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A Semi-Automatic Method for Image Analysis of Edge Dynamics in Living Cells

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Abstract

Spatial asymmetry of actin edge ruffling contributes to the process of cell polarization and directional migration, but mechanisms by which external cues control actin polymerization near cell edges remain unclear. We designed a quantitative image analysis strategy to measure the spatiotemporal distribution of actin edge ruffling. Time-lapse images of endothelial cells (ECs) expressing mRFP-actin were segmented using an active contour method. In intensity line profiles oriented normal to the cell edge, peak detection identified the angular distribution of polymerized actin within 1 μm of the cell edge, which was localized to lamellipodia and edge ruffles. Edge features associated with filopodia and peripheral stress fibers were removed. Circular statistical analysis enabled detection of cell polarity, indicated by a unimodal distribution of edge ruffles. To demonstrate the approach, we detected a rapid, nondirectional increase in edge ruffling in serum-stimulated ECs and a change in constitutive ruffling orientation in quiescent, nonpolarized ECs. Error analysis using simulated test images demonstrate robustness of the method to variations in image noise levels, edge ruffle arc length, and edge intensity gradient. These quantitative measurements of edge ruffling dynamics enable investigation at the cellular length scale of the underlying molecular mechanisms regulating actin assembly and cell polarization.

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Authors and Affiliations

  1. Department of Biomedical Engineering, University of Virginia, P.O. Box 800759, Charlottesville, VA, 22908-0759, USA

    Lawrence Huang & Brian P. Helmke

  2. Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, 22908, USA

    Lawrence Huang & Brian P. Helmke

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  1. Lawrence Huang
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  2. Brian P. Helmke
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Correspondence to Brian P. Helmke.

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Associate Editor Yingxiao Peter Wang and Peter Butler oversaw the review of this article.

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Huang, L., Helmke, B.P. A Semi-Automatic Method for Image Analysis of Edge Dynamics in Living Cells. Cel. Mol. Bioeng. 4, 205–219 (2011). https://doi.org/10.1007/s12195-010-0141-z

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  • DOI: https://doi.org/10.1007/s12195-010-0141-z

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