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Directed cell migration towards softer environments

Abstract

How cells sense tissue stiffness to guide cell migration is a fundamental question in development, fibrosis and cancer. Although durotaxis—cell migration towards increasing substrate stiffness—is well established, it remains unknown whether individual cells can migrate towards softer environments. Here, using microfabricated stiffness gradients, we describe the directed migration of U-251MG glioma cells towards less stiff regions. This ‘negative durotaxis’ does not coincide with changes in canonical mechanosensitive signalling or actomyosin contractility. Instead, as predicted by the motor–clutch-based model, migration occurs towards areas of ‘optimal stiffness’, where cells can generate maximal traction. In agreement with this model, negative durotaxis is selectively disrupted and even reversed by the partial inhibition of actomyosin contractility. Conversely, positive durotaxis can be switched to negative by lowering the optimal stiffness by the downregulation of talin—a key clutch component. Our results identify the molecular mechanism driving context-dependent positive or negative durotaxis, determined by a cell’s contractile and adhesive machinery.

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Fig. 1: U-251MG glioblastoma cells undergo negative durotaxis.
Fig. 2: U-251MG cells display limited mechanosensitive signalling and adhesion maturation.
Fig. 3: Motor–clutch simulations recapitulate negative durotaxis.
Fig. 4: Decreasing actomyosin contractility selectively inhibits negative durotaxis in U-251MG cells.
Fig. 5: Lowering stiffness optimum by blocking adhesion reinforcement shifts cells from positive to negative durotaxis.

Data availability

The data supporting the findings of this study are available within the Article and its Supplementary Information. Other raw data generated during this study are available from the corresponding authors on request. Source data are provided with this paper.

Code availability

All code and scripts used in this study are available online (https://oddelab.umn.edu/ and via GitHub at https://github.com/cbcbcbcb123/Growth-Cone-Dynamics) and on request from the corresponding authors.

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Acknowledgements

We thank L. S. Prahl, J. Tian and G. Huang for helpful discussions on computational modelling and members of the Ivaska Lab for their insightful comments and discussion. Simulations were run in part on the high-performance computing resources at the Minnesota Supercomputing Institute. Turku Bioscience Centre Cell Imaging and Cytometry Core and Biocenter Finland are acknowledged for their services, instrumentation and expertise. We are supported by the University of Turku Doctoral Programme in Molecular Life Sciences (A.I.), the Company of Biologists Travelling Fellowship (A.I.), the Finnish Cultural Foundation (A.I.), the Academy of Finland (AoF CoE 346131 and 325464 (J.I.)), ERC CoG (grant 615258 (J.I.)), Sigrid Juselius Foundation (J.I.), the Finnish Cancer Organization (J.I.), the National Natural Science Foundation of China (11972280 (F.X.); 11772253 (M.L.); 12022206 (M.L.); 11532009 (T.J.L.); 12002262 (B.C.)), Natural Science Basic Research Plan in Shaanxi Province of China (2022KWZ-17 (M.L.)), the Shaanxi Province Youth Talent Support Program (M.L.), the Young Talent Support Plan of Xi’an Jiaotong University (M.L.), the National Institutes of Health (R01 AR077793 (G.M.G.); R01 CA172986 (D.J.O.); U54 CA210190 (D.J.O.); P01 CA254849 (D.J.O.); R35GM141853 (M.D.D.)) and the NSF Science and Technology Center for Engineering Mechanobiology (CMMI 1548571 (G.M.G.)).

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A.I., K.-Y.P., J.H., B.C., B.F., J.K., M.L., M.D.D., J.I., D.J.O.: conceptualization. A.I., K.-Y.P., J.H., B.C., M.M.: formal analysis. T.J.L., G.M.G., F.X., M.L., M.D.D., J.I., D.J.O.: funding acquisition. A.I., K.-Y.P., J.H., B.C., M.M., G.A.S., B.F., J.K., M.M.M., F.X.: investigation. A.I., K.-Y.P., J.H., B.F., J.K., M.M.M., T.J.L., G.M.G., F.X., M.L.: methodology. T.J.L., F.X., M.L., M.D.D., J.I., D.J.O.: project administration. T.J.L., G.M.G., F.X., M.L., M.D.D., J.I., D.J.O.: resources. J.H., A.I., B.C., T.J.L., G.M.G., F.X., D.J.O: simulation and modelling. A.I., J.H., B.C., F.X.: software. T.J.L., G.M.G., F.X., M.L., M.D.D., J.I., D.J.O.: supervision. A.I., K.-Y.P., J.H., B.C., M.M., G.M.G., M.L.: validation. A.I., K.-Y.P., J.H., B.C., G.A.S., T.J.L., G.M.G., F.X., M.L.: visualization. A.I., K.-Y.P., J.H., B.C., G.M.G., M.L., M.D.D., J.I., D.J.O.: writing (original draft). All authors: writing (review and editing).

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Correspondence to Min Lin, Mark D. Distefano, Johanna Ivaska or David J. Odde.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–17, Notes 1–3, Tables 1–3, captions for Videos 1–3 and references.

Reporting Summary

Supplementary Video 1

Evolution of U-251MG glioblastoma cell distribution on photoresponsive stiffness gradient hydrogels over time.

Supplementary Video 2

Migration of individual U-251MG cells on photoresponsive stiffness gradient hydrogels.

Supplementary Video 3

DMSO- and H-1152-treated U-251MG cells migrating on stiffness gradients.

Supplementary Data 1

Statistical source data for Supplementary Fig. 1.

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Source data

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Statistical source data.

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Source Data Fig. 2

Unprocessed western blots.

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Unprocessed western blots.

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Isomursu, A., Park, KY., Hou, J. et al. Directed cell migration towards softer environments. Nat. Mater. (2022). https://doi.org/10.1038/s41563-022-01294-2

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