  • To evaluate the model's robustness and reproducibility.

Main Methods:

  • Adapted a reaction-diffusion model for use with standard microscopy data.
  • Developed an optimized image analysis pipeline.
  • Employed numerical least-squares fitting to estimate proliferation (l) and diffusion (D) coefficients.
  • Tested the model on NIH3T3 cell scratch assays with varying fetal bovine serum concentrations.

Main Results:

  • The model successfully disentangled cell proliferation and migration coefficients (l and D), despite an expected l-D anticorrelation.
  • Identified 7.5% serum concentration as the model's sensitivity limit.
  • Demonstrated intra-experiment reproducibility for l and D variations consistent with typical fit uncertainties.

Conclusions:

  • The adapted reaction-diffusion model effectively distinguishes cell migration from proliferation in scratch assays.
  • The model provides a valuable tool bridging simpler and complex modeling approaches.
  • Further studies will focus on model testing and robustness evaluation.