Leading edge maintenance in migrating cells is an emergent property of branched actin network growth
- Biophysics Program, Stanford University School of Medicine, United States
- Department of Biology, Howard Hughes Medical Institute, University of Washington, United States
Figures
Figure 1 with 6 supplements
High-speed, high-resolution imaging reveals fine-scale fluctuations in leading edge shape.
(a–c) Example of leading edge fluctuations extracted from a representative migrating HL-60 cell. (a) Phase contrast microscopy image from the first frame of a movie, overlaid with segmented leading …
-
Figure 1—source data 1
Source data corresponding to plots in Figure 1.
See Readme for a description of the contents, and the locations of the corresponding plots in the figure.
- https://cdn.elifesciences.org/articles/74389/elife-74389-fig1-data1-v2.zip
Figure 1—figure supplement 1
Overview of cell segmentation analysis pipeline.
Example of image processing and leading edge segmentation for a single cell. (a) Example raw image of a migrating cell. (b) Image of the same cell after aligning the image in the direction of motion …
Figure 1—figure supplement 2
Overview of analysis pipeline to extract fine-scale leading edge shape features.
An example of the fine-scale feature selection process for the same cell shown in Figure 1—figure supplement 1. (a,c) Raw leading edge shape (red line), the Loess-smoothed shape (blue line), and the …
Figure 1—figure supplement 3
Control for analysis I.
Validation of spatial Fourier mode autocorrelation analysis using analytical theory for simulated membrane dynamics. (a–f) Simulated membrane control exhibiting exponentially decaying fluctuations. …
Figure 1—figure supplement 4
Control for analysis II.
Autocorrelation analysis of HL-60 leading edges shows exponential decay to a noise window at long times. (a–c) Autocorrelation amplitude (complex magnitude) of the spatial Fourier transform plotted …
Figure 1—figure supplement 5
Control for analysis III.
No new features emerge upon a ~50% increase in span used for background subtraction. (a–b) Autocorrelation analysis results on HL-60 cell leading edges as shown in Figure 1, using either (a) a 7 μm …
Figure 1—figure supplement 6
Leading edge fluctuation behavior is reproduced in fish epidermal keratocytes.
(a–c) Example of leading edge fluctuations extracted from a representative migrating fish epidermal keratocyte, plotted as in Figure 1a–c. Note differences in scale for time, x-position, and …
Figure 2 with 3 supplements
Minimal model of branched actin growth recapitulates leading edge stability and shape fluctuation relaxation.
(a–b) Model schematic. Black lines, membrane; green circles, actin; purple flowers, Arp2/3 complex; blue crescents, capping protein. Rates: kon, polymerization; koff, depolymerization; kbranch, …
-
Figure 2—source data 1
Source data corresponding to plots in Figure 2.
See Readme for a description of the contents, and the locations of the corresponding plots in the figure.
- https://cdn.elifesciences.org/articles/74389/elife-74389-fig2-data1-v2.zip
Figure 2—figure supplement 1
Simulations are performed at sufficient temporal discretization.
A summary of leading edge fluctuations and actin network properties for simulations using the standard timestep (blue), a timestep three-fold larger (green), and a timestep three times smaller …
Figure 2—figure supplement 2
Simulations are performed at sufficient spatial discretization.
(a–l) A summary of leading edge fluctuations and actin network properties, plotted as in Figure 2—figure supplement 1, for simulations using the standard membrane segment length (blue), a segment …
Figure 2—figure supplement 3
Simulated leading edge behavior is not affected by leading edge length.
(a–l) A summary of leading edge fluctuations and actin network properties, plotted as in Figure 2—figure supplement 1, for simulations using the standard leading edge length (blue), a length two …
Figure 3
Minimal model correctly predicts response of HL-60 cells to drug treatment.
Predicted and experimentally-measured response of the autocorrelation decay fit parameters to drug treatment with Latrunculin B, plotted as in Figure 1g–h. (a–b) Predicted response to a reduction in …
-
Figure 3—source data 1
Source data corresponding to plots in Figure 3.
See Readme for a description of the contents, and the locations of the corresponding plots in the figure.
- https://cdn.elifesciences.org/articles/74389/elife-74389-fig3-data1-v2.zip
Figure 4
Simulated lamellipodial stability is governed by leading edge geometry.
(a–e) Comparison of leading edge properties with and without the coupling of the membrane segments by tension and bending rigidity (no coupling: Fspring = 0 in Figure 2a), plotted as in Figure 2d–h. …
-
Figure 4—source data 1
Source data corresponding to plots in Figure 4.
See Readme for a description of the contents, and the locations of the corresponding plots in the figure.
- https://cdn.elifesciences.org/articles/74389/elife-74389-fig4-data1-v2.zip
Figure 5
The genetically-encoded Arp2/3-mediated branching angle is optimal for suppressing leading edge fluctuations.
(a–c) Time course (a) and steady state distribution (b–c) of the filament angle (θf) for simulations with various branching angle standard deviations (Δθbr), (a–b) and means (θbr), (c). Dashed lines …
-
Figure 5—source data 1
Source data corresponding to plots in Figure 5.
See Readme for a description of the contents, and the locations of the corresponding plots in the figure.
- https://cdn.elifesciences.org/articles/74389/elife-74389-fig5-data1-v2.zip
Videos
Video 1
Segmentation overlaid onto migrating HL-60 cell.
Time lapse video representation of segmentation results shown in Figure 1a.
Video 2
Example fish epidermal keratocyte.
Time lapse video corresponding to the data shown in Figure 1—figure supplement 6a-e.
Video 3
Example simulation.
Time lapse video representation of simulation results shown in Figure 2c.
Video 4
Example HL-60 cell treated with 30 nM latrunculin B.
Video 5
Example HL-60 cell treated with 0.1% DMSO vehicle control.
Video 6
Example HL-60 cell treated with 100 μM CK-666.
Tables
Table 1
Actin network growth parameters.
Parameters listed are the default used for the simulations.
| Notation | Meaning | Value | Source |
|---|---|---|---|
| M | Free monomer concentration | 15 µM | Cooper, 1991; Marchand et al., 1995 |
| kon | Polymerization rate | 11∙10–3 monomers ms–1 µM–1 | Pollard, 1986 |
| koff | Depolymerization rate | 10–3 monomers ms–1 | Pollard, 1986 |
| kcap | Capping rate | 3∙10–3 ms–1 | ~3∙10–3 µM–1 ms-1 Schafer et al., 1996at 1 µM capping protein Pollard et al., 2000 |
| kbranch | Branching rate | 4.5∙10–5 branches ms–1 µM–1 nm–1 | 50 nm branch spacing Svitkina et al., 1997; Svitkina and Borisy, 1999;Branch rate approximated such that elongation rate / branch rate = 50 nm; kbranch = (kon∙M∙lm)/(50 nm∙M∙ybranch) |
| ybranch | Branching window length | 15 nm | ~3–5 protein diameters away from the membrane |
| θbranch | Branching angle | 70 ± 10° | Mullins et al., 1998; Volkmann et al., 2001; Rouiller et al., 2008; Blanchoin et al., 2000; Cai et al., 2008; Svitkina and Borisy, 1999 |
| lp | Actin filament persistence length | 1 µm | Käs et al., 1996 |
| lm | Actin monomer length | 2.7 nm | Pollard, 1986 |
Table 2
Physical parameters.
Parameters listed are the default used for the simulations.
| Notation | Meaning | Value | Source |
|---|---|---|---|
| kB | Boltzmann constant | 0.0183 pN nm K–1 | – |
| T | Temperature | 310.15 K | – |
| σ | Membrane tension | 0.03 pN nm–1 | Lieber et al., 2013 |
| κ | Membrane bending modulus | 140 pN nm | Lieber et al., 2013 |
| ηw | Viscosity of water at 37 °C | 7∙10–7 pN ns nm–2 | – |
| η | Effective viscosity at the leading edge | 3000 ηw | ~ effective viscosity of micron-scale beads in cytoplasm Wirtz, 2009 |
| L | Leading edge length | 20 µm | This work |
| h | Leading edge height | 200 nm | Abraham et al., 1999; Laurent et al., 2005; Urban et al., 2010 |
| Δx | Membrane segment length | 100 nm | – |
| N | Number of membrane segments | 200 | – |
