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Sensitivity of Edge Detection Methods for Quantifying Cell Migration Assays

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  • Katrina K Treloar
  • Matthew J Simpson

Abstract

Quantitative imaging methods to analyze cell migration assays are not standardized. Here we present a suite of two-dimensional barrier assays describing the collective spreading of an initially-confined population of 3T3 fibroblast cells. To quantify the motility rate we apply two different automatic image detection methods to locate the position of the leading edge of the spreading population after , and hours. These results are compared with a manual edge detection method where we systematically vary the detection threshold. Our results indicate that the observed spreading rates are very sensitive to the choice of image analysis tools and we show that a standard measure of cell migration can vary by as much as 25% for the same experimental images depending on the details of the image analysis tools. Our results imply that it is very difficult, if not impossible, to meaningfully compare previously published measures of cell migration since previous results have been obtained using different image analysis techniques and the details of these techniques are not always reported. Using a mathematical model, we provide a physical interpretation of our edge detection results. The physical interpretation is important since edge detection algorithms alone do not specify any physical measure, or physical definition, of the leading edge of the spreading population. Our modeling indicates that variations in the image threshold parameter correspond to a consistent variation in the local cell density. This means that varying the threshold parameter is equivalent to varying the location of the leading edge in the range of approximately 1–5% of the maximum cell density.

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  • Katrina K Treloar & Matthew J Simpson, 2013. "Sensitivity of Edge Detection Methods for Quantifying Cell Migration Assays," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-10, June.
    • Handle: RePEc:plo:pone00:0067389
    DOI: 10.1371/journal.pone.0067389
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    References listed on IDEAS

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    1. Assaf Zaritsky & Sari Natan & Judith Horev & Inbal Hecht & Lior Wolf & Eshel Ben-Jacob & Ilan Tsarfaty, 2011. "Cell Motility Dynamics: A Novel Segmentation Algorithm to Quantify Multi-Cellular Bright Field Microscopy Images," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-10, November.
    2. Simpson, Matthew J. & Landman, Kerry A. & Clement, T.Prabhakar, 2005. "Assessment of a non-traditional operator split algorithm for simulation of reactive transport," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 70(1), pages 44-60.
    3. Victor Nizet & Takaaki Ohtake & Xavier Lauth & Janet Trowbridge & Jennifer Rudisill & Robert A. Dorschner & Vasumati Pestonjamasp & Joseph Piraino & Kenneth Huttner & Richard L. Gallo, 2001. "Innate antimicrobial peptide protects the skin from invasive bacterial infection," Nature, Nature, vol. 414(6862), pages 454-457, November.
    4. Simpson, Matthew J. & Landman, Kerry A. & Hughes, Barry D., 2010. "Cell invasion with proliferation mechanisms motivated by time-lapse data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3779-3790.
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    Cited by:

    1. Wang Jin & Catherine J Penington & Scott W McCue & Matthew J Simpson, 2017. "A computational modelling framework to quantify the effects of passaging cell lines," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-16, July.
    2. Matthew J Simpson & Parvathi Haridas & D L Sean McElwain, 2014. "Do Pioneer Cells Exist?," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.

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