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Persistent and polarized global actin flow is essential for directionality during cell migration

Nature Cell Biology volume 21, pages 1370–1381 (2019)Cite this article

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Abstract

Cell migration is hypothesized to involve a cycle of behaviours beginning with leading edge extension. However, recent evidence suggests that the leading edge may be dispensable for migration, raising the question of what actually controls cell directionality. Here, we exploit the embryonic migration of Drosophila macrophages to bridge the different temporal scales of the behaviours controlling motility. This approach reveals that edge fluctuations during random motility are not persistent and are weakly correlated with motion. In contrast, flow of the actin network behind the leading edge is highly persistent. Quantification of actin flow structure during migration reveals a stable organization and asymmetry in the cell-wide flowfield that strongly correlates with cell directionality. This organization is regulated by a gradient of actin network compression and destruction, which is controlled by myosin contraction and cofilin-mediated disassembly. It is this stable actin-flow polarity, which integrates rapid fluctuations of the leading edge, that controls inherent cellular persistence.

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Fig. 1: Leading edge fluctuations are a weak predictor of cell directionality.
Fig. 2: Actin retrograde flow is globally organized in migrating haemocytes.
Fig. 3: The polarity of global actin flow is highly stable and correlated to haemocyte motion during random and directed migration.
Fig. 4: Negatively divergent regions of the actin flowfield represent regions of actin network strain and disassembly.
Fig. 5: Loss of myosin II and cofilin lead to reduced actin flow and cell speed.
Fig. 6: A gradient of myosin-II-driven contraction is essential for global organization of actin flow.
Fig. 7: A gradient of myosin II indirectly leads to actin network contraction.

Data availability

Source data are available online for Figs. 1–7 and Extended Data Figs. 1–6. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The computational analysis was performed in Matlab (Mathworks) using custom code, which can be obtained from the corresponding author upon reasonable request. Mathematical modelling was performed in Mathematica (Wolfram) using custom code which is available here: https://github.com/ljschumacher/actomyosin-flow.

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Acknowledgements

The authors thank B. Baum, G. Charras, L. Cramer and A. Franz for their comments on the manuscript. The authors would also like to thank B. Baum for the gift of the RPE1 cell lines and J. McQuade for help with Mathematica. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 681808), and the Wellcome Trust (grant no. 107859/Z/15/Z). L.Y. is funded by the BBSRC London Interdisciplinary Doctoral Programme. L.J.S. and R.G.E. were funded by the BBSRC (grant no. BB/N00065X/1).

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Author notes

  1. John Robert Davis

    Present address: The Francis Crick Institute, London, UK

  2. These authors contributed equally: Lawrence Yolland, Mubarik Burki.

Authors and Affiliations

  1. Randall Centre for Cell and Molecular Biophysics, King’s College London, London, UK

    Lawrence Yolland, Mubarik Burki, Stefania Marcotti, Andrei Luchici, Fiona N. Kenny, John Robert Davis, Eduardo Serna-Morales & Brian M. Stramer

  2. Department of Mechanical Engineering, University College London, London, UK

    Lawrence Yolland & Mark Miodownik

  3. Dacian Consulting, London, UK

    Andrei Luchici

  4. Institute of Science and Technology Austria (IST Austria), Am Campus 1, Klosterneuburg, Austria

    Jan Müller & Michael Sixt

  5. Centre for Inflammation Research, University of Edinburgh, Edinburgh, UK

    Andrew Davidson & Will Wood

  6. Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, UK

    Linus J. Schumacher

  7. Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, UK

    Robert G. Endres

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Contributions

L.Y., M.B. and J.R.D. performed most of the Drosophila experiments under the guidance of B.M.S. F.N.K. performed the RPE1 experiments. J.M. and M.S. provided the fish keratocyte data. A.D. and W.W. provided the ena mutant data. L.J.S. and R.G.E. designed and analysed the fluid mechanics model. L.Y., M.B., S.M., A.L. and E.S.-M. analysed the data under the guidance of B.M.S. M.M. and B.M.S. conceived and designed the study. L.Y., M.B., S.M., L.J.S., R.G.E. and B.M.S. wrote the manuscript.

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Correspondence to Brian M. Stramer.

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Extended data

Extended Data Fig. 1 Leading edge fluctuations are a weak predictor of cell directionality.

(A) Three examples of cell contour analysis during hemocyte migration revealing highly dynamic edge activity. (B) Left panel reveals a representative snapshot of a randomly migrating hemocyte with the maximum edge extension (green) and retraction (magenta) automatically tracked and compared to the direction of cell motion (white). Right panel shows that the maximum extension and retraction are positively and negatively correlated to motion. Note the high variance in the distribution. ***P < 0.0001, Mann-Whitney two-tailed test. The graph shows mean and SD as bars; each datapoint is displayed as a dot (n = 443, 9 biologically independent samples). (C) Left panel shows a representative snapshot of all extension vectors around the cell perimeter (blue) and maximum extension vectors based on the longest contiguous extension (green). White arrow shows the direction of cell motion. Right panel shows the correlation of the resultant velocity of extension vectors to the direction of motion, showing that the resultant of all extensions is better correlated than maximum extension alone. ***P < 0.0001, Mann-Whitney two-tailed test. Boxplot shows medians as central lines, 25th and 75th percentiles as box limits, 10th and 90th as whiskers (n = 443, 9 biologically independent samples).

Extended Data Fig. 2 Actin retrograde flow is globally organised in migrating hemocytes.

(A) Probability density function of the divergence within the actin flowfield. Note that most of the measured divergence is negative. (B) Time-lapse of divergence within the actin flowfield during hemocyte migration. Dashed circle highlights a region of strong negative divergence that is persistent in time. (C) Time-lapse of a LifeAct-GFP labelled hemocyte (top panel). High magnification image showing the direction of the actin flowfield (arrows) colour-coded for the strength of the negative divergence (bottom panel). Note that in the centre of the flowfield is a region of actin network deformation, which correlates with strong negative divergence. (D) Comparison of streamlines with the speed and divergence of global actin flow. The dashed circle highlights the streamline sink which correlates with a region of low flow speed and high negative divergence. (E) Example image of a hemocyte with sustained bipolar protrusions and opposing streamline sinks. Note the strong negative divergence within both sinks. Scale bar 10 µm. (F) Quantification of the mean cell-wide versus retrograde actin flow speed. Note the significant reduction in the retrograde region. ***P < 0.0001, Mann-Whitney two-tailed test. Boxplot shows medians as central lines, 25th and 75th percentiles as box limits, 10th and 90th as whiskers (n = 443, 9 biologically independent samples). (G) Comparison of instantaneous cell speed with average global actin flow speed (left, linear regression goodness-of-fit R2 = 0.06), flow speed within the retrograde region only (middle, R2 = 0.02), and flow speed in the direction of motion (right, R2 = 0). Note that there is no significant correlation in any of these comparisons (n = 443, 9 biologically independent samples).

Extended Data Fig. 3 Actin retrograde flow is globally organised in migrating cells.

(A) PIV, divergence, streamline analysis, and quantification of streamline sinks of cultured cells containing labelled actin. Representative snapshots are displayed for a fish keratocyte (scale bar 10 µm), a neural growth cone (scale bar 5 µm), and a Retinal Pigment Epithelium (RPE1) cell (scale bar 10 µm). (B) Comparison of cell speed with the speed of the maximum edge extension in RPE1 cells reveals that protrusion speed is significantly higher than instantaneous cell speed. ***P < 0.0001, Mann-Whitney two-tailed test. Boxplot shows medians as central lines, 25th and 75th percentiles as box limits, 10th and 90th as whiskers (n = 247, 3 biologically independent samples). (C) Example cell track of an RPE1 cell in which the unit vectors of the maximum edge extension or the primary streamline sink were superimposed. (D) Correlation of the primary streamline sink and the maximum edge extension vectors to the direction of cell motion in RPE1 cells. Note that that both are strongly correlated with the direction of cell motion. Mann-Whitney two-tailed test. Boxplot representation as in ‘B’ (n = 247, 3 biologically independent samples). (E) Temporal cross correlation comparing the direction of cell motion, maximum edge extension, and the maximum streamline sink in RPE1 cells, which reveals a peak correlation at 0-lag showing no obvious temporal hierarchy in these migratory behaviours (n = 3 biologically independent samples).

Extended Data Fig. 4 The polarity of global actin flow is highly stable and correlated to hemocyte motion during random and directed migration.

(A) Example cell track of a randomly migrating hemocyte in which the unit vectors of the maximum extension or the primary sink are superimposed, showing better correlation to motion for the primary sink. (B) Probability density function of the distance from the nucleus to the maximum extension and to the primary sink. (C) Time-lapse of hemocytes migrating directionally to a laser wound (asterisk) in the embryo. LifeAct-GFP in green, nuclei in magenta. Scale bar 30 µm. (D) Rose plot showing the direction of maximum extensions normalised to motion comparing random (black outline, n = 443, 9 biologically independent samples, same data shown in ‘1J’) to directed migration (green, n = 272, 4 biologically independent samples). (E) Rose plot showing the direction of the primary sink normalised to motion comparing random (black outline) to directed migration (blue). Sample size as in ‘D’. (F) Correlation to motion of the direction of the maximum extension (*P = 0.0339) and primary sink (*P = 0.0240). Note that both parameters are more correlated in directly migrating cells. Mann-Whitney two-tailed tests. Boxplot shows medians as central lines, 25th and 75th percentiles as box limits, 10th and 90th as whiskers (sample size as in ‘D’). (G) Comparison of the directional autocorrelations of cell motion (left), primary sink (middle), and maximum extension (right) during random (n = 9) and directed migration (n = 4 biologically independent samples). Note the slower decay during directed migration suggesting increased persistence. Error bars = SEM. (H) Quantification of the directionality ratio shows higher persistence in directly migrating cells (walking average over 60 s intervals). *P < 0.05, Mann-Whitney two-tailed test. Boxplot shows medians as central lines, 25th and 75th percentiles as box limits, minimum and maximum values as whiskers; each datapoint is displayed as a dot (sample size as in ‘D’).

Extended Data Fig. 5 Loss of myosin-II, cofilin and ena lead to reduced actin flow and cell speed.

(A) Schematic of the actin flow alignment analysis. The average cosine similarity between each velocity vector (F(r)) and its 8 nearest neighbours (F(n)) is calculated to reflect the organisation of the actin flow. (B) Colour-coded flowfield alignment representation for each genotype (1 meaning perfect alignment). (C) Average alignment of the actin flowfield showing that flow in myosin-II mutants is most disorganised. **P = 0.0014, (n.s) P > 0.99, Kruskal-Wallis test and Dunn’s multiple comparison test. Boxplot shows medians as central lines, 25th and 75th percentiles as box limits, minimum and maximum values as whiskers; each datapoint is displayed as a dot (n = 9 biologically independent samples for all genotypes). (D) PIV, divergence, streamline, and streamline sink analysis of LifeAct-GFP expressing wild-type and ena mutant cells. Scale bar 10 µm. (E) Comparison of the speed of extensions in wild-type (n = 443, 9 biologically independent samples) and ena mutants (n = 50, 9 biologically independent samples) reveals significantly lower speed in mutants. ***P < 0.0001, Mann-Whitney two-tailed test. Boxplot shows medians as central lines, 25th and 75th percentiles as box limits, 10th and 90th as whiskers. (F) Quantification of mean actin flow speed in wild-type and ena mutant cells reveals lower speed in mutants. ***P = 0.0005, Mann-Whitney two-tailed test. Boxplot representation and sample size as in ‘C’ (n = 9 biologically independent samples for both genotypes). (G) The primary sink is more negatively divergent in wild-type cells. ***P = 0.0003, Mann-Whitney two-tailed test. Boxplot representation as in ‘C’, sample size as in ‘F’. (H) Quantification of the percentage of streamlines at the primary sink in wild-type and ena mutant cells shows a similar level of streamline confluence. (n.s) P = 0.0625. Mann-Whitney two-tailed test. Boxplot representation as in ‘C’, sample size as in ‘F’.

Extended Data Fig. 6 A gradient of myosin-II driven contraction is essential for global organisation of actin flow.

(A) Scatter plot of Myosin-II intensity and actin divergence for each point in the lamella of a hemocyte reveals no relationship between Myosin-II levels and strength of divergence (linear regression goodness-of-fit R2 = 0.07, n = 5985 values from 5 biologically independent samples). (B) PIV analysis of actin and myosin-II flow performed simultaneously in a migrating hemocyte. (C) Comparison of the direction of actin and myosin-II flow from simultaneous PIV analysis reveals that their direction of motion is nearly identical. Boxplot shows median as central lines, 25th and 75th percentiles as box limits, 10th and 90th percentiles as whiskers (n = 147, 5 biologically independent samples). (D) Comparison of actin and myosin-II flow speed from simultaneous PIV analysis reveals that myosin-II motion is significantly slower. ***P < 0.0001, Wilcoxon matched-pairs signed rank two-tailed test. Boxplot representation and sample size as in ‘C’. (E) Comparison of actin and myosin-II divergence from simultaneous PIV analysis reveals that they have distinct profiles.

Supplementary information

Supplementary Information

Supplementary Note

Reporting Summary

Supplementary Video 1

Automatic tracking of a haemocyte comparing tracking of the cell centroid (magenta) or the nucleus (green). Note that in haemocytes at this temporal resolution (5 s frame–1), tracking the cell centroid reflects overall shape changes more than cell motion. In contrast, the nucleus represents a fixed fiducial marker within the cell that more accurately reflects cell movement. Similar results obtained in nine biologically independent samples.

Supplementary Video 2

Time-lapse movie of a randomly migrating haemocyte in which edge extensions were automatically segmented. Vectors (green arrows) were drawn from the nucleus to either each individual extension or the maximum extension (extension of the largest contiguous area), while simultaneously tracking the cell direction of travel (magenta arrow). Similar results obtained in nine biologically independent samples.

Supplementary Video 3

Time-lapse movie of a randomly migrating haemocyte in which analysis of the actin flowfield was conducted using PIV, divergence, streamlines and streamline sinks (the size of the spots are normalized to the number of streamlines ending within a defined region). Similar results obtained in nine biologically independent samples.

Supplementary Video 4

Time-lapse movie of a haemocyte expressing Actin-GFP (left panel) to directly label the actin network. A region within the network was photobleached (highlighted by the circle) and subsequently tracked as it transited through the lamella. Note that the bleached spot moves through the lamella in a direction predicted by the PIV (middle panel) and begins to disappear at the rear of the network, which shows an increase in negative divergence (right panel). Similar results obtained in nine biologically independent samples.

Supplementary Video 5

Time-lapse movies of a randomly migrating bipolar haemocyte in which the sinks colocalize with the negative divergent regions. Note that two independent sinks develop at the time when the cell develops a bipolar shape.

Supplementary Video 6

Time-lapse movie of a fish keratocyte in which analysis of the actin flowfield was conducted using PIV, divergence, streamlines and streamline sinks. Similar results obtained in two biologically independent samples.

Supplementary Video 7

Time-lapse movie of a neural growth cone in which analysis of the actin flowfield was conducted using PIV, divergence, streamlines and streamline sinks. Similar results obtained in three biologically independent samples.

Supplementary Video 8

Time-lapse movie of an RPE1 cell in which analysis of the actin flowfield was conducted using PIV, divergence, streamlines and streamline sinks. Similar results obtained in three biologically independent samples.

Supplementary Video 9

Time-lapse movie of a randomly migrating haemocyte comparing the direction of motion (white arrow) to either the direction to the maximum extension (green arrow) or the primary sink (magenta arrow). Similar results obtained in nine biologically independent samples.

Supplementary Video 10

Time-lapse movie of an RPE1 cell in which cell motion, edge extensions and streamline sink were automatically tracked. Vectors were drawn from the nucleus to the maximum extension (green arrow) and the primary sink (magenta arrow), while simultaneously showing the cell’s direction of motion (white arrow). Similar results obtained in three biologically independent samples.

Supplementary Video 11

Time-lapse movie of haemocytes migrating towards a laser wound (asterisk). LifeAct-GFP is shown in green, nuclei are labelled in magenta. Similar results obtained in four biologically independent samples.

Supplementary Video 12

Time-lapse movie of a randomly and a directionally migrating haemocyte highlighting the vector to their maximum edge extension (green) along with their direction of travel (white arrow). Asterisk denotes the wound site. Similar results obtained in nine (random) and four (directed) biologically independent samples.

Supplementary Video 13

Time-lapse movie of a randomly migrating haemocyte comparing negative divergence, compression and actin disassembly. Similar results obtained in nine biologically independent samples.

Supplementary Video 14

Time-lapse movies of wild-type, myosin II and cofilin mutant haemocytes undergoing developmental dispersal (LifeAct-GFP is shown in green, nuclei are labelled in magenta). Similar results obtained in nine biologically independent samples for all genotypes.

Supplementary Video 15

Time-lapse movies of PIV analysis of actin flow on wild-type, myosin II and cofilin mutant haemocytes expressing LifeAct-GFP. Similar results obtained in nine biologically independent samples for all genotypes.

Supplementary Video 16

Time-lapse movies wild-type, myosin II and cofilin mutant cells analysed for divergence, disassembly and streamlines of the actin flow. Similar results obtained in nine biologically independent samples for all genotypes.

Supplementary Video 17

Time-lapse movie of an Ena mutant cell in which analysis of the actin flowfield was conducted using PIV, divergence, streamlines and streamline sinks. Similar results obtained in nine biologically independent samples.

Supplementary Video 18

Time-lapse movie of a haemocyte containing fluorescently labelled actin and myosin II. Similar results obtained in five biologically independent samples.

Supplementary Video 19

Time-lapse movie of a haemocyte showing divergence of the actin flow and the location of actin and myosin II. Crosses highlight transient regions of strong negative divergence. Note that the myosin II puncta do not accumulate at regions of negative divergence and instead continue to flow through. Similar results obtained in five biologically independent samples.

Supplementary Video 20

Time-lapse movie of PIV analysis and divergence of actin and myosin II flow performed simultaneously in a migrating haemocyte. Similar results obtained in five biologically independent samples.

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Yolland, L., Burki, M., Marcotti, S. et al. Persistent and polarized global actin flow is essential for directionality during cell migration. Nat Cell Biol 21, 1370–1381 (2019). https://doi.org/10.1038/s41556-019-0411-5

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