Cortical waves mediate the cellular response to electric fields

  1. Qixin Yang
  2. Yuchuan Miao
  3. Leonard J Campanello
  4. Matt J Hourwitz
  5. Bedri Abubaker-Sharif
  6. Abby L Bull
  7. Peter N Devreotes
  8. John T Fourkas
  9. Wolfgang Losert  Is a corresponding author
  1. Department of Physics, University of Maryland, United States
  2. Institute for Physical Science and Technology, University of Maryland, United States
  3. Department of Cell Biology, Johns Hopkins University, United States
  4. Department of Chemistry & Biochemistry, University of Maryland, United States

Abstract

Electrotaxis, the directional migration of cells in a constant electric field, is important in regeneration, development, and wound healing. Electrotaxis has a slower response and a smaller dynamic range than guidance by other cues, suggesting that the mechanism of electrotaxis shares both similarities and differences with chemical-gradient-sensing pathways. We examine a mechanism centered on the excitable system consisting of cortical waves of biochemical signals coupled to cytoskeletal reorganization, which has been implicated in random cell motility. We use electro-fused giant Dictyostelium discoideum cells to decouple waves from cell motion and employ nanotopographic surfaces to limit wave dimensions and lifetimes. We demonstrate that wave propagation in these cells is guided by electric fields. The wave area and lifetime gradually increase in the first 10 min after an electric field is turned on, leading to more abundant and wider protrusions in the cell region nearest the cathode. The wave directions display ‘U-turn’ behavior upon field reversal, and this switch occurs more quickly on nanotopography. Our results suggest that electric fields guide cells by controlling waves of signal transduction and cytoskeletal activity, which underlie cellular protrusions. Whereas surface receptor occupancy triggers both rapid activation and slower polarization of signaling pathways, electric fields appear to act primarily on polarization, explaining why cells respond to electric fields more slowly than to other guidance cues.

Editor's evaluation

The authors combine a series of clever biological approaches to fuse small Dictyostelium cells into "giant cells" that greatly facilitate the spatial resolution of actin wave dynamics without or with electrical stimulation when grown on smooth or nano-textured surfaces. This compelling experimental system opens possibilities for the field to analyze the molecular subtleties involved in these cytoskeletal reorganizations.

https://doi.org/10.7554/eLife.73198.sa0

Introduction

Electrotaxis, which refers to the directed migration of cells under the guidance of an electric field (EF), is important in wound healing, development, and regeneration (Cortese et al., 2014; Lin et al., 2008; Zhao et al., 2006). EFs have been shown to cause several key signaling molecules to be distributed asymmetrically across cells (Sato et al., 2009; Zhao et al., 2002; Zhao et al., 2006), setting up cell polarity. The one-order-of-magnitude range of EF strengths sensed by cells (Zhao et al., 2002) is considerably smaller than the four-orders-of-magnitude concentration sensitivity in chemotaxis (Harvath, 1991). Furthermore, whereas cells respond to chemical guidance cues on a time scale of seconds and develop polarity over several minutes, the response to an EF can take up to 10 min or more after the EF is turned on Wang et al., 2014; Zhao et al., 2006. These differences raise the possibility that the rapid gradient sensing mechanisms do not serve as primary mediators of EF sensing by cells. In this study, we examine whether, after turning on an EF, the gradual polarization of the excitable biochemical networks that organize actin polymerization comprises a slow-acting mediator of the cellular response to the EF.

Actin polymerization, coordinated with its associated signaling molecules, self-organizes into microscale spatial regions that travel as waves across plasma membranes. These waves drive various cell behaviors, such as migration and division (Bhattacharya et al., 2019; Bretschneider et al., 2009; Flemming et al., 2020; Gerhardt et al., 2014; Gerisch, 2010). The wave system can be described as a coupled signal-transduction excitable network – cytoskeletal excitable network (STEN-CEN) (Devreotes et al., 2017; Miao et al., 2019). STEN-CEN has the characteristics of an excitable system, including exhibiting an activation threshold for wave initiation and experiencing refractory periods. It has been shown that the STEN-CEN wave properties dictate protrusion properties (Miao et al., 2019). Tuning the activity levels of key components in STEN-CEN changes wave patterns, which leads to the transition of protrusion profiles. An activator/inhibitor, reaction/diffusion system model successfully recapitulates the experimental results (Bhattacharya et al., 2020; Bhattacharya and Iglesias, 2019). For simplicity, here we will refer to STEN-CEN waves as cortical waves.

One challenge in investigating whether cortical waves can act as the mediators of EFs is that in many of the cell types that show a strong response to EFs, the wave area is comparable to the cell area. Furthermore, waves are generated at the leading edge of the cell during directed migration (Xu et al., 2003), so that wave dynamics are tightly coupled with cell dynamics. For instance, when a cell responds to an EF reversal, waves typically remain at the cell front as the cell turns. It is not known whether the waves drive cells to turn or the cell polarity keeps the previous leading edge more active so that this edge responds first.

To distinguish between wave response and cell motion, we use electro-fused giant D. discoideum (Neumann et al., 1980) with diameters up to ten times larger than that of an individual cell. Multiple simultaneous waves can be generated across the surface contact area of a giant cell (Gerhardt et al., 2014). These waves also generate actin-filled macropinosomes on the dorsal membrane (Veltman et al., 2016). The giant cells provide an excellent opportunity to study cortical wave dynamics in multiple cell regions simultaneously.

We further use nanotopography to alter the waves’ spatial structures and characteristic timescales. Upon contact with nanotopography, cells produce quasi-1D wave patches. The phenomenon of guided actin polymerization by nanotopography is known as esotaxis (Driscoll et al., 2014), and has been investigated in detail (Ketchum et al., 2018; Lee et al., 2020). There are several advantages of incorporating nanotopography in our study. First, these waves persist for a shorter time on nanotopography than on flat surfaces, enabling us to investigate whether wave systems with different characteristic timescales respond to EFs differently. Second, due to the shorter lifetime, waves on ridged surfaces only propagate in local regions of giant cells. Therefore, nanotopography allows us to distinguish between local and global mediation of the EF response.

Results

Cortical waves and cell migration can be studied independently in giant cells

We imaged cells that simultaneously expressed both limE-RFP and PHCrac-GFP. The former allows us to monitor filamentous actin (F-actin), which represents CEN activities. The latter enables us to monitor phosphatidylinositol-3,4,5-trisphosphate (PIP3), an indicator of STEN activities. In single, differentiated cells, usually only one wave is generated at the leading edge (Figure 1a and Video 1), and the wave motion is coupled with cell motion. For instance, when the cell in Figure 1a changed its direction of motion, the wave remained at the leading edge (72 s - 120 s).

Figure 1 with 1 supplement see all
STEN-CEN waves in single cells and giant cells.

(a) Snapshots of a differentiated, single D. discoideum cell expressing limE-RFP and PHcrac-GFP, with cell boundaries denoted with blue dashed lines. The right column shows the normalized intensity of limE and PHcrac from the arrows in the merge images. The scale bars are 10 µm. (b). Snapshots of an electrofused giant D. discoideum cell on a flat surface, with scanning profiles in the right column. All scale bars are 10 µm. (c) A snapshot of an electrofused giant cell on the ridged surface. The left kymographs are from the line 2 and line 3 specified in the merged image. Line two shows a wave propagating along nanoridges, and line three shows a wave that existed briefly and then dissipated. (d) 3D reconstruction (single time point) of a single D. discoideum cell plated on nanoridges, acquired using a lattice light-sheet microscope. Here, we show the top aspect view (top row) and the side aspect view (bottom row). On the dorsal membrane of the cell, there are waves forming microcytotic cups (triangle) on the curved membrane, and on the basal membrane, there are streak-like waves (asterisk). The red channel represents limE-RFP, and the green channel represents PHcrac-GFP. As both the side and top views show, the dorsal waves and basal waves are independent structures, but both are composed of coordinated F-actin and PIP3.

Video 1
Time-lapse confocal videos of PHCrac (green) and limE (red) on the basal surface of a single, differentiated Dictyostelium discoideum cell set on a flat surface.

Images were acquired every 4 s and shown at 5 frames/s.

In giant cells, multiple waves were initiated randomly and propagated radially across the basal membranes (Figure 1b and Video 2). CEN is driven by STEN, but has a substantially shorter characteristic timescale. Thus, PIP3 waves displayed band-like shapes, whereas F-actin appeared across the bands with higher levels at the rims of PIP3 waves (Miao et al., 2019). As shown in Figure 1b, colliding waves did not cross, but instead rotated by 90° (Figure 1b , 150 s - 200 s). This behavior is suggestive of a refractory period following excitation, which is a hallmark of an excitable system. On nanoridges, the giant cells generated multiple, quasi-1D patches of F-actin and PIP3 with shorter lifetimes than on flat surfaces (Figure 1c and Video 3). Some waves formed and propagated for a short distance (Line two in Figure 1c), whereas others formed and then quickly dissipated (Line three in Figure 1c). The wave dissipation can be explained in terms of an excitable system with lateral inhibition, in which the dispersion of the inhibitor is faster than that of the activator. Thus, the waves eventually dissipate due to the spatial accumulation of the inhibitor. Prior studies have shown that in this situation, the excitable system threshold determines the wave duration (Bhattacharya et al., 2020; Ermentrout et al., 1984). As was the case on flat surfaces, 1D patches occurred throughout the basal surfaces on ridges, and thus were independent of cell motion.

Video 2
Time-lapse confocal videos of PHCrac (green) and limE (red) on the basal surface of a giant Dictyostelium discoideum cell set on a flat surface.

Images were acquired every 10 s and shown at 5 frames/s.

Video 3
Time-lapse confocal videos of PHCrac (green) and limE (red) on the basal surface of a giant Dictyostelium discoideum cell set on a ridged surface.

Images were acquired every 10 s and shown at 5 frames/s.

Waves were also generated on the dorsal planes. In contrast to basal waves, which propagated across the surface contact (Videos 2 and 3), dorsal waves were associated with membrane deformations, and resembled macropinosomes (Video 4). Based on 3D lattice light-sheet images of a cell plated on nanoridges (Figure 1d and Video 5), activation of PIP3 and F-actin was correlated in both basal waves and dorsal waves. However, the dorsal waves were primarily generated in cuplike structures, whereas the stripe-like basal waves spanned the entire basal plane. In all cases, PIP3 activity was coordinated with F-actin activity (Profiles in Figure 1a, b and c), both in the absence (Figure 1) and presence of an EF (Figure 1—figure supplement 1, Video 6). Therefore, in the experiments described below, we only monitored F-actin activity.

Video 4
Time-lapse confocal videos of limE-RFP on the dorsal membrane of a giant Dictyostelium discoideum cell.

Images were acquired every 10 s and shown at 5 frames/s.

Video 5
3D reconstruction of Lattice LightSheet data of PHCrac (green) and limE (red) of a single, vegetative Dictyostelium discoideum cell set on a ridged surface.
Video 6
Time-lapse confocal videos of PHCrac (green) and limE (red) on the basal surface of a giant Dictyostelium discoideum cell set on a ridged surface.

An electric field of 20 V/cm was applied at 0 min, where the cathode was set at the left side. Then the field was reversed to cathode being on the right at 35 min. Images were acquired every 10 s and shown at 5 frames/s.

EFs increase the area, duration, and speed of waves on nanoridges

We found that giant cells respond to a narrow range (15 V/cm to 20 V/cm) of EF amplitudes (Videos 7 and 8), and that higher voltage (35 V/cm) damaged cells. The 1D waves generated on nanoridges related to esotaxis enabled us to quantify the effects of a 20 V/cm EF (all EFs used here are of this magnitude, see Figure 2—figure supplement 1 and Materials and methods for more details about the electrotaxis experiments) on the areas, durations, and speeds of waves. Figure 2a shows snapshots of the dynamics of F-actin in a giant cell on parallel nanoridges with a 1.6 μm spacing. In the absence of an EF (top row in Figure 2a), individual actin polymerization events were initiated in patches on the basal surfaces (Figure 2a and Video 9).

Video 7
Time-lapse confocal videos of LimE-RFP on the basal surface of a giant Dictyostelium discoideum cell set on a ridged surface.

An electric field of 10 V/cm was applied at 0 min, where the cathode was set at the left side. Images were acquired every 10 s and shown at 5 frames/s.

Video 8
Time-lapse confocal videos of LimE-RFP on the basal surface of a giant Dictyostelium discoideum cell set on a ridged surface.

An electric field of 15 V/cm was applied at 0 min, where the cathode was set at the left side. Images were acquired every 10 s and shown at 5 frames/s.

Figure 2 with 2 supplements see all
EFs alter F-actin wave properties.

(a) limE images of a giant cell on nanoridges without an EF (top) and in a 20 V/cm EF turned on at 0 min (bottom). (b). The temporal change of the percentage of the cell area occupied by limE without an EF (blue, Ncell = 5) and in a 20 V/cm EF introduced at 0 min (red, Ncell = 4). The shaded areas represent the mean plus or minus one standard deviation. (c). Division of groups of waves. The color represents the orientation of optical-flow vectors according to the color wheel. The green arrows are the optical-flow vectors, the length of which correspond to the magnitude of motion. The left image is an example of a large structure composed of two independent substructures, where the vectors at the right edge are not moving in the same direction. The wave scales in the directions perpendicular to (blue arrows) and parallel to the ridges (orange arrow) were measured on the preprocessed waves. (d) Density scatter plots of wave scales parallel to ridges vs. perpendicular to ridges. (e) Density scatter plots of actin-wave dimension vs. actin-wave duration. For each wave duration, the five points with the smallest wave areas (black circles) were selected to fit the boundaries (solid black lines). (f) Distributions of wave propagation speeds before (blue, Nwave = 125) and after (red, Nwave = 163) applying an EF. The analyses in d-f were based on N = 4 independent experiments. The two distributions are different (Two-sample t-test, p = 0.017).

Video 9
Time-lapse confocal videos of LimE-RFP on the basal surface of a giant Dictyostelium discoideum cell set on a ridged surface.

An electric field of 20 V/cm was applied at 0 min, where the cathode was set at the left side. Then the field was reversed to cathode being on the right at 30 min. Images were acquired every 10 s and shown at 5 frames/s.

In the first several minutes after turning on the EF, most patches propagated as a wave along a single ridge (Figure 2a, blue inset). After the EF was on for 10 min, some patches appeared to undergo coordinated motion across several ridges (Figure 2a, pink inset). We calculated the ratio of F-actin occupancy to cell area, and found that an EF increased the overall level of actin polymerization by a factor of two to three (Figure 2b). Actin patches were larger in the presence of an EF and organized into larger groups located preferentially at the cell front (bottom row in Figure 2a, 20 min, and 25 min), leading to wider protrusions at cell fronts that drove directed cell migration (Figure 2—figure supplement 2). To determine whether the groups comprised a single, large wave growing across multiple ridges or multiple, small patches nucleated in close proximity, we measured the dynamics of the patches using optical flow (Lee et al., 2020), focusing on the patch edges (Figure 2c, left image. See Materials and methods for more details). If both edges of a patch were moving in the same direction, the structure was classified as a single, large wave. If the edges were not coordinated, the patch was classified as multiple, individual structures. This method enabled us to capture accurately waves that span across multiple ridges and are moving coordinately.

Once the large actin structures were classified, their instantaneous dimensions were measured parallel and perpendicular to the ridges (Figure 2c). Density scatter plots of both dimensions exhibit elliptical contours (Figure 2d), suggesting that nanotopography constrains wave growth. With an EF parallel to the ridges, the waves broadened in both directions (Figure 2d). The average increases in wave dimension parallel and perpendicular to the ridges were 20% and 13%, respectively, and the average increase in wave area was 44%. An increase in wave duration was also observed, with the minimum wave area correlated to the duration (Figure 2e, black circles). The wave area depends exponentially on the maximum wave duration (Figure 2e, solid black lines), allowing us to extract a characteristic wave time scale via

(1) Areamin=C*eDurationT .

Here, C is a constant, and T is the characteristic time scale, which is 48 s with no EF and 61 s in the presence of a 20 V/cm EF. This difference is consistent with the EF drawing the system closer to the excitability threshold. An average increase of 9% in wave propagation speed was also observed in the presence of an EF (Figure 2f).

EFs guide the direction of actin waves

Next, we consider the directional guidance of actin waves by EFs on nanoridges (Figure 3a and Video 9) and on flat surfaces (Figure 3b and Video 10, see Methods for details). The EF was introduced at 0 min (T1), and in the first 2 min had little effect on the actin dynamics on any surface. On nanoridges, actin waves continued to propagate preferentially along the ridges (Figure 3a, T1). On flat surfaces, the waves propagated radially in groups, as seen from the broad distribution at T1 in Figure 3b. In the presence of an EF, the waves propagated preferentially towards the cathode within ~15 min (Figure 3a and b, T2). The perpendicular spread was significantly more limited on nanoridges (Figure 3a, T2).

Figure 3 with 2 supplements see all
EFs guide actin waves.

(a, b) Optical-flow analysis of actin-wave dynamics in giant cells on ridged and flat surfaces. The top row shows a time series of limE images for giant cells overlaid with optical-flow vectors, the color of which is coded according to the color wheel. The accompanying polar plots show the corresponding orientation displacements of optical-flow vectors. For both a and b the EF was turned on at 0 min. The bottom time stamp indicates when the EF was reversed from the cathode being on the right (red) to the cathode being on the left (green). (c) Distributions of wave duration from three independent days of experiments. The distributions were weighted by wave area, because the number of long-lasting large waves on flat surfaces (Nwave = 359) is smaller than the number of short-lived small patches on ridged surfaces (Nwave = 658). Correspondingly, the absolute waves counts do not match the pixel-based, optical-flow analysis in a and b. Based on a two-sample t-test on the wave areas on flat surfaces vs. on ridges, the null hypothesis was rejected at the 5% significance level with p = 2 × 10–15. (d). Kymographs of orientation displacements of optical-flow vectors. The x-axes of the kymographs represent time, and the y-axes represent orientation. The colors represent the proportions. The EF was turned on at time T1, and was reversed at the time denoted by the black arrow (e) LimE snapshots showing the patterns of actin-wave expansion during steady directed migration in a constant EF (top) and after reversing the EF direction (bottom). The blue arrows point to specific stages of wave expansion. W: Wave, S: Stage of wave expansion. The right panel is a cartoon illustrating the patterns of actin-wave expansion during directed migration in EFs (top) and after EFs were reversed (bottom).

Figure 3—source data 1

Wave duration on flat surfaces / ridged surfaces.

Related to Figure 3c.

https://cdn.elifesciences.org/articles/73198/elife-73198-fig3-data1-v1.xlsx
Video 10
Time-lapse confocal videos of limE-RFP on the basal membrane of a giant Dictyostelium discoideum cells set on a flat surface.

An electric field of 20 V/cm was applied at 10 min, where the cathode was set at the left side. The cathode was reversed at 30 min. Images were acquired every 10 s and shown at five frames/s.

The direction of the EF was reversed after the cell had commenced steady directional migration, which took ~20–25 min. Following the field reversal, waves on ridged surfaces reoriented toward the new cathode within 5 min (Figure 3a, T3). On flat surfaces, the wave propagation direction was perpendicular to both the previous and the new EF directions at ~7 min after the field reversal (Figure 3b, T3). Preferential propagation toward the new cathode occurred after ~13 min (Figure 3b, T4). The difference in response time between nanoridges and flat surfaces may be related to the fact that waves persist longer on flat surfaces than on nanoridges (Figure 3c).

Figure 3d shows the continuous temporal changes of the orientation distributions. On nanoridges, the preferred wave directions switched directly following EF reversal (left plot in Figure 3d), whereas on flat surfaces waves maintained a preferred direction that changed continuously in a U-turn behavior (Right plot in Figure 3d). In contrast, although single D. discoideum cells undergo U-turns in response to EF reversal (Sato et al., 2007), giant cells did not (Video 10).

Wave turning may be related to differences in the patterns of wave expansion (Figure 3e). On flat surfaces, waves started from a small patch (Figure 3e, S1) and eventually broke into band-shaped waves (Figure 3e, S4). During directed migration, the intermediate expansion of actin waves (Figure 3e, S2, S3 in the top row) was biased by the EF, resulting in band-shaped waves propagating preferentially towards the cathode (Figure 3e, S4 in top row). After EF reversal, waves expanded in all directions (Figure 3e, S2, S3 in bottom row), such that optical-flow analysis captured turning behavior more frequently.

We also simultaneously imaged limE-RFP at the basal plane (near the surface contact) and the dorsal plane (6 µm higher). Dorsal waves (Videos 11 and 12) are localized at cell fronts and rearranged to the new fronts following EF reversal (Figure 3—figure supplement 1). Rather than directly switching preferential direction, dorsal waves gradually turned toward the new cathode (Figure 3—figure supplement 1), in a manner similar to that of basal waves on flat surfaces. Thus, two different response times to EF reversal exist within the same cell, with a faster response for basal waves guided by nanotopography and a slower response for the free dorsal waves. On flat surfaces, the two responses are synchronized (Figure 3—figure supplement 2).

Video 11
Time-lapse confocal videos of limE-RFP on the dorsal membrane of a giant Dictyostelium discoideum cell set on a flat surface.

An electric field of 20 V/cm was applied at 5 min, where the cathode was set at the left side. The cathode was reversed at 30 min. Images were acquired every 10 s and shown at 5 frames/s.

Video 12
Time-lapse confocal videos of limE-RFP on the dorsal membrane of a giant Dictyostelium discoideum cell set on a ridged surface.

An electric field of 20 V/cm was applied at 10 min, where the cathode was set at the left side. The cathode was reversed at 30 min. Images were acquired every 10 s and shown at 5 frames/s.

Subcellular spatial inhomogeneity of the response to EFs on nanoridges

Although waves in migrating D. discoideum cells localize predominantly at the leading edge (Weiner et al., 2007; Zhao et al., 2002), waves are observed across the basal layer in giant D. discoideum cells. We analyzed the smaller, shorter lived waves on nanoridges. Although the wave locations were distributed essentially uniformly throughout cells in the absence of an EF, more waves were generated at the cell fronts in the presence of an EF (Figure 4a and Video 9). In addition, the average area per wave was larger near the front of cells in an EF (Figure 4b). We also measured the wave properties in the single cells scattered throughout the field of view but did not observe a corresponding gradient of wave properties among single cells closer to the cathode versus the cells closer to the anode. This result indicates that the spatial inhomogeneity shown in Figure 4a and b was caused by the EF rather than by the absolute electrical potential relative to the ground (Figure 4—figure supplement 1).

Figure 4 with 2 supplements see all
Spatial inhomogeneity of the response to EFs on nanoridges.

(a) Density scatter plots of the wave area vs. x position of the wave relative to the cell center. Nanoridges and EF are orientated in the x-direction. The difference of x coordinates of cell center and wave location was calculated, then the value was further normalized by the cell radius. Each point represents a wave, and all the points were collected from five independent experiments. The left plot is for a period in which there was no EF (Nwave = 296), and the right plot is for a period in which there was a 20 V/cm EF, during which the cells exhibited steady directional migration (Nwave = 224). For each experiment without an EF, the EF was always turned on several minutes later. Thus, we defined the direction in which cathode was located in the presence of an EF as the positive direction in the absence of an EF. The color code corresponds to the density of points. (b) Average wave area in subcellular regions. The points in a were sectioned, based on their x position relative to the cell center (normalized by cell radius) at a bin size of 0.25 (8 sections in total from –1–1), and calculated the average wave area in each section. (c) Changes in actin waves' spatial distribution in response to EF reversal; data from six independent experiments. The color of each plot is coded according to the timeline displayed at the bottom of the panel. P2-P5: The EF was reversed, and cells gradually developed polarization toward the new cathode. The number of waves in each plot: Np2 = 272, Np3 = 277, Np4 = 193, Np5 = 246. (d) A schematic illustrating the old and new fronts of giant cells when the EF was reversed. (e) Time stacks of orientation distributions of optical-flow vectors at an old front and a new front. The EF was reversed from the cathode being at the right (0) to the cathode being at the left (π) at 0 min. (f) Comparisons of response time between new fronts (green) and old fronts (orange) from multiple experiments (Ncell = 5). The p-value was calculated using a pairwise t-test at the 5% significance level. (g) Cartoon illustrating different time scales of local wave propagation and global rearrangement of STEN-CEN thresholds, in response to EF reversal.

Figure 4—source data 1

Wave area and wave x position relative to the cell center (normalized by cell radius) in different periods of electrotaxis experiment.

Related to Figure 4a–c.

https://cdn.elifesciences.org/articles/73198/elife-73198-fig4-data1-v1.xlsx

We explored the response of this inhomogeneity to EF reversal by tracking each wave location relative to the cell centroid in the 12 min following EF reversal (Figure 4c). New waves started to appear near the side of the cell facing the new cathode within 3 min (Figure 4c, left region of P2), whereas the complete inhibition of wave generation near the old cell front took longer (Figure 4c, right region of P5). This observation suggests that the initiation at a new cell front and the inhibition of waves at the old front are regulated by two distinct processes with different timescales.

Next, we looked at the time required to switch propagation direction in different subcellular regions following EF reversal. The basal membrane was segmented into an ‘old front’ region (facing the original cathode) and a ‘new front’ region (facing the new cathode), as illustrated in Figure 4d. The distributions of wave propagation directions show that waves in the new front region switched their preferential direction at ~4 min. In contrast, waves in the old front region changed their preferential direction on a time scale of ~7 min (Figure 4e and f). Our analysis further shows that larger waves in the old fronts are less sensitive to EF reversal than those in the new fronts (Figure 4—figure supplement 2).

Discussion

By employing giant cells, in which the cortical waves are disentangled from cell motion, we demonstrate that EFs modulate cortical wave dynamics directly, providing a mechanism for cell guidance by EFs (Figure 1c, b and d). Our use of nanoridges to generate quasi-1D waves that are small, short-lived, and unable to turn (Figure 1c) enabled detailed quantification of wave properties, demonstrating that EFs directly affect the abundance, locations, and directions of cortical waves.

EFs guide cortical wave dynamics

Previous studies have suggested that the basal cortical waves in D. discoideum are insensitive to external chemotactic gradients, whereas ‘pseudopods’ at other regions in the same cells can be guided (Lange et al., 2016). This conclusion is surprising because the biochemical events traveling with the waves are the same as those occurring on pseudopods, and pseudopods with the dorsal cups on the same cells do respond to chemoattractants. Also, similar cortical waves in human mammary epithelial cells can be guided effectively by epidermal growth factors (Zhan et al., 2020). Additional input from the greater contact of giant D. discoideum cells with the surface may outweigh the effect of applied chemical gradients on the basal waves. Other studies have shown that single cells can integrate combinations of external chemical and mechanical stimuli.

Our work shows that in giant cells, waves of both F-actin polymerization (Figure 3) and its upstream regulator PIP3 (Figure 1—figure supplement 1) are indeed guided by EFs. These biased biochemical and biomechanical events lead to more protrusions at the cell front than at the cell back, thus driving cell migration (Figure 2—figure supplement 2). The development of the biased wave activities takes ~10 min following the introduction of an EF (Figures 2a and 3), which is much slower than the timescale of surface-receptor-regulated chemotaxis. The high resistance of the cell membrane limits the effects of EFs on intracellular components, but EFs may act on the charged lipids and molecular clusters. Thus, we suspect that the slow response results from the electrophoresis of the charged membrane components involved in wave formation, which has a characteristic time scale of 5–10 min (Allen et al., 2013; McLaughlin and Poo, 1981).

We further explored the dynamics in response to EF reversal at the subcellular level using nanotopography (Figure 4). We observed that the new waves are induced to propagate towards the current cathode within 2–3 min (Figure 4e and Figure 4—figure supplement 2), suggesting that waves themselves can adapt quickly to the changing electrical environments. Because we only observed the fast adaptation on ridged surfaces, this phenomenon may be related to the shorter wave lifetimes on nanoridges than on flat surfaces. A short lifetime allows waves to be nucleated at a higher rate on the nanoridges, leading to a rapid directional response. During this process, the EF may regulate the wave nucleation through locally changing specific charged lipids, ion fluxes, or local pH gradients (Crevenna et al., 2013; Frantz et al., 2008; Köhler et al., 2012; Martin et al., 2011; Zhou and Pang, 2018).

EFs modulate the thresholds of the excitable wave system

Recent studies have shown that the cortical wave system can be described as a coupled signal transduction and cytoskeletal excitable network. Based on both simulation and experimental studies (Bhattacharya et al., 2020; Miao et al., 2017), it has been shown that the wave ranges, durations, and speeds are determined by the local threshold of activation, which in turn are regulated by the relative levels of activators and inhibitors (Miao et al., 2017; Miao et al., 2019).

Our quantification shows that guided waves become larger, faster, and more persistent in an EF (Figure 2), indicating that the excitable system is closer to its threshold for activation (Miao et al., 2019). This effect may arise from enhanced positive feedback, reduced negative feedback, or both. We further find that wave nucleation is enhanced at the cell front and suppressed at the back (Figure 4a and b). This subcellular inhomogeneity is consistent with a biased excitable network framework (Iglesias and Devreotes, 2012; Meinhardt, 1999; Tang et al., 2014; Xiong et al., 2010), which was added to the STEN-CEN model to introduce an internal spatial gradient in the local threshold of wave initiation, akin to cell polarity.

Local excitation and global inhibition (Xiong et al., 2010), LEGI, schemes have effectively recreated the features of both fast directional sensing and stable polarity in response to chemical signals, which can lead to robust biased excitable network. Both directional sensing and stable polarity can lead to a robust biased excitable network. For chemical signals, the directional response from PIP3 occurs within seconds, whereas the establishment of stable polarity usually requires many minutes. However, based on our analysis, establishing both directional response (Figure 3) and polarity (Figure 4) in response to EFs requires 5–10 min. It is worth noting that PIP3 waves also sense EFs on a time scale of minutes (Figure 1—figure supplement 1). Our observation suggests that EFs act on the polarity establishment rather than directional sensing. This hypothesis is supported by a recent study showing that G-protein-coupled receptors (GPCRs), which are the regulator in the LEGI model for D. discoideum that allows for sensing chemoattractant on timescales of seconds, are not essential for electrotaxis (Zhao et al., 2002).

EFs act on waves, and waves determine cell behaviors

Our results raise the possibility that cortical wave dynamics are modulated directly by EFs and that the waves in turn mediate cellular response. Waves travel across cell membranes to coordinate the trailing edge with the front edge, and the cytoskeletal components in cortical waves are involved in developing the stable polarity. On the other hand, the duration and turning capacity of STEN-CEN waves directly impact the speed and characteristics of the cellular response to EFs (Figure 3) on a longer timescale than that of surface-receptor-regulated chemotaxis.

Our results shed light on how EFs modulate protrusions. Previous studies have shown that various protrusions that drive cell motion, such as filopodia, lamellipodia (Miao et al., 2019), and macropinocytotic cups (Video 4), are always associated with expanding waves near cell perimeter. Our previous work has shown that changing wave properties by perturbing STEN-CEN states leads to the transition of protrusion profiles, which indicates that wave properties dictate the properties of the protrusions (Miao et al., 2019). Here we showed that EFs can alter the waves differently on the two ends of the cell (Figure 4a). As a result of these spatially inhomogeneous wave properties, protrusions become more abundant and larger on one side of the cell versus the other, which eventually leads to guidance of cell migration.

On flat surfaces, a slow U-turn is observed following EF reversal, whereas on nanoridges, faster switching is observed. Thus, the response of migrating cells to a changing guidance cue can be predicted from the characteristics of the waves driving the migration process. Indeed, the U-turn behaviors of neutrophils and differentiated, single D. discoideum cells in response to EF reversal (Hind et al., 2016; Sato et al., 2007; Srinivasan et al., 2003; Xu et al., 2003), which are usually ascribed to stable cell polarity, may instead reflect the persistence and 2D turning behavior of cortical waves in these environments (Figure 3).

Nanoridges allow us to shed further light on the multiscale character of the system, because cells include both short, 1D waves on the basal plane, and longer lasting, 2D waves on the dorsal plane. The different response times on the subcellular level due to different wave behaviors (Figure 4—figure supplement 2) provide strong evidence that cortical waves act as direct mediators of EFs. Waves on different planes are similar in composition but are impacted differently by the EF. We observed fast switching of wave directions in the basal plane near the ridged substrate and slower turning of the waves in the dorsal plane within the same cell, indicating that the direction of waves is controlled locally by external cues (Figure 3—figure supplement 1).

EFs provide a means to modulate cortical waves directly. On the other hand, biological conditions that modulate wave characteristics may also speed up or suppress the cellular response to directional cues. Longer-lasting waves offer persistence in the face of rapidly changing gradients, whereas shorter waves yield faster adaptability to changing directional signals. The durations of waves and their ability to turn together have a dominant effect on the response of cells to an EF.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Cell line (D. discoideum)Aca nullhttps://doi.org/10.1016/S0092-8674(03)00081–3The cell line was a gift from Carole A. Parent lab.
Cell line (D. discoideum)PHcrac-GFPLimE-RFPhttps://doi.org/10.1038/ncb3495The cell line was a gift from Peter N. Devreotes lab.
Software, algorithmOptical flow analysis (run by MATLAB)https://doi.org/10.1091/mbc.E19-11-0614

Cell line

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In the study, we used LimE-RFP aca null Dictyostelium discoideum (D.d.) and PHcrac-GFP/LimE-RFP D.d cell lines. LimE-RFP aca null was a gift from Carole A. Parent lab (https://doi.org/10.1016/S0092-8674(03)00081-3), and PHcrac-GFP-LimE-RFP was a gift from Peter N. Devreotes lab (https://doi.org/10.1038/ncb3495). We have conducted the mycoplasma contamination testing for both cell lines and did not detected contamination.

Cell culture

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Dictyostelium discoideum cell lines were grown axenically in the HL5 medium. Aggregation adenylyl cyclase null (ACA−) mutants, which do not produce cAMP and do not have chemotaxis signal relay (Kriebel et al., 2003), were used in electrotaxis experiments to avoid chemotaxis. The cells used also express limE-RFP as a reference for filamentous actin structures. G418 was used as the selection medium during cell culture. For the experiments in Figure 1, Figure 1—figure supplement 1, we used Dictyostelium discoideum co-expressing PHcrac-GFP and LimE-RFP, and we used G418 as the selective medium. Note that an enhancement in LimE concentration is associated with protrusions, as seen in Figure 2—figure supplement 2, and that the protrusions are biased to the side facing the cathode. Because protrusions are driven by F-actin polymerization, we believe this observation rules out the possibility that LimE binding/unbinding to/from F-actin itself is sensitive to EFs.

Electrofusion

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Cells were washed twice with 17 mM Sorensen buffer (15 mM KH2PO4 and 2 mM Na2HPO4, pH 6.0) and rolled for 30 min at a concentration of 1.5 × 107 mL–1. Electrofusion was conducted with a Gene Pulser Gen1 system. Three pulses of 1 kV at a 1 s interval were applied. After electroporation, cells were relaxed for 5 min. Then cells were diluted to 5 × 105 mL–1 with normal developing buffer (5 mM KH2PO4, 5 mM Na2HPO4, 2 mM MgCl2 and 0.2 mM CaCl2, PH 6.5) and seeded into a customized electrotactic chamber, with dimensions 20 mm × 5 mm × 0.25 mm. The aca null cell line that we used did not generate many waves in the vegetative stage, and electro-fusion with 1 kV pulses stressed the cells. Thus cells were starved for 2 hr before experiments to generate more actin waves.

Nanotopography fabrication

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The nanotopographic pattern used in these cell studies was fabricated through a technique known as multiphoton absorption polymerization (MAP), as described elsewhere (LaFratta et al., 2006; LaFratta et al., 2004). An ultrafast, pulsed laser beam (Coherent Mira 900 F, 76 MHz) was passed through a high-numerical-aperture microscope objective into a photopolymerizable resin sandwiched between glass coverslips. A LabVIEW (National Instruments) program allowed for control of the stage position and the shutter state, determining where polymerization occurred (and did not) in the resin, allowing patterning. Once fabrication was completed, the patterned sample was developed in ethanol twice for 3 min each to remove unreacted monomer. The polymerized structure was baked at 110 °C for at least 1 hr.

To produce the necessary number of replicate patterns with the same dimensions, an adapted version of replica molding was performed (Sun et al., 2018). A hard polydimethylsiloxane (h-PDMS) film containing hexanes to increase the resolution of feature replication was spin-coated onto the functionalized structure made from MAP. The film was allowed to sit on the structure for 2 hr at room temperature and was then baked at 60 °C for 1 hr. Regular PDMS (Sylgard 184) was prepared at a 10:1 ratio of elastomer base to the curing agent by degassing and mixing. The PDMS was poured onto the h-PDMS film, and molding was completed by baking at 60 °C for an additional 70 min. The final mold was peeled from the glass slide supporting the MAP-patterned structure.

The mold was used to produce replicas of the original pattern. A drop of the same acrylic resin was placed on the patterned area of the PDMS mold, and then an acrylate-functionalized glass coverslip was pressed firmly on top, spreading the sandwiched drop. Tape secured this system in place. The resin was cured for a total of 5 min under a UV lamp (Blak-ray), producing a polymer film. It should be noted that the PDMS mold is the negative relief pattern of the structure made using MAP. Therefore, samples (or replicas) of the original pattern could be produced on a relatively large scale with this method. The replicas were soaked in ethanol for at least 12 hr before use in the cell studies. We fabricated samples with flat surfaces by using a PDMS mold with a smooth surface.

Lattice light-sheet microscopy

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The 3i lattice light-sheet microscope in the Johns Hopkins School of Medicine Microscope Facility was used for two-color, 3D imaging. Vegetative, single Dictyostelium cells were seeded on a circular 5 mm coverslip patterned with nanoridges, which was immersed in a bath of standard developing buffer throughout imaging.

Electrotaxis experiments

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We 3D-printed electrotaxis chambers (Figure 2—figure supplement 1) with dimensions of 20 mm × 5 mm × 0.25 mm and composed of a clear resin using a Formlabs Form2 3D-printer. Agar bridges were used to isolate cell media from electrodes to minimize electrochemical products and pH changes. Twenty V/cm constant EFs were applied. Time-lapse images of the phase-contrast channel and the RFP/GFP channel were recorded using PerkinElmer spinning-disk microscope at a frame rate of 0.1 frames/s (Yokogawa CSU-X1 spinning-disk scan head (5000 rpm)) with Hamamatsu EMCCD camera and Volocity analysis software.

Optical-flow analysis and model fitting of actin polymerization dynamics

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We applied the Lukas-Kanade optical-flow method to quantify the direction of the intensity flow in fluorescence videos. This algorithm produced pixel-basis vector fields of intensity motion. Before applying the optical-flow algorithm, each image was smoothed by a 2D Gaussian filter (σ = 3) to reduce noise. After the smoothing, we further removed the flow vectors created by noise using optical-flow reliability as our criterion. The reliability is defined as the smallest eigenvalues of the ATwA matrix, where w is a Gaussian weight matrix and A is the intensity gradient matrix. The size of the weight matrix for D. discoideum was set at 19 × 19, with standard deviation σ = 2 (0.42 μm).

We built a bimodal von Mises model to compare the actin and cellular responses accurately. A von Mises distribution is given by

(2) fVMθ|μ,κ=eκcosθ-μ2πI0(κ) .

where the peak location is μ and the concentration κ. The orientation distribution of optical-flow vectors at each time point is fit with two von Mises distributions

(3) fθ|μ1,μ2,κ,p1,p2=p1fVMθ|μ1,κ+p2fVMθ|μ1+π,κ

where p is the proportion of each component. We use the constraints

(4) μ1-μ2=π

and

(5) p1+p2=1

Maximum likelihood estimation (MLE) is applied to estimate model parameters based on the orientation of all the optical-flow vectors every 12 frames (2 min). With this model, we can quantitatively study the temporal change of actin dynamics. The preferential direction is defined as the μ with the largest proportion p at each time point.

Quantification of actin wave properties

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The segmentation of actin waves was conducted based on the combined information from fluorescence intensities and optical flow. We first applied the kmeans (k = 3) cluster (Kanungo et al., 2002) to pick up the bright regions in the limE-RFP videos, then only kept the moving objects by applying the reliability mask from the optical-flow analysis.

To classify the large actin structures composed of substructures moving independently, we considered the optical flow at the edges of the large structure (Figure 2c). If the optical-flow vectors were moving in the same direction at both edges, the large structure was classified as a single wave. Otherwise, the large structure was divided into multiple smaller patches. In the latter case, a pronounced boundary was detectable between two substructures, then we used the detected boundary to divide the large structure into multiple substructures.

After classification, we measured properties such as wave speed, wave duration, and wave area to characterize STEN-CEN. Wave speed was measured by tracking the clusters of optical-flow vectors oriented in similar directions. The detailed algorithm can be found in a prior publication (Lee et al., 2020). To measure wave duration, we first tracked actin waves using a customized, multi-object tracking tool based on the overlapping areas between frames. A unique identification number was assigned to each wave, then wave duration, wave area (measured by the Matlab function regionprops) were recorded for each wave.

Data availability

The data and the codes that were used to analyze the data have been uploaded to Dyrad. https://doi.org/10.5061/dryad.f7m0cfxx4.

The following data sets were generated

References

    1. Harvath L
    (1991)
    Elevation of cAMP differentially affects chemotactic responsiveness
    The Journal of Immunology 146:22.

Decision letter

  1. Alphee Michelot
    Reviewing Editor; Institut de Biologie du Développement, France
  2. Jonathan A Cooper
    Senior Editor; Fred Hutchinson Cancer Research Center, United States

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Cortical waves mediate the cellular response to electric fields" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Jonathan Cooper as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

eLife usually recommends to include a numbered list of essential revision requirements. However, in the case of your manuscript, the 3 reviewers, who appreciated your work, all raised different and interesting points that deserve a response from you. Therefore, we are exceptionally leaving the comments of the different reviewers for you to answer separately. Since many of these points do not require any experimental effort from you, this should allow you to improve your manuscript with a reasonable amount of work for these revisions.Reviewer #1 (Recommendations for the authors):

I would recommend its publication provided that few complementary experiments are performed to validate this approach.

Main comments:

1/ How good is LimE-RFP as a marker of actin networks? Is there any evidence from the literature that LimE-RFP binding/unbinding to/from actin filaments is not itself sensitive to EF? If this was the case, it would complexify greatly the analysis of the data. If no information is available from the literature, the authors should perform control experiments with a different actin marker (ideally GFP-actin expressed at low level) to confirm this point unambiguously.

2/ Response to EF could have been characterized even better. What happens when EF are switched off? Is the process of network growth reversible and at which time scale?

In Figure 2a, could the authors provide comparative data in the absence of EF?

Is the effect of network propagation under EF saturating after a certain time (> 25 min)?

What is the threshold intensity of EF that is required to initiate visible wave propagation in this system?

Reviewer #2 (Recommendations for the authors):

Several aspects should be addressed and clarified.

1) The authors convincingly show that the dynamics of cortical waves can be guided by EFs. But it remains unclear to me what actually is the role of cortical waves in electrotaxis. Does electrotaxis depend on cortical waves or do we also observe electrotaxis in cells that don't show any waves? D. discoideum cells do not show waves at all times. Also, there are cell lines that do not show basal actin waves. So this could be actually tested, unless the authors want to concentrate on the wave dynamics and leave their functional role in electrotaxis open for now. The wave dynamics are very interesting in its own right but a clearer statement regarding the role of cortical waves in electrotaxis would give their findings a wider biological relevance.

2) A very nice aspect of the present work is that not only basal but also dorsal waves are considered. However, some of the results on dorsal waves do not look convincing to me. In Figure S2, it is shown that conclusions about the dynamics of dorsal waves are drawn from 2D images taken along a planar confocal cut (red line in Figure S2). This means that features on top of the cell (above this plane) may be missed, and looking at the corresponding videos, it indeed is obvious that most of the activity is seen along the "border", where the cut intersects with the dorsal membrane. Why are the authors not using the full power of their lattice light-sheet microscope and image the dorsal side in full 3D? Is this technically not possible? It would be much clearer and more convincing to actually track the dorsal waves across the full dorsal membrane and not only at the rim, similar to waves at the basal membrane.

3) In the Discussion, the authors interpret their findings in the context of excitable waves (STEN-CEN waves). If this way of modeling cortical waves is correct, I understand that the present findings can be probably related to this concept. However, the line of argments is somewhat vague and hand-waving because the authors do not present an actual extension of their STEN-CEN model to incorporate electric fields. For example, I am not sure if waves necessarily will become "larger, faster, and more persistent" if the excitable system is closer to its activation threshold. Perhaps the authors could demonstrate this based on their existing STEN-CEN framework?

Line 340: it is not clear to me why “the coexistence of waves with different behaviors in different portions of the cell provides powerful evidence that cortical waves act as direct mediators of Efs.” What is meant by “direct mediators”? Earlier (line 242) you say that it is the polarized intracellular environment that causes the spatial inhomogeneity in the response to Efs. So aren’t the waves rather “indirect mediators” of the Efs? Please explain.

References:

There is quite a bit of literature on actin waves in D. discoideum. In addition to Ref. 8 which addresses oversized cells, I suggest that you give more credit to earlier work by Gunther Gerisch and others, also with respect to possible functions of actin waves in motility, division etc.

Reviewer #3 (Recommendations for the authors):

The big picture summary is found in the public review. Here, we list the specific comments we have that are needed to strengthen the work for eventual publication. I have kept experimental requests to a minimum, and indicated the points where they are discussed with an (X) symbol.

1. (X) Lack of experimental methods clarity. In a field as confusing as electrotaxis/galvanotaxis, it is not acceptable to simply write in the methods: “20V/cm constant EF fields were applied.” How were they applied? Which specific approach was deployed and what did the chamber look like, and how might the choice of system have affected the results? 20V/cm is a very high stimulus for injecting charge into an aqueous electrolyte medium—surely this would produce hydrolysis and pH changes unless this is controlled for? All of these concerns can be addressed by a proper discussion of the methodology. However, this reviewer feels two experimental details (X) are required here. (1) Why was 20 V/cm chosen, and how does the cortical wave phenotype vary with field strength? What happens if a lower or higher field strength is used? It seems like these results would greatly help bolster the STEN-CEN framework and clarify the input-output aspects. (2) Can the authors validate that they controlled well for pH changes due to electrolysis, or might this be an underlying mechanism?

2. Statistics. Many figure panels are missing error bars, and there are very few statistical tests or direct comparisons to help understand how different given phenotypes actually are or how representative the presented data are. Figures 2b,f ; 3c,d; and 4b seem like obvious cases where more discussion of the stats is needed. Similarly, it was often unclear how many samples were being compared in those same panels. A clearer discussion in the text, captions, methods, and those figure panels emphasizing how many explicit experimental replicates and explicit individual cells analyzed would go a long way towards clarifying that these data are truly representative. Control cases were also lacking in a number of figures (see Figure discussion below).

3. The STEN-CEN framework was exciting (pun intended), but also didn’t seem to coalesce into a clear finding. It’s a great idea to try to link these findings to an excitable system with known dynamics, but STEN, CEN, BEN and other acronyms were frequently deployed without obvious quantitative connections to the data itself, at least in the sense that non-specialist readers such as myself would be able to follow this. Given the centrality of STEN-CEN to the introduction and claims in the discussion, I think the authors need to do better represent the importance and validity of STEN-CEN here. I understand that Figure 1 shows ‘STEN-CEN’ dynamics, but most of that discussion took the form of (“see Figure 1b 150s-200s”) and it wasn’t really clear to me what I should be looking for in these panels. If Figure 1 is the place where STEN-CEN is validated as being relevant, it would be great to see something a little clearer in the text, and in the figure emphasizing the specific characteristics we should focus on.

4. The nano-ridges seemed almost superfluous until the end. The nano-ridges are quite clever, but their introduction and discussion of their importance seemed a bit scattered, despite their being involved in each figure. I would have appreciated a clearer discussion of why this method was necessary compared to just the smooth surface data. Esotaxis is interesting, but it wasn’t clear that it was necessary for most of the claims here, although I can see how it was useful. Kudos for the detailed methods section here. One experimental question—did the use of PDMS here affect dicty motion relative to migration on glass/plastic in the flat surface cases?

5. (X) Biological claims and mechanisms. This is a nice biophysics paper-I do not think the authors need to go further down the rabbit hole and prove a specific molecular mechanism. However, I do think they need to be a little more careful about some of the claims and to also spend more time discussing why they think these waves are forming here and what might be nucleating the waves upstream of them. Speculation would OK here, and I felt the paper sort of overemphasized the dynamics of the waves without really musing on how the sausage was made. More discussion would help here. That said, there is one biological experiment that seems easily within reach and, unless I misread the paper, is missing. The authors stated up front that the co-localization of F-actin and PIP3 in control data meant that only F-actin needed to be evaluated for the actual stimulation experiments. It was actually unclear from Figure 1 how strong that co-localization actually was (see below), but more to the point-I think the authors need (X) to show even a single supplemental figure clearly demonstrating that this overlap of PIP3/F-actin still holds in the stimulated case as well as the control rather than simply assuming that it does from looking only at the control data. Apologies if I misinterpreted the data and this is already accounted for; it just wasn’t clear to me and I’d like to see an analogous panel to some of the data currently in Figure 1 except for stimulated cells.

Figure 1. Why is red consistently ‘outlining’ green, and how does that support the co-localization claim?

Figure 2d/e were a little hard to assess in the sense that going from 0-20V/cm seems like it should produce a drastic change, but the visualizations seem relatively similar, so I think I’m not reading these right. Can these be clarified in the figure or caption? 2f doesn’t have any statistics (I get that it’s a histogram, but I don’t know how important the implied shift in the peaks is).

Figure S4-I did not follow this discussion. How do the authors use the data in S4 to show that polarized intracellular environments were key vs. external fields? I was also confused about that claim because I thought it was well established that the field must act on the extracellular membrane components because it can’t penetrate the plasma membrane (re: impedance), so I wasn’t sure what this discussion point or figure were addressing.

https://doi.org/10.7554/eLife.73198.sa1

Author response

Reviewer #1 (Recommendations for the authors):

I would recommend its publication provided that few complementary experiments are performed to validate this approach.

Main comments:

1/ How good is LimE-RFP as a marker of actin networks? Is there any evidence from the literature that LimE-RFP binding/unbinding to/from actin filaments is not itself sensitive to EF? If this was the case, it would complexify greatly the analysis of the data. If no information is available from the literature, the authors should perform control experiments with a different actin marker (ideally GFP-actin expressed at low level) to confirm this point unambiguously.

Thank you for the recommendation to publish the manuscript and for this important question of whether we are observing a bias in actin polymerization or merely a bias in actin odelling. We did not find any literature that addresses whether LimE binding / unbinding is sensitive to Efs. However, we see that an enhancement in LimE concentration is associated with protrusions, as seen in supplementary Figure 2 —figure supplement 2. The protrusions are biased to the side facing the cathode. Because protrusions are driven by F-actin polymerization, we would expect no bias in protrusions if our observations were based on a odelling bias. Also, as explained in our response to the next comment, the EF-induced changes in actin polymerization persist for some time after the field is turned off, suggesting that the field itself is not directly altering the binding of LimE to F-actin. We have added the explanation to the cell culture method session.

Edited methods section of cell culture

“Note that an enhancement in LimE concentration is associated with protrusions, as seen in Figure 2 —figure supplement 2, and that the protrusions are biased to the side facing the cathode. Because protrusions are driven by F-actin polymerization, we believe this observation rules out the possibility that LimE binding/unbinding to/from F-actin itself is sensitive to Efs.”

2/ Response to EF could have been characterized even better. What happens when EF are switched off? Is the process of network growth reversible and at which time scale?

Thank you for these suggestions. We have conducted more experiments in which we turned off the EF after 30 min and recorded for an additional 20 min. We found that the actin-wave propagation direction became unbiased within 4 to 6 min. However, we also found that the higher concentration of polymerized actin observed in the presence of an EF remained elevated throughout the 20 min time window after the EF was switched off (not shown in the published paper but shown to the reviewer). Such long-term effects of an EF point to a complex adaptation of cells to the EF, which is beyond the scope of the current manuscript.

In Figure 2a, could the authors provide comparative data in the absence of EF?

We updated figures 2a and b with the results of new control experiments in which we recorded ode for 30 min in the absence of an EF. In the absence of an EF, we did not observe a significant increase in polymerized actin.

Is the effect of network propagation under EF saturating after a certain time (> 25 min)?

In Figure 3d, we show that the wave dynamics become stable after ~ 15 min of turning on the EF.

What is the threshold intensity of EF that is required to initiate visible wave propagation in this system?

We have carried out additional experiments with lower Efs and found that giant cells did not respond to a 10 V/cm EF but did respond to a 15 V/cm EF. Thus, we estimated the threshold intensity of EF is in between 10 V/cm and 15 V/cm. We describe this result in the revised manuscript and include as new supplementary material videos of giant cells in the presence of a 10 V/cm EF (Video7) and a 15 V/cm EF (Video 8).

Added to Results section:

“We found that giant cells respond to a narrow range (15 V/cm to 20 V/cm) of EF amplitudes (Video 7 and Video 8), and that higher voltage (35 V/cm) damaged cells.”

Reviewer #2 (Recommendations for the authors):

Several aspects should be addressed and clarified.

1) The authors convincingly show that the dynamics of cortical waves can be guided by Efs. But it remains unclear to me what actually is the role of cortical waves in electrotaxis. Does electrotaxis depend on cortical waves or do we also observe electrotaxis in cells that don’t show any waves? D. discoideum cells do not show waves at all times. Also, there are cell lines that do not show basal actin waves. So this could be actually tested, unless the authors want to concentrate on the wave dynamics and leave their functional role in electrotaxis open for now. The wave dynamics are very interesting in its own right but a clearer statement regarding the role of cortical waves in electrotaxis would give their findings a wider biological relevance.

We have reworded our discussion (see below) to explain better the role of cortical waves during electrotaxis. We agree that there are cells in which large ventral waves are not visible, but these cells likely have “normal” sized waves associated with each protrusion. Devreotes’ previous studies have shown that the protrusion activities are always associated with wave activities. If a cell has no waves (such as in cells treated with signal transduction inhibitors), it cannot move in a dc EF. A study on cells without large ventral waves is an exciting topic, but it is beyond the scope of this paper, and we would like to leave it for future studies.

Edited Discussion section

“Efs act on waves, and waves determine cell behaviors

Our results raise the possibility that cortical wave dynamics are modulated directly by Efs and that the waves in turn mediate cellular response. Waves travel across cell membranes to coordinate the trailing edge with the front edge, and the cytoskeletal components in cortical waves are involved in developing the stable polarity. On the other hand, the duration and turning capacity of STEN-CEN waves directly impact the speed and characteristics of the cellular response to Efs (Figure 3) on a longer timescale than that of surface-receptor-regulated chemotaxis.

Our results shed light on how Efs modulate protrusions. Previous studies have shown that various protrusions that drive cell motion, such as filopodia, lamellipodia (Miao et al., 2019), and macropinocytotic cups (Video 4), are always associated with expanding waves near cell perimeter. Our previous work has shown that changing wave properties by perturbing STEN-CEN states leads to the transition of protrusion profiles, which indicates that wave properties dictate the properties of the protrusions (Miao et al., 2019). Here we showed that Efs can alter the waves differently on the two ends of the cell (Figure 4a). As a result of these spatially inhomogeneous wave properties, protrusions become more abundant and larger on one side of the cell versus the other, which eventually leads to guidance of cell migration.

On flat surfaces, a slow U-turn is observed following EF reversal, whereas on nanoridges, faster switching is observed. Thus, the response of migrating cells to a changing guidance cue can be predicted from the characteristics of the waves driving the migration process. Indeed, the U-turn behaviors of neutrophils and differentiated, single D. discoideum cells in response to EF reversal (Hind et al., 2016; Sato et al., 2007; Srinivasan et al., 2003; Xu et al., 2003), which are usually ascribed to stable cell polarity, may instead reflect the persistence and 2D turning behavior of cortical waves in these environments (Figure 3).

Nanoridges allow us to shed further light on the multiscale character of the system, because cells include both short, 1D waves on the basal plane, and longer-lasting, 2D waves on the dorsal plane. The different response times on the subcellular level due to different wave behaviors (Figure 4 —figure supplement 2) provide strong evidence that cortical waves act as direct mediators of Efs. Waves on different planes are similar in composition but are impacted differently by the EF. We observed fast switching of wave directions in the basal plane near the ridged substrate and slower turning of the waves in the dorsal plane within the same cell, indicating that the direction of waves is controlled locally by external cues (Figure 3 —figure supplement 1).

Efs provide a means to modulate cortical waves directly. On the other hand, biological conditions that modulate wave characteristics may also speed up or suppress the cellular response to directional cues. Longer-lasting waves offer persistence in the face of rapidly changing gradients, whereas shorter waves yield faster adaptability to changing directional signals. The durations of waves and their ability to turn together have a dominant effect on the response of cells to an EF.”

2) A very nice aspect of the present work is that not only basal but also dorsal waves are considered. However, some of the results on dorsal waves do not look convincing to me. In Figure S2, it is shown that conclusions about the dynamics of dorsal waves are drawn from 2D images taken along a planar confocal cut (red line in Figure S2). This means that features on top of the cell (above this plane) may be missed, and looking at the corresponding videos, it indeed is obvious that most of the activity is seen along the “border”, where the cut intersects with the dorsal membrane. Why are the authors not using the full power of their lattice light-sheet microscope and image the dorsal side in full 3D? Is this technically not possible? It would be much clearer and more convincing to actually track the dorsal waves across the full dorsal membrane and not only at the rim, similar to waves at the basal membrane.

Thank you for the suggestion. We agree that our data is just a cross-section of the actual waves, and full z-scanning using lattice light-sheet would be more powerful than our spinning disk experiments. Unfortunately, the lattice light-sheet system sample chamber is not compatible with the channel device used to generate reproducible Efs. Photo-bleaching and laser damage prevented capturing more than two z planes and tracking waves along the curved surface for our spinning-disk system. However, these cross-sections did provide some information, such as where waves are located, and in which direction the waves move in the x-y plane. Accordingly, we have added explanation to the text and legends indicating that the ventral and dorsal data are not exactly comparable:

Edited text (current Figure 3 – supplement figure 1)

“Figure 3 —figure supplement 1. Basal actin waves reverse direction on nanoridges, whereas dorsal waves turn. A. A schematic showing the two imaging planes used, with the morphology of the substrate. B. LimE-RFP images recorded at the dorsal plane. Unlike the basal focal plane images, which capture the complete basal wave dynamics, the dorsal plane images do not capture the full dorsal wave motion. To avoid photobleaching and laser damage, we only imaged the cross-section of the dorsal waves and tracked the cross-sections using optical-flow analysis”

3) In the Discussion, the authors interpret their findings in the context of excitable waves (STEN-CEN waves). If this way of odelling cortical waves is correct, I understand that the present findings can be probably related to this concept. However, the line of argments is somewhat vague and hand-waving because the authors do not present an actual extension of their STEN-CEN model to incorporate electric fields.

We have worked on the discussion to do a better job of relating the STEN-CEN model with our findings. We did not extend our STEN-CEN model with Efs in the current manuscript. Still, there are numerous works on the extended STEN-CEN model with LEGI/BEN to explain how the excitable system is modulated by external chemical cues, the mechanism of which is similar to electrotaxis to some extent. We built up our discussion of how Efs perturb STEN-CEN based on the current chemotaxis models.

Discussion section

“Efs modulate the thresholds of the excitable wave system

Recent studies have shown that the cortical wave system can be described as a coupled signal transduction and cytoskeletal excitable network. Based on both simulation and experimental studies (Bhattacharya et al., 2020; Miao et al., 2017), it has been shown that the wave ranges, durations, and speeds are determined by the local threshold of activation, which in turn are regulated by the relative levels of activators and inhibitors (Miao et al., 2017, 2019).

Our quantification shows that guided waves become larger, faster, and more persistent in an EF (Figure 2), indicating that the excitable system is closer to its threshold for activation (Miao et al., 2019). This effect may arise from enhanced positive feedback, reduced negative feedback, or both. We further find that wave nucleation is enhanced at the cell front and suppressed at the back (Figure 4a, b). This subcellular inhomogeneity is consistent with a biased excitable network framework (Iglesias and Devreotes, 2012; Meinhardt, 1999; Tang et al., 2014; Xiong et al., 2010), which was added to the STEN-CEN model to introduce an internal spatial gradient in the local threshold of wave initiation, akin to cell polarity.

Local excitation and global inhibition (Xiong et al., 2010), LEGI, schemes have effectively recreated the features of both fast directional sensing and stable polarity in response to chemical signals, which can lead to robust biased excitable network. Both directional sensing and stable polarity can lead to a robust biased excitable network. For chemical signals, the directional response from PIP3 occurs within seconds, whereas the establishment of stable polarity usually requires many minutes. However, based on our analysis, establishing both directional response (Figure 3) and polarity (Figure 4) in response to Efs requires 5 to 10 min. It is worth noting that PIP3 waves also sense Efs on a time scale of minutes (Figure 1 —figure supplement 1). Our observation suggests that Efs act on the polarity establishment rather than directional sensing. This hypothesis is supported by a recent study showing that G-protein-coupled receptors (GPCRs), which are the regulator in the LEGI model for D. Discoideum that allows for sensing chemoattractant on timescales of seconds, are not essential for electrotaxis (Zhao et al., 2002).”

For example, I am not sure if waves necessarily will become “larger, faster, and more persistent” if the excitable system is closer to its activation threshold. Perhaps the authors could demonstrate this based on their existing STEN-CEN framework?

We now clarify that previous studies combining simulations and experiments have shown that the thresholds of the excitable system determine wave properties. Please refer to the last section discussion for more details.

Line 340: it is not clear to me why "the coexistence of waves with different behaviors in different portions of the cell provides powerful evidence that cortical waves act as direct mediators of EFs." What is meant by "direct mediators"?

We have rephrased the sentence to make it clearer.

Edited text

“The different response times on the subcellular level due to different wave behaviors (Figure 4 —figure supplement 2) provide strong evidence that cortical waves act as direct mediators of EFs. Waves on different planes are similar in composition but are impacted differently by the EF. We observed fast switching of wave directions in the basal plane near the ridged substrate and slower turning of the waves in the dorsal plane within the same cell, indicating that the direction of waves is controlled locally by external cues (Figure 3 —figure supplement 1).”

Earlier (line 242) you say that it is the polarized intracellular environment that causes the spatial inhomogeneity in the response to EFs. So aren't the waves rather "indirect mediators" of the EFs? Please explain.

Based on our data, we showed that there were both fast, local responses and slow, global responses to EFs (Figure 4). We showed that the polarized intracellular environment caused slow global responses (Figure 4). In contrast, our analysis on single-wave dynamics showed that waves themselves displayed faster response than cellular level response (Figure 4 —figure supplement 2). These two results suggest that waves themselves sense EFs rapidly but that polarity also interacts with wave dynamics and leads to slower global rearrangement. We have included the Discussion section in response to major comment 5 from reviewer 3. Please see the third paragraph in that Discussion section for the actual edits.

References:

There is quite a bit of literature on actin waves in D. discoideum. In addition to Ref. 8 which addresses oversized cells, I suggest that you give more credit to earlier work by Gunther Gerisch and others, also with respect to possible functions of actin waves in motility, division etc.

Thank you for the suggestion. We have edited our reference and added more references about actin waves driving migration and division.

Edited text

“Actin polymerization, coordinated with its associated signaling molecules, self-organizes into microscale spatial regions that travel as waves across plasma membranes. These waves drive various cell behaviors, such as migration and division (Bhattacharya et al., 2019; Bretschneider et al., 2009; Flemming et al., 2020; Gerhardt et al., 2014; Gerisch, 2010).”

Reviewer #3 (Recommendations for the authors):

The big picture summary is found in the public review. Here, we list the specific comments we have that are needed to strengthen the work for eventual publication. I have kept experimental requests to a minimum, and indicated the points where they are discussed with an (X) symbol.

1. (X) Lack of experimental methods clarity. In a field as confusing as electrotaxis/galvanotaxis, it is not acceptable to simply write in the methods: "20V/cm constant EF fields were applied." How were they applied? Which specific approach was deployed and what did the chamber look like, and how might the choice of system have affected the results?

We made a new supplemental figure (Figure 2 —figure supplement 1) to illustrate our experiment setup. This device is optimized for easy assembly and minimal leakage based on a previously published protocol paper (Yang et al., 2014).

20V/cm is a very high stimulus for injecting charge into an aqueous electrolyte medium--surely this would produce hydrolysis and pH changes unless this is controlled for? All of these concerns can be addressed by a proper discussion of the methodology.

We isolated the cell media from the electrodes using agar bridges to avoid electrochemical products and pH changes. Please check the new figure supplement Figure 2 —figure supplement 1 for the setup.

Edited method section

Electrotaxis experiments

“We 3D-printed electrotaxis chambers (Figure 2 —figure supplement 1) with dimensions of 20 mm × 5 mm × 0.25 mm and composed of a clear resin using a Formlabs Form2 3D-printer. Agar bridges were used to isolate cell media from electrodes to minimize electrochemical products and pH changes. 20 V/cm constant EFs were applied. Time-lapse images of the phase-contrast channel and the RFP/GFP channel were recorded using PerkinElmer spinning-disk microscope at a frame rate of 0.1 frames/s (Yokogawa CSU-X1 spinning-disk scan head (5000 rpm)) with Hamamatsu EMCCD camera and Volocity analysis software.”

However, this reviewer feels two experimental details (X) are required here. (1) Why was 20 V/cm chosen, and how does the cortical wave phenotype vary with field strength? What happens if a lower or higher field strength is used? It seems like these results would greatly help bolster the STEN-CEN framework and clarify the input-output aspects.

We chose 20 V/cm because we found the dynamical range of electrotaxis was narrow: we have tried lower voltages and found giant cells respond to EFs in the range of 15 V/cm to 20 V/cm. Higher field strengths (e.g., 35 V/cm) damaged cells.

Edited result section

“We found that giant cells respond to a narrow range (15 V/cm to 20 V/cm) of EF amplitudes (Video 7 and Video 8), and that higher voltage (35 V/cm) damaged cells.”

(2) Can the authors validate that they controlled well for pH changes due to electrolysis, or might this be an underlying mechanism?

We isolated the cell media from the electrodes using agar bridges to avoid electrochemical products and pH changes. Please check the new figure supplement Figure 2 – supplement figure 1 for the setup.

2. Statistics. Many figure panels are missing error bars, and there are very few statistical tests or direct comparisons to help understand how different given phenotypes actually are or how representative the presented data are. Figures 2b,f ; 3c,d; and 4b seem like obvious cases where more discussion of the stats is needed. Similarly, it was often unclear how many samples were being compared in those same panels. A clearer discussion in the text, captions, methods, and those figure panels emphasizing how many explicit experimental replicates and explicit individual cells analyzed would go a long way towards clarifying that these data are truly representative. Control cases were also lacking in a number of figures (see Figure discussion below).

Thank you for bringing up this concern. We have added more precise statistical analysis to the panels you mentioned and clarified the number of samples/independent experiments, the statistical methods in the figure captions, and method sessions (Please see the specific locations in the following ‘figure specific comments’). We also added the control cases in the figures.

3. The STEN-CEN framework was exciting (pun intended), but also didn't seem to coalesce into a clear finding. It's a great idea to try to link these findings to an excitable system with known dynamics, but STEN, CEN, BEN and other acronyms were frequently deployed without obvious quantitative connections to the data itself, at least in the sense that non-specialist readers such as myself would be able to follow this. Given the centrality of STEN-CEN to the introduction and claims in the discussion, I think the authors need to do better represent the importance and validity of STEN-CEN here.

Thank you for the suggestion. We now explain the relevance of the STEN-CEN model in the introduction by explaining prior studies of the Devreotes group that used the model to explain cell protrusions and cell migration. We then introduce BEN/LEGI in the discussion, where it becomes necessary to make sense of our results.

We also revised the discussion to relate our results in Figure 2 and Figure 4 more closely to the STEN-CEN model. Please find the Discussion section in the previous major comment 3 from reviewer 2.

Edited introduction section related to STEN-CEN

“Actin polymerization, coordinated with its associated signaling molecules, self-organizes into microscale spatial regions that travel as waves across plasma membranes. These waves drive various cell behaviors, such as migration and division (Bhattacharya et al., 2019; Bretschneider et al., 2009; Flemming et al., 2020; Gerhardt et al., 2014; Gerisch, 2010). The wave system can be described as a coupled signal transduction excitable network – cytoskeletal excitable network (STEN-CEN) (Devreotes et al., 2017; Miao et al., 2019). STEN-CEN has the characteristics of an excitable system, including exhibiting an activation threshold for wave initiation and experiencing refractory periods. It has been shown that the STEN-CEN wave properties dictate protrusion properties (Miao et al., 2019). Tuning the activity levels of key components in STEN-CEN changes wave patterns, which leads to the transition of protrusion profiles. An activator/inhibitor, reaction/diffusion system model successfully recapitulates the experimental results (Bhattacharya et al., 2020; Bhattacharya and Iglesias, 2018). For simplicity, here we will refer to STEN-CEN waves as cortical waves.”

I understand that Figure 1 shows 'STEN-CEN' dynamics, but most of that discussion took the form of ("see Figure 1b 150s-200s") and it wasn't really clear to me what I should be looking for in these panels. If Figure 1 is the place where STEN-CEN is validated as being relevant, it would be great to see something a little clearer in the text, and in the figure emphasizing the specific characteristics we should focus on.

We edited the text and captions related to figure 1 to highlight the features of interest of the excitable system, that is, wave duration, wave sizes, and the existence of refractory periods. We also worked on the related discussion paragraphs.

Edited Result Section related to the key features of STEN-CEN

“In giant cells, multiple waves were initiated randomly and propagated radially across the basal membranes (Figure 1b and Video 2). CEN is driven by STEN, but has a substantially shorter characteristic timescale. Thus, PIP3 waves displayed band-like shapes, whereas F-actin appeared across the bands with higher levels at the rims of PIP3 waves (Miao et al., 2019). As shown in Figure 1b, colliding waves did not cross, but instead rotated by 90° (Figure 1b, 150 s – 200 s). This behavior is suggestive of a refractory period following excitation, which is a hallmark of an excitable system. On nanoridges, the giant cells generated multiple, quasi-1D patches of F-actin and PIP3 with shorter lifetimes than on flat surfaces (Figure 1c and Video 3). Some waves formed and propagated for a short distance (Line 2 in Figure 1c), whereas others formed and then quickly dissipated (Line 3 in Figure 1c). The wave dissipation can be explained in terms of an excitable system with lateral inhibition, in which the dispersion of the inhibitor is faster than that of the activator. Thus, the waves eventually dissipate due to the spatial accumulation of the inhibitor. Prior studies have shown that in this situation, the excitable system threshold determines the wave duration (Bhattacharya et al., 2020; Ermentrout et al., 1984). As was the case on flat surfaces, 1D patches occurred throughout the basal surfaces on ridges, and thus were independent of cell motion.”

4. The nano-ridges seemed almost superfluous until the end. The nano-ridges are quite clever, but their introduction and discussion of their importance seemed a bit scattered, despite their being involved in each figure. I would have appreciated a clearer discussion of why this method was necessary compared to just the smooth surface data. Esotaxis is interesting, but it wasn't clear that it was necessary for most of the claims here, although I can see how it was useful.

We have revised the introduction to clarify that nanotopography is a tool for us based on our extensive prior studies but is not the main focus of the current work (Edited text 1).

Edited introduction section related to nanotopography

“We further use nanotopography to alter the waves’ spatial structures and characteristic timescales. Upon contact with nanotopography, cells produce quasi-1D wave patches. The phenomenon of guided actin polymerization by nanotopography is known as esotaxis (Driscoll et al., 2014), which has been investigated in detail (Ketchum et al., 2018; Lee et al., 2020). There are several advantages of incorporating nanotopography in our study. First, these waves persist for a shorter time on nanotopography than on flat surfaces, enabling us to investigate whether wave systems with different characteristic timescales respond to EFs differently. Second, waves on ridged surfaces have shorter lifetimes than those on flat surfaces, and thus only propagate in local regions of giant cells. Therefore, nanotopography allows us to distinguish between local and global mediation of the EF response.”

Kudos for the detailed methods section here. One experimental question--did the use of PDMS here affect dicty motion relative to migration on glass/plastic in the flat surface cases?

We now clarify that PDMS is the mold for the final replicas we used. We added a paragraph describing how we fabricate nanotopography from the PDMS mold. The material for nanotopography is an acrylic resin, and all the flat surfaces we used in this study are also made of the same resin.

Methods section

“The mold was used to produce replicas of the original pattern. A drop of the same acrylic resin was placed on the patterned area of the PDMS mold, and then an acrylate-functionalized glass coverslip was pressed firmly on top, spreading the sandwiched drop. Tape secured this system in place. The resin was cured for a total of 5 min under a UV lamp (Blak-ray), producing a polymer film. It should be noted that the PDMS mold is the negative relief pattern of the structure made using MAP. Therefore, samples (or replicas) of the original pattern could be produced on a relatively large scale with this method. The replicas were soaked in ethanol for at least 12 h before use in the cell studies. We fabricated flat surface samples by using a PDMS mold with a smooth surface.”

5. (X) Biological claims and mechanisms. This is a nice biophysics paper-I do not think the authors need to go further down the rabbit hole and prove a specific molecular mechanism. However, I do think they need to be a little more careful about some of the claims and to also spend more time discussing why they think these waves are forming here and what might be nucleating the waves upstream of them. Speculation would OK here, and I felt the paper sort of overemphasized the dynamics of the waves without really musing on how the sausage was made. More discussion would help here. That said, there is one biological experiment that seems easily within reach and, unless I misread the paper, is missing. The authors stated up front that the co-localization of F-actin and PIP3 in control data meant that only F-actin needed to be evaluated for the actual stimulation experiments. It was actually unclear from Figure 1 how strong that co-localization actually was (see below), but more to the point-I think the authors need (X) to show even a single supplemental figure clearly demonstrating that this overlap of PIP3/F-actin still holds in the stimulated case as well as the control rather than simply assuming that it does from looking only at the control data. Apologies if I misinterpreted the data and this is already accounted for; it just wasn't clear to me and I'd like to see an analogous panel to some of the data currently in Figure 1 except for stimulated cells.

Thank you for the suggestion. We have carried out new experiments to image both LimE and PHcrac in the presence of EFs, and significantly revised the Discussion sections. We included a supplementary figure (Figure 1 – supplement figure 1) to show PIP3 and F-actin are still coupled in the presence of EFs.

Result section related to imaging both PIP3 and F-actin

“PIP3 activity was coordinated with F-actin activity (Profiles in Figure 1a, 1b, and 1c), both in the absence (Figure 1) and presence of an EF (Figure 1 —figure supplement 1, Video 6). Therefore, in the experiments described below, we only monitored F-actin activity.”

Edited Discussion section

“EFs guide cortical wave dynamics

Previous studies have suggested that the basal cortical waves in D. discoideum are insensitive to external chemotactic gradients, whereas “pseudopods” at other regions in the same cells can be guided (Lange et al., 2016). This conclusion is surprising because the biochemical events traveling with the waves are the same as those occurring on pseudopods, and pseudopods with the dorsal cups on the same cells do respond to chemoattractants. Also, similar cortical waves in human mammary epithelial cells can be guided effectively by epidermal growth factors (Zhan et al., 2020). Additional input from the greater contact of giant D. discoideum cells with the surface may outweigh the effect of applied chemical gradients on the basal waves. Other studies have shown that single cells can integrate combinations of external chemical and mechanical stimuli.

Our work shows that in giant cells, waves of both F-actin polymerization (Figure 3) and its upstream regulator PIP3 (Figure 1 —figure supplement 1) are indeed guided by EFs. These biased biochemical and biomechanical events lead to more protrusions at the cell front than at the cell back, thus driving cell migration (Figure 2 —figure supplement 2). The development of the biased wave activities takes ~10 min following the introduction of an EF (Figure 2a and Figure 3), which is much slower than the timescale of surface-receptor-regulated chemotaxis. The high resistance of the cell membrane limits the effects of EFs on intracellular components, but EFs may act on the charged lipids and molecular clusters. Thus, we suspect that the slow response results from the electrophoresis of the charged membrane components involved in wave formation, which has a characteristic time scale of 5 to 10 min (Allen et al., 2013; McLaughlin and Poo, 1981).

We further explored the dynamics in response to EF reversal at the subcellular level using nanotopography (Figure 4). We observed that the new waves are induced to propagate towards the current cathode within 2 to 3 min (Figure 4e and Figure 4 —figure supplement 2), suggesting that waves themselves can adapt quickly to the changing electrical environments. Because we only observed the fast adaptation on ridged surfaces, this phenomenon may be related to the shorter wave lifetimes on nanoridges than on flat surfaces. A short lifetime allows waves to be nucleated at a higher rate on the nanoridges, leading to a rapid directional response. During this process, the EF may regulate the wave nucleation through locally changing specific charged lipids, ion fluxes, or local pH gradients (Crevenna et al., 2013; Frantz et al., 2008; Köhler et al., 2012; Martin et al., 2011; Zhou and Pang, 2018).”

Figure specific comments:

Figure 1. Why is red consistently 'outlining' green, and how does that support the co-localization claim?

This phenomenon of red outlining green is a characteristic observation in prior studies of this system and can be understood through the STEN-CEN model. We have added more explanation about STEN-CEN in the Results section related to figure 1.

Result section related to Figure 1

“In giant cells, multiple waves were initiated randomly and propagated radially across the basal membranes (Figure 1b and Video 2). CEN is driven by STEN, but has a substantially shorter characteristic timescale. Thus, PIP3 waves displayed band-like shapes, whereas F-actin appeared across the bands with higher levels at the rims of PIP3 waves (Miao et al., 2019).”

Figure 2d/e were a little hard to assess in the sense that going from 0-20V/cm seems like it should produce a drastic change, but the visualizations seem relatively similar, so I think I'm not reading these right. Can these be clarified in the figure or caption?

20 V/cm did produce a drastic change, as shown in Figure 2a, in the sense that larger waves are generated. However, because one large wave involves much more actin polymerization than one small wave, the number of large waves is limited compared to the number of small waves. In Figure 2d/e, each dot represents a wave, and it is expected that the pattern is determined by the many small waves (red region). If you compare the areas representing large wave sizes (yellow-blue regions), there are indeed more waves in a dc EF. To make it clearer, we have also included the average of areas and dimensions in the text related to Figure 2d/e.

Edited text related to Figure 2d/e

“Density scatter plots of both dimensions exhibit elliptical contours (Figure 2d), suggesting that nanotopography constrains wave growth. With an EF parallel to the ridges, the waves broadened in both directions (Figure 2d). The average increases in wave dimension parallel and perpendicular to the ridges were 20% and 13%, respectively, and the average increase in wave area was 44%.”

Figure S4-I did not follow this discussion. How do the authors use the data in S4 to show that polarized intracellular environments were key vs. external fields? I was also confused about that claim because I thought it was well established that the field must act on the extracellular membrane components because it can't penetrate the plasma membrane (re: impedance), so I wasn't sure what this discussion point or figure were addressing.

Because the giant cells span ~50 mm, we wanted to ensure the intracellular bias was caused by the EF, not the absolute potential relative to the ground. The result was expected. Hence we only included the analysis as supplementary material.

Edited result section related to Figure S4 (current version Figure 4 – supplement figure 1)

“We also measured the wave properties in the single cells scattered throughout the field of view but did not observe a corresponding gradient of wave properties among single cells closer to the cathode versus the cells closer to the anode. This result indicates that the spatial inhomogeneity shown in Figures 4a, b was caused by the EF rather than by the absolute electrical potential relative to the ground (Figure 4 —figure supplement 1).”

Edited Figure 4 – supplement figure 1 caption

“The spatial inhomogeneity of wave properties could be caused either by the EF or by the external electrical potential gradient relative to the ground. To explore these scenarios, we quantified the waves in single cells surrounding the giant cell in the field of view, where the center of the field was defined as the origin. In contrast to the giant cells, these single cells are scattered throughout the field of view but are not large enough for the potential gradient to create significant intracellular polarization. Thus, if the spatial inhomogeneity is caused by the external electrical potential gradient, we would observe a gradient of wave properties from single cells located in the region between -50 µm and 50 µm. a. A limE image. Single cells are highlighted with blue circles. b, c. Density scatter plots of wave location vs. wave area for single cells (highlighted by blue circles in a) in the absence (b) and the presence (c) of EF. Unlike in giant cells (Figure 4a, 4b), the wave areas in single cells are spatially homogeneous. The ratio of mean wave areas in the regions nearer the cathode (location > 0) to those in regions farther from the cathodes (location < 0) was calculated for both single cells and giant cells. In giant cells, this ratio increases from 0.85 to 2.00 with an EF (Figure 4a), whereas for single cells, the ratio is almost unchanged (1.08 without an EF and 0.98 with an EF). This analysis was based on the experiments from 4 different days.”

Figure 4. Lots going on in this figure! 4b-how many cells, what are the statistics, how representative is this? 4f-it seems like many cells must have been analyzed, so I'm curious why only 5 are shown here and it's not clear if the p value means this is a strong conclusion or not given only 5 datapoints.

We have edited the caption of figure 4 for clarity and to include information on the number of cells. We have several hundreds of waves in the 4a-4c wave-basis analysis, but we only have 5 giant cells for 4e-4f cell-basis analysis.

Edited Figure 4 caption

“Figure 4. Spatial inhomogeneity of the response to EFs on nanoridges. a. Density scatter plots of the wave area vs. x position of the wave relative to the cell center. Nanoridges and EF are orientated in the x-direction. The difference of x coordinates of cell center and wave location was calculated, then the value was further normalized by the cell radius. Each point represents a wave, and all the points were collected from 5 independent experiments. The left plot is for a period in which there was no EF (Nwave = 296), and the right plot is for a period in which there was a 20 V/cm EF, during which the cells exhibited steady directional migration (Nwave = 224). For each experiment without an EF, the EF was always turned on several minutes later. Thus, we defined the direction in which cathode was located in the presence of an EF as the positive direction in the absence of an EF. The color code corresponds to the density of points. b. Average wave area in sub-cellular regions. The points in a were sectioned, based on their x position relative to the cell center (normalized by cell radius) at a bin size of 0.25 (8 sections in total from -1 to 1), and calculated the average wave area in each section. c. Changes in actin waves' spatial distribution in response to EF reversal; data from 6 independent experiments. The color of each plot is coded according to the timeline displayed at the bottom of the panel. P2-P5: The EF was reversed, and cells gradually developed polarization towards the new cathode. The number of waves in each plot: Np2 = 272, Np3 = 277, Np4 = 193, Np5 = 246 d. A schematic illustrating the old and new fronts of giant cells when the EF was reversed. e. Time stacks of orientation distributions of optical-flow vectors at an old front and a new front. The EF was reversed from the cathode being at the right (0) to the cathode being at the left (p) at 0 min. f. Comparisons of response time between new fronts (green) and old fronts (orange) from multiple experiments (Ncell = 5). The P-value was calculated using a pairwise t-test at the 5% significance level. g. Cartoon illustrating different time scales of local wave propagation and global rearrangement of STEN-CEN thresholds, in response to EF reversal.”

Reference mentioned in the response

Yang, H. Y., La, T. D., and Isseroff, R. R. (2014). Utilizing custom-designed galvanotaxis chambers to study directional migration of prostate cells. Journal of Visualized Experiments, 94, 1–8. https://doi.org/10.3791/51973

https://doi.org/10.7554/eLife.73198.sa2

Article and author information

Author details

  1. Qixin Yang

    1. Department of Physics, University of Maryland, College Park, United States
    2. Institute for Physical Science and Technology, University of Maryland, College Park, United States
    Contribution
    Data curation, Formal analysis, Investigation, Visualization, Writing - original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5349-6992
  2. Yuchuan Miao

    Department of Cell Biology, Johns Hopkins University, Baltimore, United States
    Contribution
    Investigation, Visualization, Writing – review and editing
    Competing interests
    No competing interests declared
  3. Leonard J Campanello

    1. Department of Physics, University of Maryland, College Park, United States
    2. Institute for Physical Science and Technology, University of Maryland, College Park, United States
    Contribution
    Software
    Competing interests
    No competing interests declared
  4. Matt J Hourwitz

    Department of Chemistry & Biochemistry, University of Maryland, College Park, United States
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  5. Bedri Abubaker-Sharif

    Department of Cell Biology, Johns Hopkins University, Baltimore, United States
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5003-7934
  6. Abby L Bull

    1. Department of Physics, University of Maryland, College Park, United States
    2. Institute for Physical Science and Technology, University of Maryland, College Park, United States
    Contribution
    Writing – review and editing
    Competing interests
    No competing interests declared
  7. Peter N Devreotes

    Department of Cell Biology, Johns Hopkins University, Baltimore, United States
    Contribution
    Conceptualization, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
  8. John T Fourkas

    1. Institute for Physical Science and Technology, University of Maryland, College Park, United States
    2. Department of Chemistry & Biochemistry, University of Maryland, College Park, United States
    Contribution
    Conceptualization, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4522-9584
  9. Wolfgang Losert

    1. Department of Physics, University of Maryland, College Park, United States
    2. Institute for Physical Science and Technology, University of Maryland, College Park, United States
    Contribution
    Conceptualization, Supervision, Writing – review and editing
    For correspondence
    wlosert@umd.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1792-7860

Funding

Air Force Office of Scientific Research (FA9550-16-1-0052)

  • Qixin Yang
  • Yuchuan Miao
  • Leonard J Campanello
  • Matt J Hourwitz
  • Bedri Abubaker-Sharif
  • Abby L Bull
  • Peter Devreotes
  • John T Fourkas
  • Wolfgang Losert

National Institute of Health (T32 GM136577)

  • Bedri Abubaker-Sharif

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Q Qing and M Zhao for discussions.

Senior Editor

  1. Jonathan A Cooper, Fred Hutchinson Cancer Research Center, United States

Reviewing Editor

  1. Alphee Michelot, Institut de Biologie du Développement, France

Publication history

  1. Preprint posted: June 8, 2021 (view preprint)
  2. Received: August 19, 2021
  3. Accepted: March 8, 2022
  4. Version of Record published: March 23, 2022 (version 1)

Copyright

© 2022, Yang et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Qixin Yang
  2. Yuchuan Miao
  3. Leonard J Campanello
  4. Matt J Hourwitz
  5. Bedri Abubaker-Sharif
  6. Abby L Bull
  7. Peter N Devreotes
  8. John T Fourkas
  9. Wolfgang Losert
(2022)
Cortical waves mediate the cellular response to electric fields
eLife 11:e73198.
https://doi.org/10.7554/eLife.73198

Further reading

    1. Cell Biology
    Saskia-Larissa Jauch-Speer et al.
    Research Article Updated

    The proinflammatory alarmins S100A8 and S100A9 are among the most abundant proteins in neutrophils and monocytes but are completely silenced after differentiation to macrophages. The molecular mechanisms of the extraordinarily dynamic transcriptional regulation of S100a8 and S100a9 genes, however, are only barely understood. Using an unbiased genome-wide CRISPR/Cas9 knockout (KO)-based screening approach in immortalized murine monocytes, we identified the transcription factor C/EBPδ as a central regulator of S100a8 and S100a9 expression. We showed that S100A8/A9 expression and thereby neutrophil recruitment and cytokine release were decreased in C/EBPδ KO mice in a mouse model of acute lung inflammation. S100a8 and S100a9 expression was further controlled by the C/EBPδ antagonists ATF3 and FBXW7. We confirmed the clinical relevance of this regulatory network in subpopulations of human monocytes in a clinical cohort of cardiovascular patients. Moreover, we identified specific C/EBPδ-binding sites within S100a8 and S100a9 promoter regions, and demonstrated that C/EBPδ-dependent JMJD3-mediated demethylation of H3K27me3 is indispensable for their expression. Overall, our work uncovered C/EBPδ as a novel regulator of S100a8 and S100a9 expression. Therefore, C/EBPδ represents a promising target for modulation of inflammatory conditions that are characterized by S100a8 and S100a9 overexpression.

    1. Biochemistry and Chemical Biology
    2. Cell Biology
    Haikel Dridi et al.
    Research Article Updated

    Age-dependent loss of body wall muscle function and impaired locomotion occur within 2 weeks in Caenorhabditis elegans (C. elegans); however, the underlying mechanism has not been fully elucidated. In humans, age-dependent loss of muscle function occurs at about 80 years of age and has been linked to dysfunction of ryanodine receptor (RyR)/intracellular calcium (Ca2+) release channels on the sarcoplasmic reticulum (SR). Mammalian skeletal muscle RyR1 channels undergo age-related remodeling due to oxidative overload, leading to loss of the stabilizing subunit calstabin1 (FKBP12) from the channel macromolecular complex. This destabilizes the closed state of the channel resulting in intracellular Ca2+ leak, reduced muscle function, and impaired exercise capacity. We now show that the C. elegans RyR homolog, UNC-68, exhibits a remarkable degree of evolutionary conservation with mammalian RyR channels and similar age-dependent dysfunction. Like RyR1 in mammals, UNC-68 encodes a protein that comprises a macromolecular complex which includes the calstabin1 homolog FKB-2 and is immunoreactive with antibodies raised against the RyR1 complex. Furthermore, as in aged mammals, UNC-68 is oxidized and depleted of FKB-2 in an age-dependent manner, resulting in ‘leaky’ channels, depleted SR Ca2+ stores, reduced body wall muscle Ca2+ transients, and age-dependent muscle weakness. FKB-2 (ok3007)-deficient worms exhibit reduced exercise capacity. Pharmacologically induced oxidization of UNC-68 and depletion of FKB-2 from the channel independently caused reduced body wall muscle Ca2+ transients. Preventing FKB-2 depletion from the UNC-68 macromolecular complex using the Rycal drug S107 improved muscle Ca2+ transients and function. Taken together, these data suggest that UNC-68 oxidation plays a role in age-dependent loss of muscle function. Remarkably, this age-dependent loss of muscle function induced by oxidative overload, which takes ~2 years in mice and ~80 years in humans, occurs in less than 2–3 weeks in C. elegans, suggesting that reduced antioxidant capacity may contribute to the differences in lifespan among species.