Persistent cell migration emerges from a coupling between protrusion dynamics and polarized trafficking

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Version of Record published: March 29, 2022 (This version)
Accepted Manuscript published: March 18, 2022 (Go to version)
Accepted: February 26, 2022
Received: April 11, 2021
Preprint posted: March 22, 2021 (Go to version)

1. Of interest
Regulation of defective mitochondrial DNA accumulation and transmission in C. elegans by the programmed cell death and aging pathways

Sagen Flowers, Rushali Kothari ... Joel H Rothman

Research Article Oct 2, 2023
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Abstract

Migrating cells present a variety of paths, from random to highly directional ones. While random movement can be explained by basal intrinsic activity, persistent movement requires stable polarization. Here, we quantitatively address emergence of persistent migration in (hTERT)–immortalizedRPE1 (retinal pigment epithelial) cells over long timescales. By live cell imaging and dynamic micropatterning, we demonstrate that the Nucleus-Golgi axis aligns with direction of migration leading to efficient cell movement. We show that polarized trafficking is directed toward protrusions with a 20-min delay, and that migration becomes random after disrupting internal cell organization. Eventually, we prove that localized optogenetic Cdc42 activation orients the Nucleus-Golgi axis. Our work suggests that polarized trafficking stabilizes the protrusive activity of the cell, while protrusive activity orients this polarity axis, leading to persistent cell migration. Using a minimal physical model, we show that this feedback is sufficient to recapitulate the quantitative properties of cell migration in the timescale of hours.

Editor's evaluation

It has previously been suspected that secretion supports cell migration in human cells. This study proposes a physical model and offers various results that elegantly link the activation of a small GTPAse at the leading edge with the re-organisation of the secretory pathway, creating a feedback loop that allows persistence of direction. Hopefully, the simple physical model will serve as a foundation to include more regulatory loops in the conceptualisation of cell migration.

https://doi.org/10.7554/eLife.69229.sa0

Introduction

Cell migration is involved in many processes such as development, invasion, wound healing, or immune response (Vicente-Manzanares and Horwitz, 2011). There is an impressive variety of modalities by which cells migrate, including mesenchymal or amoeboid type of movement for which single cells or a group of cells (Shellard and Mayor, 2019) use different propulsive forces for displacement (Othmer, 2018). Regardless of the propulsive force or single/collective mode of migration, cells polarize to move (Rappel and Edelstein-Keshet, 2017). This is characterized by an asymmetric shape and distribution of proteins, organelles, and lipids, as well as differential activities at the two extreme sides of the cell (Vaidžiulytė et al., 2019). This polarity allows cells to spatially segregate propulsive and contractile forces in order to move their body forward. In the context of mesenchymal cell migration, the polarity axis of cells is specified by a protruding front and a retracting back (Ebnet, 2015; Ridley et al., 2003). On the contrary, when cells are not polarized, they present several protruding regions along their contour and barely move (Petrie et al., 2009). Several mechanisms have been proposed to explain the long range coordination of front and back activities, from reaction-diffusion of signaling molecules (Jilkine et al., 2007), cytoskeleton template dynamics (Gan et al., 2016; Maiuri et al., 2015; Prentice-Mott et al., 2016; Wang et al., 2013), mechanical signals such as membrane tension (Houk et al., 2012), to contractility (Cramer, 2013; Schuster et al., 2016; Vicente-Manzanares et al., 2011; Yam et al., 2007). Eventually, numerous studies have highlighted the role of retrograde trafficking (Shafaq-Zadah et al., 2016) and directed secretion from the Golgi complex in sustaining persistent migration (Hao et al., 2020; Yadav and Linstedt, 2011; Yadav et al., 2009). However, it is not completely understood how these different mechanisms can be combined and what are their respective roles in allowing cells to maintain a stable polarity while migrating.

In the case of mesenchymal migration, cells move thanks to the sum of local protrusive activity (Yamao et al., 2015), and persistent migration relies on lamellipodial persistence (Krause and Gautreau, 2014). Protrusions are initiated and controlled by the small RhoGTPases (Jaffe and Hall, 2005; Lawson and Ridley, 2018). These signaling proteins are engaged in spatiotemporal patterns of activity (Machacek et al., 2009; Fritz and Pertz, 2016; Pertz, 2010), thanks to a large set of activators and deactivators, GEFs (Guanine nucleotide exchange factors) and GAPs (GTPase-activating proteins) (Bos et al., 2007; Müller et al., 2020). Among the RhoGTPases, Cdc42 has been recognized to be integrated into an excitable signaling network that can spontaneously polarize (Yang et al., 2016). Cdc42 crosstalks with polarity proteins (Etienne-Manneville, 2008; Iden and Collard, 2008) and with the cytoskeleton (Bear and Haugh, 2014). Notably, persistently migrating mesenchymal cells present a sustained and polarized internal organization, which can be viewed as an ‘internal compass’. This compass corresponds to the polarity axis that can be represented by the axis from the nucleus to the centrosome or the associated Golgi complex (Elric and Etienne-Manneville, 2014; Luxton and Gundersen, 2011). In wound scratch assay, the Golgi complex reorients in front of the nucleus (Etienne-Manneville, 2006). Similarly, the centrosome reorients toward the leading edge during EMT (Epithelial-Mesenchymal Transition) (Burute et al., 2017). In other studies, the investigators have reported that the Golgi does not align with direction of migration at all (Uetrecht and Bear, 2009) or tends to be behind the nucleus when cells are studied on adhesive 1D lines (Pouthas et al., 2008). Thus, the role of the Golgi positioning and the internal compass in persistent migration remains to be clarified.

Based on pioneering work in yeast (reviewed in Chiou et al., 2017), cell polarity could be considered as an emergent property based on the coupling of high-level cellular functions, rather than being attributed to one specific pathway or to one single ‘culprit’ protein (Vaidžiulytė et al., 2019). Similarly, the emergence of persistency in cell migration could also rely on the coupling of high-level cellular functions. In the present work, we tested if the coupling between protrusion dynamics and internal cell polarity is present in mesenchymal cells and if this coupling could be sufficient to maintain persistent cell migration. For this, we quantified and manipulated the two subcellular functions described above at short and long timescales. Our experimental results were integrated into a minimal physical model that recapitulates the emergence of persistency from this coupling.

Results

Freely migrating RPE1 cells persistently protrude in front of the Golgi

First, we assessed the coupling between the internal polarity axis and cell protruding activity during persistent cell migration. We chose RPE1 cells which are known to have a reproducible internal organization (Schauer et al., 2010) and move persistently (Maiuri et al., 2012). To quantify the orientation of the internal polarity axis of the cell while migrating, we generated stable cell lines, with fluorescently labeled Golgi complex and nucleus. Rab6A fused to a GFP tag was overexpressed to follow the Golgi, and the nucleus was stained with Hoechst 33,342 (see Materials and methods). Cell contours were segmented in live by expressing an iRFP-fluorescent reporter anchored to the plasma membrane by a myristoylation motif. The live segmentation was employed to move the stage accordingly to the cell movement in order to keep the cell in the field of view (sup Figure 1A and Materials and methods). This experimental strategy let us image cells with high spatial resolution for up to 16 hr with a 5-min temporal resolution (Figure 1A and Figure 1—video 1). For each timepoint, we quantified the direction of movement by measuring the displacement of the center of mass of the cell from the segmented images (orange arrow Figure 1B). We quantified the direction of the internal polarity axis by taking the vector joining the centers of mass of the nucleus to the Golgi (black arrow Figure 1B). We then computed the angular difference between the two vectors and averaged it over all timepoints and over 17 cells. The distribution of the angular difference is sharply pointing toward zero (–10° ±33°, Figure 1C), showing that there is a clear alignment of the Nucleus-Golgi axis with the direction of migration in RPE1 cells.

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Persistent protrusions form in front of the Golgi complex.

(A) Snapshots of a representative migrating RPE1 cell at different timepoints tracked with a feedback routine in which the microscope stage follows a migrating cell (Figure 1—figure supplement 1) for 16 hr (cyan: myr-iRFP, yellow: GFP-Rab6A, blue: Hoechst 33342, trajectory overlaid in orange, microscope stage movement represented by an arrow, scale bar – 20 µm). (B) Full trajectory of a representative cell shown in (A) with Nucleus-Golgi (black dashed arrow) and direction of movement (orange dashed arrow) axes overlaid. (C) Polar histogram representing the averaged angle between Nucleus-Golgi axis and direction of movement (n = 17 cells). (D) Explanatory sketch of how a morphodynamic map of cell shape changes is computed. The contour of the cell is extracted and compared between frames and stretched out to a line representation, where the distance traveled by a point in the contour is represented (red color meaning protrusion, blue – retraction, black dashed arrow – Nucleus-Golgi axis). (E) Morphodynamic map of a representative cell (all maps in Figure 3—figure supplement 2) recentered to Nucleus-Golgi axis (black). X-axis represents time and Y-axis represents cell contour. (F) Average protrusion speed over time (n = 17 cells, dashed blue line - SD). X-axis represents average protrusion speed, and Y- axis represents cell contour with the midline corresponding to (E). Data used for C and F and related scripts can be found in Figure 1—source data 1.

Figure 1—source data 1 Data and analysis scripts with explanations for Figure 1 and its supplements.: https://cdn.elifesciences.org/articles/69229/elife-69229-fig1-data1-v2.zip
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Next, we computed morphodynamic maps of the cell contour, which allows the visualization of protruding activity over time (Machacek et al., 2009). We computed these maps by measuring the displacement of the cell contour between two consecutive timepoints and by color coding the displacement, from blue (retraction) to red (protrusion) (Figure 1D, and Materials and methods). All displacements along the cell contour (y-axis) were plotted as a function of time. Using the direction of movement as reference (midline on the y-axis) throughout the movement, we found that the protrusive activity is perfectly aligned with the direction of movement (sup Figure 1C and D). This showed that cell migration is indeed driven by protrusions. When the Nucleus-Golgi axis is used as reference (midline on the y-axis), the protrusive activity appeared to align well with this axis (Figure 1E). Averaging over time and over cells, there is indeed a sustained protrusive activity in front of the Golgi, whose speed is significantly higher than throughout the cell (Figure 1F). These results demonstrate that there is a strong correlation between the direction of protruding activity driving cell movement and the orientation of the polarity axis in RPE1 cells indicating a strong coupling of these activities in freely migrating RPE1 cells.

The Nucleus-Golgi axis does not predict the direction of migration, but aligns when cells move effectively

The correlation we observed does not imply a causal role of the internal polarity axis in driving the persistence of cell migration, because the Nucleus-Golgi axis may follow the direction of migration in a passive manner as a byproduct of cell morphological changes. Thus, we assessed its role by testing if cells start to move in a preferential direction along the given internal polarity axis (Figure 2A). For this, we employed the dynamic micropattern technique (van Dongen et al., 2013) that allows to release cells from a pattern. Cells are initially plated on round adhesive micropatterns coated with fibronectin surrounded by a repulsive PLL-PEG (poly(L-lysine)-poly(ethylene glycol)) coating. And 5 hr after plating, migration is initiated by adding BCN-RGD (bicyclo[6.1.0]nonyne-Arginine-Glycine-Aspartate) that renders the whole surface adhesive. Since the pattern is isotropic, there are no external cues to orient cell escape. We monitored cell movement by tracking the nucleus center of mass, and Nucleus-Golgi axis for 36 cells (Figure 2B, Figure 2—figure supplement 1, and Figure 2—video 1). During a first phase of ~5 hr cells remain on the pattern, and during a second phase they start to move out of it (Figure 2—figure supplement 2A). When we compared the orientation of the Nucleus-Golgi axis at t = 0 (addition of BCN-RGD) with the direction of escape, we found no correlation (Figure 2C). This result shows that the direction of escape is independent from the initial positioning of the Golgi, as previously suggested in the literature (Chen et al., 2013). However, we found a clear correlation between the direction of escape and orientation of the Nucleus-Golgi axis at the time of escape (Figure 2D). A detailed temporal analysis (Figure 2—figure supplement 2B) showed that both the Nucleus-Golgi axis and cell direction of motion start to align about 2 hr before the escape. Our analysis indicates that they are concomitantly required to initiate effective migration.

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Nucleus-Golgi axis and direction of movement align when a cell starts moving.

(A) Scheme of the dynamic micropatterns experimental design that is used to study the initiation of cell movement. A cell is confined on a round fibronectin pattern and after the addition of BCN-RGD is enabled to move outside and ‘escape’ the pattern (‘escape’ is defined to be the moment when the center of the cell nucleus is leaving the area of the pattern). (B) Representative RPE1 cell ‘escaping’ the pattern (transparent cyan: pattern, cyan dot: nucleus centroid, yellow: GFP-Rab6A, red dot: Golgi centroid, black dashed line: Nucleus-Golgi axis, orange dashed line: direction of movement, scale bar – 20 µm). (**C–D**) Polar histograms representing the angle between Nucleus-Golgi axis at the beginning of experiment (t = 0) (C) or at the time of ‘escape’ (t = ESCAPE) (D) and direction of movement when the cell moves out of the pattern (n = 36 cells). Data used for C and D and related scripts can be found in Figure 2—source data 1.

Figure 2—source data 1 Data and analysis scripts with explanations for Figure 2 and its supplements.: https://cdn.elifesciences.org/articles/69229/elife-69229-fig2-data1-v2.zip
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Disruption of microtubule dynamics abolishes persistence of migration on long timescales

Next, we addressed the role of the internal polarity axis on sustaining protrusion dynamics. Since microtubules (MTs) are known to play a major role in cell internal organization, we perturbed MT dynamics using low doses of Nocodazole (NZ), namely, 0.1 μM. This dose was sufficient to perturb MT dynamics without full disruption of MT network and did not impact cell viability during the experimental set-up (Figure 3—figure supplement 1 and Figure 3—videos 1 and 2). We monitored control and NZ treated cells for 16 hr using our previously described feedback routine to assess their migration properties. As seen from the corresponding morphodynamic maps, NZ treated cells still show protruding activity that, however, is less sustained and connected giving rise to separated patches of activity (Figure 3A–B and Figure 3—figure supplements 2 and 3). We measured the average protrusion speed for each single cell with or without treatment (Figure 3C) and found no significant difference, showing that the protrusive ability of NZ treated cells was not altered. As a consequence, the average instantaneous speeds of cells in a short 5 min time window are the same in both conditions (Figure 3D). Yet, the directionality ratio – defined as the cell displacement divided by the length of the cell trajectory – strongly differs, pointing to a difference in the directionality of movement (Figure 3E). Thus, NZ treated cells are less persistent than control cells, as directly observed from their trajectories (Figure 3F–G and Figure 3—video 1). We further quantified the persistence of migration by measuring the autocorrelation of direction of movement, which takes into account only the angle of direction of a moving cell and correlates it over time (Gorelik and Gautreau, 2014). The decay of this autocorrelation informs on the timescale over which cells randomize their direction of movement (Figure 3H). By fitting an exponential function on the autocorrelation curves of single cells, we extracted a characteristic persistence time of each cell (Figure 3I). Control cells are persistent over ~2.5 hr on average, whereas NZ treated cells are persistent over 20 min on average only. Taken together, our results showed that NZ treated cells are protruding as efficiently as control cells, but in a nonco-ordinated manner over the timescale of more than 20 min. It thus suggests that cell internal organization is required for long-term coordinated protruding activity and persistent cell migration.

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Low dose of Nocodazole (NZ) reduces persistence of migration.

(**A–B**) Representative morphodynamic maps of RPE1 cells freely moving on a fibronectin-covered coverslip in control condition (Ctrl) (A) and with NZ (0.1 µm) (B). (**C–E**) Average protrusion speed (C), average cell speed (D), and directionality ratio (E) compared in Ctrl and with NZ (Wilcoxon rank sum test, *p≤0.05, **p≤0.01, ***p≤0.001). (**F–G**) Trajectories of RPE1 cells in Ctrl (n = 17) (F) and with NZ (n = 14) (G) (trajectories plotted over 7 hr of experiment). (**H–I**) Direction autocorrelation (H), and persistence time (I) compared in Ctrl and with NZ (Wilcoxon rank sum test, *p≤0.05, **p≤0.01, ***p≤0.001). Data used for **C-I** and related scripts can be found in Figure 3—source data 1.

Figure 3—source data 1 Data and analysis scripts with explanations for Figure 3 and its supplements.: https://cdn.elifesciences.org/articles/69229/elife-69229-fig3-data1-v2.zip
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Persistent cells show sustained polarized trafficking from the Golgi complex to protrusions

The Golgi complex plays important roles in directed secretion of vesicles and cargos to the leading edge of migrating cells (Yadav et al., 2009). To assess the dynamics of Golgi-derived secretion, we followed synchronized secretion of collagen X from the ER to the plasma membrane using the Retention Using Selective Hooks (RUSH) assay. To better visualize the sites of collagen X arrival we combined RUSH with selective protein immobilization (SPI) on the coverslip via antibody capturing that shows secretion of collagen X at ~20 min after release from the ER (see Materials and methods) (Fourriere et al., 2019). Interestingly, although we could detect secreted cargos accumulating in the direction of the Nucleus-Golgi axis (in 6 out of 52 cells), (Figure 4—figure supplement 1A for representative examples), in 20 out of 52 cells, the accumulated cargo did not align with the Nucleus-Golgi axis, probably because we captured cells while turning (Figure 4A at 42 min when the cargo accumulates in the Golgi). Importantly, we observed the accumulation of secreted cargos toward the newly formed protrusions in all these cases (6 + 20 out of 52 cells in total) (Figure 4A at 1 hr, Figure 4—figure supplement 1A and Figure 4—video 1). To further follow constitutive Golgi-derived trafficking activity at a longer timescale, we used Rab6 as general marker for Golgi-derived secretion (Fourriere et al., 2019). We engineered a CRISPR knock-in cell line with an iRFP fluorescent protein fused to the endogenous Rab6A protein (see Materials and methods). Compared to these CRISPR knock-in cells (and wild-type [WT] hTERT-RPE1 cells), GFP-Rab6 overexpressing cells were more persistent (Figure 4—figure supplement 3), likely due to increased secretory activity in overexpressing cells. Using highly inclined and laminated optical sheet (HILO) microscopy, we could minimize the signal from the Golgi and enhance signal from the Rab6 vesicles (Figure 4B, top and Figure 4—video 1). Moreover, we further suppressed the signal from the Golgi complex by segmenting and masking it to quantify only cell trafficking. We performed the morphodynamic map analysis of cell protrusions (Figure 4C, top), in addition to a Rab6- trafficking map (Figure 4C, bottom). The latter was computed by measuring the average intensity along lines from the Golgi centroid to the cell contour (Figure 4B, bottom). The color code from blue (no Rab6-signal) to red (max Rab6-signal) of this trafficking map shows the hotspots of trafficking as a function of time. We found that the morphodynamic and trafficking maps correlate (Figure 4D) confirming a sustained trafficking to protrusions at the long timescale. By performing a temporal cross-correlation analysis, we observed a peak that occurs at a positive time lag of 19 ± 11 min indicating that protrusions precede trafficking. This delay is also obvious from the alignment of the morpho and trafficking maps when a cell reorients (e.g. Figure 4C, black dashed lines). To further test how internal organization impacts polarized trafficking, we analyzed the secretion of collagen X and the flow of Rab6-positive vesicles in NZ treated cells. As expected, in these cells there was no polarized secretion of vesicles (n = 18, see Figure 4—figure supplement 1B and Figure 4—video 1 for representative examples) and no polarized trafficking of vesicles, but an isotropic directed flow toward the membrane (n = 14 cells, see Figure 4—video 1 for a representative example). To further investigate the role of Golgi-based trafficking, we used Golgicide A, which perturbs secretion from the Golgi by specifically targeting GBF1, a GEF of Arf1 for the COPI coat production at the Golgi. We found that treatment with Golgicide A reduces persistence (Figure 4—figure supplement 4), however, less than treatment with either NZ or Taxol, a MT stabilizing drug that interferes with MT dynamics. Along the same line, as mentioned above, overexpression of Rab6 leads to increase in persistence of migration (Figure 4—figure supplement 3).

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Trafficking from Golgi complex is biased toward the protrusion.

(A) Collagen X cargo (labeled in black) is traveling from ER to the Golgi complex and secreted during a Retention Using Selective Hooks assay experiment (blue contour: nucleus, yellow contour: Golgi complex, green dashed line: protrusions, black dashed line: Nucleus-Golgi axis, cyan dashed line: secretion axis, scale bar - 20 μm). (B) Top, RPE1 cells expressing endogenous levels of a marker for post-Golgi vesicles (iRFP-Rab6A) (scale bar - 20 μm). Bottom, red lines represent the lines over which vesicle traffic intensity is calculated over time. (C) Top, a representative morphodynamic map representing plasma membrane protrusions in time. Bottom, a representative trafficking map showing the flow of post-Golgi vesicles from the Golgi complex along straight lines toward the surface over time time period - 6.5 hr, black dashed lines represent the time difference between a spike in protrusions (top) and a spike in secretion (bottom). X-axis represents time and Y-axis represents the contour of the cell. (D) Cross-correlation coefficient between plasma membrane protrusions and secretion as a function of the time lag (n = 15 cells, red line: average curve depicting the correlation coefficient, gray lines: single cell data; ‘before’ and ‘after’ denote the time before the protrusion peak and after, respectively). (E) Single cell time lags in minutes between protrusions and secretion at maximal correlation, obtained by fitting the peak of individual cross-correlation curves (gray curves in (D)). Data used for **C-E** and related scripts can be found in Figure 4—source data 1.

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Together, these results show that intracellular polarity axis is required to keep trafficking aligned with protrusive activity over timescales longer than ~20 min. Moreover, we found that interfering with MT dynamics had a stronger effect on persistent migration than interfering with secretion activity from the Golgi.

An imposed Cdc42 gradient reorients the Golgi complex

Because protrusion activity preceded trafficking from the Golgi, we next investigated how protruding activity regulates internal polarity. We controlled the protrusive activity with optogenetic stimulation while monitoring internal polarity. For the optogenetics, we used the iLID/SspB dimerizing system (Guntas et al., 2015) to locally activate Cdc42 (Valon et al., 2015) by recruiting the catalytic DH-PH domain of ITSN (Intersectin) – one of its specific activators – using localized blue light illumination. We used the Nucleus-Golgi axis as a proxy of the internal polarity axis. Using the previously described feedback imaging routine, we added a possibility to induce Cdc42 activation while imaging a migrating cell and adapt the activation pattern to the changing shape of the cell (see Figure 1—figure supplement 1B and Materials and methods). Our previous experiments revealed that a sharp gradient of Cdc42 is the most effective to control directionality of cell movement (de Beco et al., 2018), therefore, we chose to activate a thin region along the border of the cell. We conducted 16 hr live cell imaging experiments, starting with the activation 90° away from the existing Nucleus-Golgi axis. We found that optogenetic activation of Cdc42 was sufficient to reorient the Nucleus-Golgi axis toward the region of activation (Figure 5A and Figure 5—video 1). We quantified the rotation of the Nucleus-Golgi axis toward the axis of the optogenetic activation (going from the center of nucleus to the center of activation area) over time for 19 cells and observed a systematic reorientation in 3 hr followed by a stabilization of the Nucleus-Golgi axis around 0° (18° ± 28°, after 4 hr, Figure 5B and Figure 5—figure supplement 1C). To test the specificity of the Cdc42 activation, we performed control experiments (n = 26 cells), in which the DH-PH domain of ITSN is missing (see scheme in Figure 5C, top). In control experiments, the Nucleus-Golgi axis constantly moved without stabilization at the axis of optogenetic activation (0°) (33° ± 172°, after 4 hr, Figure 5C). To further confirm that the optogenetic activation stabilized the Nucleus-Golgi axis, we optogenetically activated cells in front of existing Nucleus-Golgi axis (Figure 5—figure supplement 1A). The axis was stabilized as the angle between Nucleus-Golgi axis and optogenetic activation stayed close to 0° during the full duration of the experiment (n = 19 cells) (8° ± 34°, after 4 hr, Figure 5—figure supplement 1B). Since our optogenetic activation leads to protrusions and cell migration, the Nucleus-Golgi axis may reorient in a passive manner through cell shape changes. To better control cell shape, we performed similar experiments on round fibronectin micropatterns. Similar to nonpatterned cells, the reorientation of Nucleus-Golgi axis aligned with the activation area (Figure 5—figure supplement 1D-F). Yet, we found that the Nucleus-Golgi axis reorientation happened faster, with 50% of cells reorienting in 1 hr on a pattern compared to 3 hr when freely moving (Figure 5—figure supplement 1C). Thus, our results show that a biochemical Cdc42 activity but not a change in cell shape is able to reorient the Nucleus-Golgi axis toward it and then stabilize it.

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Biochemical gradient of Cdc42 reorients the Golgi complex and rescues directional migration.

(A) DIC image overlaid with Golgi marker (yellow, iRFP-Rab6A) of an RPE1 cell optogenetically activated 90° away from its initial Nucleus-Golgi axis (black dashed line: Nucleus-Golgi axis, blue dashed line: optogenetic activation axis, scale bar – 20 μm). (**B–C**) Optogenetic activation of Cdc42 90° away from Nucleus-Golgi axis leads to its reorientation in RPE1 cells freely moving on fibronectin covered coverslip (n = 19 cells) (B) and is random in control condition (n = 26 cells) (C) (thin orange lines: single cell data, thick orange line: data average, dashed thick orange lines: standard deviation; corresponding optogenetic constructs used are depicted above the graphs). (D) Trajectories of cells moving in this experimental condition (n = 13 cells; trajectories plotted over 7 hr of experiment). (E) Directionality ratio comparison between optogenetically activated cells in presence of Nocodazole (NZ) (orange: freely moving cells (‘Ctrl’, n = 17 cells), black: freely moving cells in presence of NZ (‘NZ’, n = 14 cells), blue: optogenetically activated cells (‘Cdc42’, n = 22 cells), gray: optogenetically activated cells in presence of NZ (‘NZ +Cdc42, n = 13 cells); Kruskal–Wallis test followed by a post hoc Dunn’s multiple comparison test, *p≤0.05, **p≤0.01, ***p≤0.001). Data used for **B-E** and related scripts can be found in Figure 5—source data 1.

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An imposed Cdc42 activation rescues persistent cell migration

We found that persistent migration requires stable protrusive activity, which is lost upon NZ treatment. We thus tested if we could rescue this loss of protrusive stability using activation of Cdc42 by optogenetics. We used optogenetic activation of Cdc42 in presence of NZ (0.1 μM) and found that optogenetic Cdc42 activation indeed rescued the persistence of cell migration, as observed directly from cell trajectories (Figure 5D) or from the directionality ratios, which are calculated by taking the ratio of the displacement of the cell and the length of the actual path it took (Figure 5E). Whereas directionality ratio in presence of NZ was drastically perturbed, reaching only 19% of directionality in control cells (directionality ratio of 0.11 ± 0.04), optogenetic Cdc42 activation restored the directionality to 0.4 ± 0.2, a number comparable to freely migrating cells (0.59 ± 0.31) and similar to optogenetically activated cells without NZ (0.4 ± 0.24) (Figure 5E). The fact that we could rescue persistent migration in NZ treated cells indicates that the loss of persistency in NZ treated cells is the consequence of the absence of a mechanism stabilizing the protrusive activity and not an inherent inability of cells to move persistently.

A minimal model coupling protrusive activity and polarized trafficking recapitulates persistent migration

Our results indicate that persistent mesenchymal migration emerges from a feedback between the alignment of the internal polarity axis by Cdc42 and stabilization of Cdc42-dependent protruding activity through polarized trafficking toward protrusions (Figure 6A). We constructed a minimal physical model to know whether this feedback is enough by itself to recapitulate the features we observed with the persistently migrating RPE1 cells (see Figure 6—figure supplement 1 and Materials and methods for a detailed explanation of the model). To implement the two sides of the feedback with minimal settings, we chose to model synthetic morphodynamic maps that advantageously capture quantitatively the process of cell migration in a single piece of data.

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A minimal physical model based on the coupling between protrusive activity and internal polarity recapitulates persistent migration.

(A) Scheme representing the sequence of events leading to persistent migration. The Golgi complex is in yellow, microtubules are in violet, and vesicles in white. (**B–C**) Membrane dynamics for a point-like Cdc42 activation (A, upper panel) were convolved with a RhoGTPases signal (A, lower panel) to compute membrane dynamics for a single protrusive event (B). (D) Overall synthetic morphodynamic maps generated by varying the protrusive activity frequency. The intensity of protrusive activity varies from one protrusion every 20 frames (top), to one every 5 frames (bottom), the latter value having been retained for our simulations in (**G–H**). (**E–F**) Implementation of the feedback, with $P_{p o l a r i z e d}$ quantifying the probability to form a protrusion in front of the polarity axis (equal to one in the simulated morphodynamic map in (E)), and $κ$ quantifying the capacity of protrusions to pull on the polarity axis (equal to one the simulated morphodynamic map in (F)). (G) Examples of morphodynamic maps (top, black line is $c_{a x i s} (t)$ the direction of the internal polarity axis), cell trajectories (bottom left), and autocorrelation of direction (bottom right) for different values of strength of the feedback. 1: $P_{p o l a r i z e d} = 0; κ = 0$ . 2: $P_{p o l a r i z e d} = 0.5; κ = 0.8$ . 3: $P_{p o l a r i z e d} = 0.8; κ = 1$ . (H) Phase diagrams of persistence time, protrusive unicity, and alignment index. Black lines in the diagrams correspond to the experimental values (persistent time ~2 hr, protrusive unicity ~0.6 and alignment index ~0.7). The lines of the two last diagrams are reported on the first one, where they cross at a single point (blue dot), hereby showing consistency between the model and our experimental measurements. Data used for **A-H**, related scripts and additional explanations can be found in Figure 6—source data 1.

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We first implemented the morphodynamic map corresponding to a single event of protrusive activity, which may comprise several protrusion/retraction cycles. Membrane dynamics following a pulse activation of Cdc42 and Rac1 were previously experimentally obtained and described in Yamao et al., 2015. In this work, the authors computed the transfer function between a point-like RhoGTPase activity at time 0 and position 0, and the membrane dynamics that follow. We numerically synthesized this transfer function and a Cdc42 pulse of activity extended in space and time (Figure 6B) such that the convolution of the two leads to a morphodynamic map similar to a single event of protrusive activity as seen in our data (Figure 6C). Next, we simulated full morphodynamic maps by nucleating protrusive events randomly in space and time such that the frequency of protrusive activity of our model matched the data (Figure 6D). On these maps, we assumed that the cell possessed an internal polarity axis parametrized by a moving point, $c_{a x i s} (t)$ , on the y-axis, which corresponds to the intersection between the polarity axis and the cell contour. For sake of simplicity, we did not make the distinction between the axis of directed trafficking/secretion and Nucleus-Golgi axis but considered a single effective one. We then implemented the feedback between polarity axis and protrusion dynamics. For the first side of the feedback, we introduced a probability $P_{p o l a r i z e d}$ to nucleate a protrusive event in front of the polarity axis and a probability $P_{r a n d} = 1 - P_{p o l a r i z e d}$ elsewhere. When $P_{p o l a r i z e d} = 0$ , protrusions are happening randomly along the contour, and when $P_{p o l a r i z e d} = 1$ , protrusions are always happening in front of the polarity axis (Figure 6E). For the second side of the feedback, we assumed that the polarity axis was pulled toward the protrusion by an effective force $F_{p r o t}$ that acts against a force $F_{b a s a l}$ characterizing the random rotation of the polarity axis. The bias of the protrusive activity on the polarity axis positioning can be parametrized by a number $κ$ such that $\frac{d}{d t} c_{a x i s} (t) = κ F_{p r o t} + (1 - κ) F_{b a s a l}$ . When $κ = 0$ , the polarity axis follows its natural evolution, and when $κ = 1$ , the polarity axis follows the protrusive activity (Figure 6F). The strength of both sides of the feedback can thus be summarized by two numbers between 0 and 1. For a given value of these two numbers, we could simulate realistic morphodynamic maps ranging from nonpersistent to persistent migrating cells (Figure 6G and Figure 6—video 1). From these maps, we generated cell trajectories from which we computed the autocorrelation of direction and persistence time, as for our experimental data (Figure 6G). In addition, we computed two other independent parameters aimed at quantifying cell polarity (Figure 6H). The protrusive unicity index characterizes how many distinct protrusive activities are competing at a given time, and the alignment index characterizes how well the polarity axis aligns with the direction of movement.

Running our simulation for all possible values of $P_{p o l a r i z e d}$ and $κ$ , we obtained three phase diagrams for the persistence time, protrusive unicity, and alignment index (Figure 6H). These ‘look-up’ tables differ in their dependencies with regard to the two parameters, and can, thus, be used to estimate their values independently. When combined, they should converge to a single couple of values. If it is the case, it would be a signature of the consistency of our minimal model. It was indeed the case for our data on RPE1 cells, where we found a persistence time of 2.3 ± 1.4 hr, a protrusion unicity index of 0.65 ± 0.15, and an alignment index of 0.72 ± 0.21. Using these numbers and the phase diagrams to estimate $P_{p o l a r i z e d}$ and $κ$ , we obtained a region of the parameter space that is consistent and predicts that $κ = 0.9$ and $P_{p o l a r i z e d} = 0.7$ . Thus, our model suggests that the high persistence of RPE1 cells can be explained by a relatively high values of the feedback strengths. Next, we used the same approach on HeLa cells, a cell line that is less persistent than RPE1 (Figure 6—figure supplement 2). Our model indicates that the decreased persistency of HeLa cells can be explained by a lower value of $P_{p o l a r i z e d} = 0.35$ , reflecting the multiple competing ruffling fronts observed in these cells. To further exploit the quantitative aspect of our model, we also analyzed our NZ and Golgicide A datasets (Figure 6—figure supplement 3). For those conditions, unfortunately, the alignment index cannot be calculated, because the Golgi complex is dispersed. Yet, from the persistence time and protrusive unicity, the model predicts that NZ has a stronger effect on $P_{p o l a r i z e d}$ and $κ$ than Golgicide A, confirming the central role of MTs in the feedback.

Discussion

In this work, we showed that persistent mesenchymal migration observed on a timescale of several hours can emerge from a feedback between protrusion dynamics and polarized trafficking. This feedback mechanism corresponds to the one that has been intensively documented in yeast, where polarized bud formation is dynamically maintained by coupling of transport and signaling (Eugenio et al., 2008). Using experimental approaches, we showed that protrusion dynamics and polarized trafficking are coupled in mesenchymal RPE1 cells. We demonstrated using optogenetics that sustained local activation of Cdc42 is sufficient to reorient the Nucleus-Golgi axis in 3 hr (Figure 5). Moreover, we showed that the Nucleus-Golgi axis correlates with the direction of migration and the trafficking of Rab6 secretory vesicles in freely migrating cells (Figures 1 and 4) and that protrusions precede trafficking. Together, our results suggest that a sustained protrusive activity reorients the trafficking and secretory pathway toward protrusions. We observed a time lag of 20 min between protrusions and redirection of the trafficking of Rab6-positive vesicles and that secretion was preferentially directed to newly formed protrusions. Note that whereas Rab6 marks the specific trafficking from the Golgi complex, the Nucleus-Golgi polarity axis could additionally represent other polarized trafficking, independent of the Golgi complex. Indeed, the recycling compartment regulated by Rab11 also aligns with the Nucleus-Golgi polarity axis of the cell and could additionally contribute to polarized trafficking (Ferro et al., 2021). Taken together, these results strongly support the fact that protrusions orient polarized trafficking on a short timescale, and orient the Nucleus-Golgi axis on a longer one.

The Rho GTPase Cdc42 was already proposed to play a role in the Nucleus-Golgi axis reorientation (Etienne-Manneville and Hall, 2001), and several diﬀerent pathways activated by Cdc42 could be working in the process. MTs, that are anchored to the protrusion by forming focal adhesions, have been proposed to pull toward the protrusion hereby reorienting the centrosome and the Golgi complex together (Etienne-Manneville et al., 2005). The Golgi complex could be pulled by polymerizing actin forces via GOLPH3/MYO18A pathway (Xing et al., 2016). Eventually, the actin retrograde ﬂow could push the nucleus backward (Gomes et al., 2005). Our cargo trafficking experiments have indicated that the cargo is trafficked and secreted toward the newly forming nascent protrusions. This could be explained by the MTs being guided toward the newly forming adhesions by the actin cytoskeleton (Etienne-Manneville, 2013; Meiring et al., 2020). Fourriere et al. has also shown that Rab6-positive post-Golgi vesicles release their cargo in the vicinity of FAs (Focal Adhesions) (Fourriere et al., 2019). Further investigation is needed to reveal the exact mechanisms by which Rab6-positive vesicles accumulate at membrane ruffles forming the protruding front of the cell, before the Nucleus-Golgi axis reorients. One hypothesis could be that the MT density is higher on the side of the protruding front (Etienne-Manneville, 2013; Meiring et al., 2020). Alternatively, post-translational modification of a subset of MTs via acetylation of α-tubulin, which has been found to accumulate in cell protrusions and to regulate cell polarization (Montagnac et al., 2013), could lead to preferential trafficking of Rab6-positive vesicles. It has been proposed that Rab6-positive vesicles fission from Golgi/TGN at a limited number of hotspot sites, to regulate their exit along MTs (Miserey-Lenkei et al., 2017). Our data, showing that reorganization of secretion precedes the reorientation of the Golgi complex, is consistent with the fact that the MTs, which direct secretion, reorganize before the Golgi complex is reoriented.

On the other hand, our data demonstrate that polarized trafficking sustained protruding activity: using low doses of NZ to disrupt internal cell organization and polarized trafficking (Figure 4—figure supplement 1B and Figure 4—video 1), we showed that the persistence time of cell migration dropped from 2.3 hr to 20 min (Figure 3). Interestingly, protrusion speed and instantaneous cell speed were not affected, showing that the loss of persistency was not due to cell’s inability to protrude or move. Our rescue experiment using constant Cdc42 activation (Figure 5) confirmed that sustaining protruding activity is sufficient for persistent migration. Of note, NZ treated cells were still persistent over 20 min, showing that in this condition the protrusive activity is stable for a longer time than the duration of a single protrusion-retraction event (on the order of 100 s). This also suggested that RhoGTPases’ activities are comparable between WT and mildly NZ treated cells, although we cannot exclude that endogenous associated GEF/GAP activators and deactivators were affected. Thus, NZ treated cells are still able to stabilize a protrusive activity over several cycles, possibly thanks to the existence of a vimentin template (Gan et al., 2016). However, NZ treated cells were not able to stabilize their protrusive activity over longer time ( >20 min), which we attributed to the loss of polarized trafficking (Figure 4—video 1), potentially of protrusion-promoting factors toward the cell front in Rab6-positive vesicles (Figure 4). Rab6 has been proposed to be a general regulator of post-Golgi secretion, and it has been shown that irrespective of the transported cargos most Rab6-positive carriers are not secreted randomly at the cell surface, but on localized hotspots juxtaposed to focal adhesions (Fourriere et al., 2019). However, we cannot exclude that protrusion-promoting factors are transported from other compartments, such as the recycling endosomal compartment that is found at the proximity of the Golgi complex, and, thus, also polarizes along the Nucleus-Golgi axis.

We recapitulated the two sides of the feedback in the framework of a new minimal physical model. This model is based on the coupling between an internal polarity axis – a vector, and protrusion dynamics modeled by synthetic morphodynamic maps. This model can be thought of as a ‘cell compass’, where protrusions pull on the needle that has some inertia, and the direction of the needle locally promotes the initiation of protrusions. Even if we do not specify the exact nature of the polarity axis in the model, our data suggest that the relevant axis is the direction of secretion, which follows protrusions with a 20 min delay. The Nucleus-Golgi axis also follows the direction of motion powered by protrusions, albeit with a longer delay, but neither the Nucleus-Golgi axis, nor the direction of secretion appears to be instructive. Yet, for stretches of persistent motion when the direction of secretion, Nucleus-Golgi axis, and direction of migration are all aligned, the Nucleus-Golgi axis is a good proxy of the polarity axis. Many mathematical models of cell polarization (Jilkine and Edelstein-Keshet, 2011; Mogilner et al., 2012) or of cell migration (Danuser et al., 2013) were previously introduced, but our approach differs in the sense that the whole cell migratory behavior can be described by only two effective parameters that quantify the strength of the feedback. We showed that the persistence time of different cell types can be predicted from the measurement of two parameters – the average number of competing protrusions and the alignment of polarity axis with direction of motion (Figure 6). Interestingly, RPE1 cells sit in the persistence time phase diagram at the relatively sharp transition between nonpersistency and superpersistency. This suggests that cells might be tuned at an optimal functioning point, to be persistent in their migration while not being locked into a straight path, possibly to be able to respond to environmental cues. One way to test this prediction would be to experimentally modify the strengths of the feedback: a slight increase of $κ$ and $P_{p o l a r i z e d}$ should lead to superpersisters. This could be achieved by expressing in cells a fusion between ITSN-DHPH domain, an activator of Cdc42, and Rab6 or Rab11 to reinforce the feedback loop between protrusions and secretion. Our model shows that cells can polarize with a unique protruding front (protrusive unicity close to 1) when the probability to form a protrusion in front of the polarity axis is high ( $P_{p o l a r i z e d} = 1$ ). Of course, this was expected since we assumed that there was a unique polarity axis. In our experiments, we indeed observed a unique axis of polarized trafficking (Figure 4), and it remains to be understood how cells achieve this unicity. As a matter of fact, we could imagine that multiple protrusions would be sustained simultaneously, associated to their own polarized trafficking routes, and with the same feedback mechanism being involved. A possible answer would be the existence of a limiting component in the system, as proposed in the context of yeast polarity (Chiou et al., 2017). Alternatively, the level of RhoGTPase activity might be tuned to limit the number of competing protrusions, as suggested by the relationship between Rac1 activity and directional persistent migration (Pankov et al., 2005).

To conclude, our present work focused on the coupling between protrusion dynamics and polarized trafficking. Many other functional units supporting cell polarity are likely to be involved (Vaidžiulytė et al., 2019), and it will be interesting to see in future studies how other coupling mechanisms can contribute to the robustness of cell polarity during persistent migration. Additionally, it would be of interest to see how our conclusions can be extended to other types of migration, such as the amoeboid one.

Materials and methods

Key resources table

Reagent type (species) or resource	Designation	Source or reference	Identifiers	Additional information
Cell line (Homo sapiens)	hTERT RPE1 (immortalized, normal, female)	ATCC	ATCC Cat# CRL-4000, RRID:CVCL_4388
Cell line (H. sapiens)	HeLa (adenocarcinoma, female)	ATCC	ATCC Cat# CCL-2, RRID:CVCL_0030
Cell line (H. sapiens)	RPE1::iRFP-Rab6A	This paper		Heterozygous iRFP-Rab6A CRISPR knock-in
Cell line (H. sapiens)	RPE1::iRFP-Rab6::hITSN1-tgRFP-SSPB wtt::Venus-iLID-CAAX	This paper		Stable cell line with heterozygous iRFP-Rab6A label and lentivirally induced expression of optogenetic constructs
Cell line (H. sapiens)	RPE1::GFP-Rab6A	This paper		Lentivirally induced stable GFP-Rab6A overexpression
Cell line (H. sapiens)	RPE1::GFP-Rab6A::myr-iRFP	This paper		Stable cell line with GFP-Rab6A and myr-iRFP overexpression
Cell line (H. sapiens)	RPE1::EGFP-α-tubulin	Piel lab		Stable cell line with a α-tubulin marker
Cell line (H. sapiens)	RPE1::EB3-EGFP	https://doi.org/10.1038/nmeth.1493		Krek lab
Transfected construct (H. sapiens)	pLL7.0: hITSN1(1159–1509)-tgRFPt-SSPB WT	Addgene	RRID: Addgene_60419	Lentiviral optogenetic construct for Cdc42 activation
Transfected construct (H. sapiens)	pLL7.0: Venus-iLID-CAAX	Addgene	RRID: Addgene_60411	Lentiviral optogenetic construct
Transfected construct (H. sapiens)	pMD2.G	Addgene	RRID: Addgene_12259	Lentiviral VSV-G envelope expressing plasmid
Transfected construct (H. sapiens)	psPAX2	Addgene	RRID:Addgene_12260	Second generation lentiviral packaging plasmid
Transfected construct (H. sapiens)	pHR: myr-iRFP	Coppey lab		Lentiviral construct to label plasma membrane with iRFP
Transfected construct (H. sapiens)	pEGFP-C3: Rab6A_wt	Goud lab		Plasmid construct to label Rab6A (Golgi complex)
Transfected construct (H. sapiens)	pIRESneo3: Str-KDEL-SBP-EGFP-COL10A1	https://doi.org/10.1083/jcb.201805002		RUSH system plasmid with EGFP tagged Collagen X cargo
Antibody	anti-GFP (rabbit monoclonal)	Recombinant Antibody Platform of Institut Curie	Cat#:A-P-R#06	(dilution 1:100)
Antibody	anti-α-Tubulin (mouse monoclonal)	Sigma Aldrich	Sigma-Aldrich Cat# T5168, RRID:AB_477579	(dilution 1:1000)
Antibody	Anti-mouse AlexaFluor 546 F(ab’)2 fragment of IgG (H + L) (goat polyclonal)	Life Technologies		(dilution 1:1000)
Sequence-based reagent	gRNA-3-mRAB6A	Eurofins	sgRNA	GTCTCCGCCCGTGGACATTG
Chemical compound, drug	Nocodazole	Sigma Aldrich	M1404	(0.1 µM)
Chemical compound, drug	Golgicide A	Sigma Aldrich	G0923	(35 µM)
Chemical compound, drug	Taxol	Sigma Aldrich	T7402	(0.1 µM)
Chemical compound, drug	Biotin	Sigma Aldrich	B4501	(40 µM)
Chemical compound, drug	Poly-L-lysine	Sigma Aldrich	P8920	(0.01% diluted in water)
Chemical compound, drug	Fibronectin	Sigma Aldrich	F1141	(2 µg/mL; 10 µg/mL; 20 µg/mL)
Chemical compound, drug	PLL-g-PEG	Surface Solutions	PLL(20)-g[3.5]- PEG(2)	(100 µg/mL)
Chemical compound, drug	azido-PLL-g-PEG (APP)	https://doi.org/10.1002/adma.201204474		(100 µg/mL)
Chemical compound, drug	BCN-RGD	https://doi.org/10.1002/adma.201204474		(20 µM)
Software, algorithm	Matlab	MathWorks	RRID:SCR_001622
Software, algorithm	Fiji, ImageJ	https://doi.org/10.1038/nmeth.2019	RRID:SCR_002285
Software, algorithm	Trackmate	https://doi.org/10.1016/j.ymeth.2016.09.016 https://doi.org/10.1016/j.ymeth.2016.09.016
Software, algorithm	MetaMorph	Molecular Devices	RRID:SCR_002368
Other	Hoechst 33,342	Thermo Fisher Scientiﬁc	H3570	(1 µg/mL)
Other	DAPI stain	Merck	D9542	(1 µg/mL)

Name	Guide sequence (5′–3′)
gRNA-3-mRAB6A	GTCTCCGCCCGTGGACATTG
LeftArm fwd	gaccatgattacgccaagcttgcatgcctgcaggtcgactGCCACAGTGCTCCGCTTTCC
LeftArm rev	gcgacggatccttcagccatTGTGGAACTAGAGGAGCGGC
Linker-iRFP fwd	gccgctcctctagttccacaATGGCTGAAGGATCCGTCGC
Linker-iRFP rev	ccgaagtctgcgcgcgtggaCCGGATTGGCCACTCTTCCAT
RightArm fwd	tggaagagtggccaatccggTCCACGgGaGgAGACTTCGG
RightArm rev	GggttttcccagtcacgacgttgtaaaacgacggccagtgCAGTGATGAAAGTCAAGAGAACAAAATG AGGTTTTCCG

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Persistent protrusions form in front of the Golgi complex.

Figure 1—source data 1

Nucleus-Golgi axis and direction of movement align when a cell starts moving.

Figure 2—source data 1

Low dose of Nocodazole (NZ) reduces persistence of migration.

Figure 3—source data 1

Trafficking from Golgi complex is biased toward the protrusion.

Figure 4—source data 1

Biochemical gradient of Cdc42 reorients the Golgi complex and rescues directional migration.

Figure 5—source data 1

A minimal physical model based on the coupling between protrusive activity and internal polarity recapitulates persistent migration.

Figure 6—source data 1

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Kotryna Vaidžiulytė

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Anne-Sophie Macé

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Aude Battistella

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William Beng

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Kristine Schauer

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Mathieu Coppey

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