Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Cargo-specific recruitment in clathrin- and dynamin-independent endocytosis

Nature Cell Biology volume 23, pages 1073–1084 (2021)Cite this article

Subjects

A Publisher Correction to this article was published on 29 August 2022

This article has been updated

Abstract

Spatially controlled, cargo-specific endocytosis is essential for development, tissue homeostasis and cancer invasion. Unlike cargo-specific clathrin-mediated endocytosis, the clathrin- and dynamin-independent endocytic pathway (CLIC-GEEC, CG pathway) is considered a bulk internalization route for the fluid phase, glycosylated membrane proteins and lipids. While the core molecular players of CG-endocytosis have been recently defined, evidence of cargo-specific adaptors or selective uptake of proteins for the pathway are lacking. Here we identify the actin-binding protein Swiprosin-1 (Swip1, EFHD2) as a cargo-specific adaptor for CG-endocytosis. Swip1 couples active Rab21-associated integrins with key components of the CG-endocytic machinery—Arf1, IRSp53 and actin—and is critical for integrin endocytosis. Through this function, Swip1 supports integrin-dependent cancer-cell migration and invasion, and is a negative prognostic marker in breast cancer. Our results demonstrate a previously unknown cargo selectivity for the CG pathway and a role for specific adaptors in recruitment into this endocytic route.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Swip1 interacts directly with active Rab21.
Fig. 2: Swip1 interacts with Rab21 and β1-integrin.
Fig. 3: Swip1 interacts with members of the CG pathway.
Fig. 4: Swip1 mediates the endocytosis of active integrins via the CG pathway.
Fig. 5: Swip1 is a cargo adaptor for the CG pathway.
Fig. 6: Swip1–actin binding regulates integrin traffic.
Fig. 7: Swip1 regulates adhesion dynamics, cell motility and breast cancer progression.

Similar content being viewed by others

Data availability

The proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD016478. All other data that support the findings of this study are available within the paper and its Supplementary Information files. The tissue microarray and Swip1 mRNA expression in breast tumours analysis data are available from the corresponding authors on request. Source data are provided with this paper.

Code availability

Custom code written to perform the migration analyses has been deposited to Zenodo (https://zenodo.org/record/4812018#.YK5JdHUzaV4). The Deep Learning model used to analyse the cell migration data has also been deposited to Zenodo (https://zenodo.org/record/4811213#.YK5FGnUzaV4).

Change history

References

  1. Kaksonen, M. & Roux, A. Mechanisms of clathrin-mediated endocytosis. Nat. Rev. Mol. Cell Biol. 19, 313–326 (2018).

    Article  CAS  PubMed  Google Scholar 

  2. Mettlen, M., Chen, P.-H., Srinivasan, S., Danuser, G. & Schmid, S. L. Regulation of clathrin-mediated endocytosis. Annu. Rev. Biochem. 87, 871–896 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Sanger, A., Hirst, J., Davies, A. K. & Robinson, M. S. Adaptor protein complexes and disease at a glance. J. Cell Sci. 132, jcs222992 (2019).

  4. Mayor, S., Parton, R. G. & Donaldson, J. G. Clathrin-independent pathways of endocytosis. Cold Spring Harb. Perspect. Biol. 6, a016758 (2014).

  5. Maldonado-Báez, L., Williamson, C. & Donaldson, J. G. Clathrin-independent endocytosis: a cargo-centric view. Exp. Cell Res. 319, 2759–2769 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Ferreira, A. P. A. & Boucrot, E. Mechanisms of carrier formation during clathrin-independent endocytosis. Trends Cell Biol. 28, 188–200 (2018).

    Article  CAS  PubMed  Google Scholar 

  7. Kirkham, M. et al. Ultrastructural identification of uncoated caveolin-independent early endocytic vehicles. J. Cell Biol. 168, 465–476 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Howes, M. T. et al. Clathrin-independent carriers form a high capacity endocytic sorting system at the leading edge of migrating cells. J. Cell Biol. 190, 675–691 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sathe, M. et al. Small GTPases and BAR domain proteins regulate branched actin polymerisation for clathrin and dynamin-independent endocytosis. Nat. Commun. 9, 1835 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Thottacherry, J. J., Sathe, M., Prabhakara, C. & Mayor, S. Spoiled for choice: diverse endocytic pathways function at the cell surface. Annu. Rev. Cell Dev. Biol. 35, 55–84 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Pellinen, T. et al. Small GTPase Rab21 regulates cell adhesion and controls endosomal traffic of β1-integrins. J. Cell Biol. 173, 767–780 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Pellinen, T. et al. Integrin trafficking regulated by Rab21 is necessary for cytokinesis. Dev. Cell 15, 371–385 (2008).

    Article  CAS  PubMed  Google Scholar 

  13. Högnäs, G. et al. Cytokinesis failure due to derailed integrin traffic induces aneuploidy and oncogenic transformation in vitro and in vivo. Oncogene 31, 3597–3606 (2012).

    Article  PubMed  Google Scholar 

  14. Alanko, J. et al. Integrin endosomal signalling suppresses anoikis. Nat. Cell Biol. 17, 1412–1421 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Moreno-Layseca, P., Icha, J., Hamidi, H. & Ivaska, J. Integrin trafficking in cells and tissues. Nat. Cell Biol. 21, 122–132 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Meyer, K. & Selbach, M. Quantitative affinity purification mass spectrometry: a versatile technology to study protein–protein interactions. Front. Genet. 6, 237 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Hubner, N. C. et al. Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions. J. Cell Biol. 189, 739–754 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Jean, S. & Kiger, A. A. RAB21 activity assay using GST-fused APPL1. Bio-protocol 6, e1738 (2016).

    Article  PubMed  Google Scholar 

  19. Zhu, G. et al. Structure of the APPL1 BAR-PH domain and characterization of its interaction with Rab5. EMBO J. 26, 3484–3493 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Del Olmo, T. et al. APEX2-mediated RAB proximity labeling identifies a role for RAB21 in clathrin-independent cargo sorting. EMBO Rep. 20, e47192 (2019).

    PubMed  PubMed Central  Google Scholar 

  21. Croucher, D. R. et al. Bimolecular complementation affinity purification (BiCAP) reveals dimer-specific protein interactions for ERBB2 dimers. Sci. Signal. 9, ra69 (2016).

    Article  PubMed  Google Scholar 

  22. Bhave, M. et al. Functional characterization of 67 endocytic accessory proteins using multiparametric quantitative analysis of CCP dynamics. Proc. Natl Acad. Sci. USA 117, 31591–31602 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kumari, S. & Mayor, S. ARF1 is directly involved in dynamin-independent endocytosis. Nat. Cell Biol. 10, 30–41 (2008).

    Article  CAS  PubMed  Google Scholar 

  24. Schnitzbauer, J., Strauss, M. T., Schlichthaerle, T., Schueder, F. & Jungmann, R. Super-resolution microscopy with DNA-PAINT. Nat. Protoc. 12, 1198–1228 (2017).

    Article  CAS  PubMed  Google Scholar 

  25. Park, E. J., Yuki, Y., Kiyono, H. & Shimaoka, M. Structural basis of blocking integrin activation and deactivation for anti-inflammation. J. Biomed. Sci. 22, 51 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Caldieri, G. et al. Reticulon 3-dependent ER–PM contact sites control EGFR nonclathrin endocytosis. Science 356, 617–624 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. McMahon, H. T. & Boucrot, E. Molecular mechanism and physiological functions of clathrin-mediated endocytosis. Nat. Rev. Mol. Cell Biol. 12, 517–533 (2011).

    Article  CAS  PubMed  Google Scholar 

  28. Thottacherry, J. J. et al. Mechanochemical feedback control of dynamin independent endocytosis modulates membrane tension in adherent cells. Nat. Commun. 9, 4217 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Krugmann, S. et al. Cdc42 induces filopodia by promoting the formation of an IRSp53:Mena complex. Curr. Biol. 11, 1645–1655 (2001).

    Article  CAS  PubMed  Google Scholar 

  30. Vaggi, F. et al. The Eps8/IRSp53/VASP network differentially controls actin capping and bundling in filopodia formation. PLoS Comput. Biol. 7, e1002088 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Disanza, A. et al. CDC42 switches IRSp53 from inhibition of actin growth to elongation by clustering of VASP. EMBO J. 32, 2735–2750 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Lundmark, R. et al. The GTPase-activating protein GRAF1 regulates the CLIC/GEEC endocytic pathway. Curr. Biol. 18, 1802–1808 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Hemalatha, A. & Mayor, S. Recent advances in clathrin-independent endocytosis. F1000Research 8, 138 (2019).

  34. Park, K. R. et al. Structural implications of Ca2+-dependent actin-bundling function of human EFhd2/Swiprosin-1. Sci. Rep. 6, 39095 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ariotti, N. et al. Modular detection of GFP-labeled proteins for rapid screening by electron microscopy in cells and organisms. Dev. Cell 35, 513–525 (2015).

    Article  CAS  PubMed  Google Scholar 

  36. Paul, N. R., Jacquemet, G. & Caswell, P. T. Endocytic trafficking of integrins in cell migration. Curr. Biol. 25, R1092–R1105 (2015).

    Article  CAS  PubMed  Google Scholar 

  37. Lim, K. B. et al. The Cdc42 effector IRSp53 generates filopodia by coupling membrane protrusion with actin dynamics. J. Biol. Chem. 283, 20454–20472 (2008).

    Article  CAS  PubMed  Google Scholar 

  38. Miki, H., Yamaguchi, H., Suetsugu, S. & Takenawa, T. IRSp53 is an essential intermediate between Rac and WAVE in the regulation of membrane ruffling. Nature 408, 732–735 (2000).

    Article  CAS  PubMed  Google Scholar 

  39. Pipathsouk, A. et al. WAVE complex self-organization templates lamellipodial formation. J. Cell Biol. 220, e202003086 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Koronakis, V. et al. WAVE regulatory complex activation by cooperating GTPases Arf and Rac1. Proc. Natl Acad. Sci. USA 108, 14449–14454 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Schlienger, S., Ramirez, R. A. M. & Claing, A. ARF1 regulates adhesion of MDA-MB-231 invasive breast cancer cells through formation of focal adhesions. Cell. Signal. 27, 403–415 (2015).

    Article  CAS  PubMed  Google Scholar 

  42. Norman, J. C. et al. ARF1 mediates paxillin recruitment to focal adhesions and potentiates Rho-stimulated stress fiber formation in intact and permeabilized Swiss 3T3 fibroblasts. J. Cell Biol. 143, 1981–1995 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Shafaq-Zadah, M. et al. Persistent cell migration and adhesion rely on retrograde transport of β1 integrin. Nat. Cell Biol. 18, 54–64 (2016).

    Article  CAS  PubMed  Google Scholar 

  44. Ezratty, E. J., Bertaux, C., Marcantonio, E. E. & Gundersen, G. G. Clathrin mediates integrin endocytosis for focal adhesion disassembly in migrating cells. J. Cell Biol. 187, 733–747 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Sahgal, P. et al. GGA2 and RAB13 promote activity-dependent β1-integrin recycling. J. Cell Sci. 132, jcs233387 (2019).

    Article  CAS  PubMed  Google Scholar 

  46. Hornbruch-Freitag, C., Griemert, B., Buttgereit, D. & Renkawitz-Pohl, R. Drosophila Swiprosin-1/EFHD2 accumulates at the prefusion complex stage during Drosophila myoblast fusion. J. Cell Sci. 124, 3266–3278 (2011).

    Article  CAS  PubMed  Google Scholar 

  47. Fan, C.-C. et al. EFHD2 promotes epithelial-to-mesenchymal transition and correlates with postsurgical recurrence of stage I lung adenocarcinoma. Sci. Rep. 7, 14617 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Zhang, S. et al. Swiprosin-1 deficiency impairs macrophage immune response of septic mice. JCI Insight 3, e95396 (2018).

    Article  PubMed Central  Google Scholar 

  49. Tu, Y. et al. EFhd2/swiprosin-1 regulates LPS-induced macrophage recruitment via enhancing actin polymerization and cell migration. Int. Immunopharmacol. 55, 263–271 (2018).

    Article  CAS  PubMed  Google Scholar 

  50. Reimer, D. et al. B cell speed and B-FDC contacts in germinal centers determine plasma cell output via Swiprosin-1/EFhd2. Cell Rep. 32, 108030 (2020).

    Article  CAS  PubMed  Google Scholar 

  51. Paul, F. E., Hosp, F. & Selbach, M. Analyzing protein–protein interactions by quantitative mass spectrometry. Methods 54, 387–395 (2011).

    Article  CAS  PubMed  Google Scholar 

  52. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    Article  CAS  PubMed  Google Scholar 

  53. Borchers, H. W. pracma: Practical Numerical Math Functions (R Foundation for Statistical Computing, 2019).

  54. Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    Article  CAS  PubMed  Google Scholar 

  55. Ovesný, M., Křížek, P., Borkovec, J., Svindrych, Z. & Hagen, G. M. ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging. Bioinformatics 30, 2389–2390 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  57. Martens, K. J. A., Bader, A. N., Baas, S., Rieger, B. & Hohlbein, J. Phasor based single-molecule localization microscopy in 3D (pSMLM-3D): an algorithm for MHz localization rates using standard CPUs. J. Chem. Phys. 148, 123311 (2018).

    Article  PubMed  Google Scholar 

  58. Farage, E. & Caswell, P. T. Quantitative analysis of integrin trafficking. Methods Mol. Biol. 2217, 251–263 (2021).

    Article  CAS  PubMed  Google Scholar 

  59. Arjonen, A., Alanko, J., Veltel, S. & Ivaska, J. Distinct recycling of active and inactive β1 integrins. Traffic 13, 610–625 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Berginski, M. E. & Gomez, S. M. The Focal Adhesion Analysis Server: a web tool for analyzing focal adhesion dynamics. F1000Research 2, 68 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Krull, A., Buchholz, T.-O. & Jug, F. Noise2Void—learning denoising from single noisy images. IEEE/CVF Conf. Comp. Vision Pattern Recog. (CVPR) 2019, 2124–2132 (2019).

    Google Scholar 

  62. von Chamier, L. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat. Commun. 12, 2276 (2021).

    Article  Google Scholar 

  63. Arganda-Carreras, I. et al. Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33, 2424–2426 (2017).

    Article  CAS  PubMed  Google Scholar 

  64. Gilles, J.-F., Dos Santos, M., Boudier, T., Bolte, S. & Heck, N. DiAna, an ImageJ tool for object-based 3D co-localization and distance analysis. Methods 115, 55–64 (2017).

    Article  CAS  PubMed  Google Scholar 

  65. Jacquemet, G. Archived source code for cargo-specific recruitment in clathrin and dynamin-independent endocytosis. Zenodo https://doi.org/10.5281/zenodo.4812018 (2021).

  66. Fazeli, E. et al. Automated cell tracking using StarDist and TrackMate. F1000Research 9, 1279 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Tinevez, J.-Y. et al. TrackMate: an open and extensible platform for single-particle tracking. Methods 115, 80–90 (2017).

    Article  CAS  PubMed  Google Scholar 

  68. Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. in Medical Image Computing and Computer Assisted Intervention—MICCAI 2018 (eds Frangi, A. F. et al.) 265–273 (Springer, 2018); https://doi.org/10.1007/978-3-030-00934-2_30

  69. Al-Akhrass, H., Ivaska, J. & Jacquemet, G. Stardist model and training dataset for automated tracking of MDA-MB-231 and BT20 cells. Zenodo https://doi.org/10.5281/zenodo.4811213 (2021).

  70. Postma, M. & Goedhart, J. PlotsOfData—a web app for visualizing data together with their summaries. PLOS Biol. 17, e3000202 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Heuser, V. D. et al. Formin proteins FHOD1 and INF2 in triple-negative breast cancer: association with basal markers and functional activities. Breast Cancer 12, 1178223418792247 (2018).

    PubMed  PubMed Central  Google Scholar 

  72. Dunkler, D., Ploner, M., Schemper, M. & Heinze, G. Weighted Cox regression using the R package coxphw. J. Stat. Softw. 84, 1–26 (2018).

    Article  Google Scholar 

  73. Legler, K. et al. Reduced mannosidase MAN1A1 expression leads to aberrant N-glycosylation and impaired survival in breast cancer. Br. J. Cancer 118, 847–856 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Stamm, H. et al. Targeting the TIGIT–PVR immune checkpoint axis as novel therapeutic option in breast cancer. OncoImmunology 8, e1674605 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Milde-Langosch, K. et al. Prognostic relevance of glycosylation-associated genes in breast cancer. Breast Cancer Res. Treat. 145, 295–305 (2014).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We acknowledge Euro-BioImaging (www.eurobioimaging.eu) for providing access to imaging technologies and services via the Finnish Advanced Light Microscopy Node (Turku, Finland). We thank P. Laasola, J. Siivonen and A. Gödicke for technical assistance, the Ivaska laboratory for critical reading of the manuscript and H. Hamidi for editing of the manuscript. We thank V. Faila, B. Zobiak and M. Saari for help with the microscopes and D. Mielenz, A. Itzen and P. Jackson for providing reagents. The Cell Imaging and Cytometry core (Turku Bioscience Centre, University of Turku and Åbo Akademi University and Biocenter Finland), the Euro-BioImaging Finnish Node, the Electron Microscopy Unit and Histocore at the University of Turku and the UMIF (UKE Microscopy Imaging Facility, Universitätsklinikum Hamburg, Eppendorf) are acknowledged for services, instrumentation and expertise. This study has been supported by the DFG (grant no. VE 750/2-1) and Alexander von Humboldt-Foundation (S.V.), ProExzellenzia Hamburg and Academy of Finland (grant nos 321493 (to P.M.-L.) and 325464 (to J.I.)), an ERC CoG grant (grant no. 615258 to J.I.), grants and a fellowship from the National Health and Medical Research Council of Australia (grant nos APP1140064 and APP1150083 and fellowship no. APP1156489 to R.G.P.), and the Australian Research Council Centre of Excellence in Convergent Bio-Nano Science and Technology (grant no. CE140100036 to R.G.P.). We acknowledge a University Grants Commission (UGC) graduate fellowship (to R.G.), and S.M. acknowledges the support of India Alliance Margdarshi fellowship (IN M/15/1/502018), Department of Atomic Energy (Project Identification No. RTI 4006), and Department of Science and Technology (J.C. Bose Fellowship), Government of India.

Author information

Authors and Affiliations

  1. Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland

    Paulina Moreno-Layseca, Niklas Z. Jäntti, Guillaume Jacquemet, Hussein Al-Akhrass, James R. W. Conway & Johanna Ivaska

  2. University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany

    Paulina Moreno-Layseca, Leticia Oliveira-Ferrer, Pasquale Cervero, Stefan Linder, Martin Aepfelbacher & Stefan Veltel

  3. National Centre for Biological Science (TIFR), Bangalore, India

    Rashmi Godbole & Satyajit Mayor

  4. The University of Trans-Disciplinary Health Sciences and Technology (TDU), Bangalore, India

    Rashmi Godbole

  5. Max-Delbrück-Centrum für Molekulare Medizin (MDC), Berlin, Germany

    Christian Sommer, Henrik Zauber & Matthias Selbach

  6. Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku, Finland

    Guillaume Jacquemet

  7. Institute of Biomedicine, Faculty of Medicine, University of Turku, Turku, Finland

    Pauliina Kronqvist

  8. Auria Biobank, Turku University Hospital and University of Turku, Turku, Finland

    Roosa E. Kallionpää

  9. Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia

    James Rae & Robert G. Parton

  10. Centre for Microscopy and Microanalysis, University of Queensland, Brisbane, Queensland, Australia

    Robert G. Parton

  11. IFOM, Fondazione Istituto FIRC di Oncologia Molecolare and University of Milan, Milan, Italy

    Andrea Disanza & Giorgio Scita

  12. Hochschule Bremen, City University of Applied Sciences, Bremen, Germany

    Stefan Veltel

  13. Department of Life Sciences, University of Turku, Turku, Finland

    Johanna Ivaska

  14. InFLAMES Research Flagship Center, University of Turku, Turku, Finland

    Johanna Ivaska

Authors
  1. Paulina Moreno-Layseca
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Niklas Z. Jäntti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rashmi Godbole
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christian Sommer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guillaume Jacquemet
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hussein Al-Akhrass
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. James R. W. Conway
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Pauliina Kronqvist
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Roosa E. Kallionpää
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Leticia Oliveira-Ferrer
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Pasquale Cervero
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Stefan Linder
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Martin Aepfelbacher
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Henrik Zauber
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. James Rae
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Robert G. Parton
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Andrea Disanza
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Giorgio Scita
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Satyajit Mayor
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Matthias Selbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Stefan Veltel
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Johanna Ivaska
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: S.V. and J.I. Methodology: P.M.-L., N.Z.J., C.S., R.G., G.J., H.A.-A., J.R.W.C., M.S., S.M., S.V., R.G.P., J.R. and J.I. Formal analysis: P.M.-L., N.Z.J., C.S., R.G., G.J., P.K., R.E.K., L.O.-F., H.Z., S.M., M.S., S.V. R.G.P., J.R. and J.I. Investigation: S.V., P.M.-L. and J.I. Resources: P.C., S.L., M.A., G.S. and A.D. Writing–original draft: P.M.-L. and J.I. Writing–review and editing: S.V., P.M.-L., G.J., N.Z.J., J.R.W.C., R.G.P. and J.I. Supervision: P.M.-L., S.V. and J.I. Funding acquisition: S.V., M.A., P.M.-L., R.G.P. and J.I.

Corresponding authors

Correspondence to Stefan Veltel or Johanna Ivaska.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Cell Biology thanks Patrick Caswell, and the other, anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Swip1 (EFHD2) is an interactor of Rab21.

SILAC proteomics analysis of GFP-Trap pulldowns in MDA-MB-231 cells expressing GFP-WT-Rab21 vs. GFP–DN-Rab21 (T31N inactive GDP-bound/nucleotide-free mutant) (a); GFP-CA-Rab21 (Q76L constitutively active GTP-bound mutant) vs. GFP (b); GFP-WT-Rab21 vs. GFP (c) or GFP-WT-Rab21 vs. GFP-CA-Rab21 (d). (a-d) Each spot in the plots corresponds to one identified protein by mass spectrometry. Plots are representative of 2 independent experiments, forward and reverse; where every experiment consists of two independent affinity purifications. Plots show mean fold-changes from forward and reverse experiments against absolute protein abundances (intensity-based absolute quantification, iBAQ). Abundance bins were defined by including 1000 proteins in a subsequent order. Log10 fold change of proteins were tested for significance using double-sided significance B tests. No multiple hypothesis correction method was applied, due to the small number of selected proteins for the statistical analysis. Proteins with a P-value < 0.01 are projected as a triangle, while non-significant proteins are shown as circles. P-values are depicted in the figure for a selected set of proteins relevant to the work. Proteins in red are markedly enriched in the CA- or WT-Rab21 fraction and proteins in blue are known endosomal proteins clathrin (CLTA, CLTB, CLTC), AP2 (AP2A1, AP2B1, AP2M1, AP2S1), caveolin (CAV1) and dynamin II (DNM2), which are not specifically enriched. Swip1 was strongly enriched in the GFP-WT-Rab21 vs. GFP–DN-Rab21 (a) and GFP-CA-Rab21 and GFP-WT-Rab21 fractions compared to GFP fractions (b, d), and it was equally enriched in the GFP-CA-Rab21 compared to the GFP-WT-Rab21 fraction (c). Clathrin (CLTA, CLTB, CLTC), AP2 (AP2A1, AP2B1, AP2M1, AP2S1), caveolin (CAV1) and dynamin II (DNM2) are not strongly enriched in any fraction. (e). Representative immunoblots of GFP-Trap pulldowns from MDA-MB-231 cells transfected with GFP, GFP-WT-Rab21, GFP-WT-Rab5, GFP–DN-Rab5 or GFP-CA-Rab5 stained for endogenous swip1 and β1 integrin. Blots are representative of 2 independent experiments. Unprocessed blots are provided in Source data.

Source data

Extended Data Fig. 2 BiFC/BiCAP approaches to assess the interaction between Rab21 and Swip1.

(a) Cartoon of the BiFC/BiCAP approach used to image (BiFC) and biochemically detect (BiCAP) the interactions between Rab21 and Swip1 in live cells and from cell lysate. (b) MDA-MB-231 cells were transfected with the indicated constructs and lysed. Proteins expressed in the transfected cells were detected by immunoblotting. Immunoblot shows the size of the bands detected by a polyclonal anti-GFP antibody. Blots are representative of 2 independent experiments. (c) Representative confocal microscopy BiFC images of MDA-MB-231 cells expressing V1–Rab21 and V2–Swip1 MDA-MB-231 and immunostained for endosomal markers. Insets show BiFC colocalizing with the indicated endosomal markers. Scale bars, 10 µm (main figures) and 1 µm (insets). Representative pictures of 3 independent experiments. Unprocessed blots are provided in Source data.

Source data

Extended Data Fig. 3 SIM images of Swip1 with CG components and Rab proteins.

(a) Confocal micrographs of MDA-MB-231 cells expressing mScarlet-I-Swip1 and GFP–Rab21, and immunostained for β1-integrin (12G10 antibody). Arrows show areas of co-localization in endosomal structures. Representative pictures of n = 3 independent experiments. Scale bar, 10 µm. (b) Full pictures from Fig. 2d, e and 3d. x-y projections of MDA-MB-231 cells expressing mScarlet-I-Swip1 and either GFP–Rab21, Arf1–GFP or GFP–IRSp53 were imaged using structured illumination microscopy (SIM). Blue and yellow squares highlight the regions of interest (ROI) shown as x-z projections in Fig. 2d, e and Figure 3d. Representative pictures of n = 3 independent experiments. Scale bars, 5 µm (2d), 8 µm (2e) and 8 µm (3d); insets, 1 µm. (c) SIM x-z projections of MDA-MB-231 cells expressing mScarlet-I-Swip1 and immunostained for endogenous Rab proteins and quantification of Rab protein co-localization with mScarlet-I-Swip1. Each dot represents the co-localization ratio in one cell. Data are presented as mean values ± 95 % CI. Statistical significance was assessed with two-sided Mann–Whitney tests, where n is the total number of cells pooled from 3 independent experiments. P values calculated compared to Rab21 condition. **** P < 0.0001. Number of cells analysed over 3 independent experiments: n = 39 cells for Rab21, n = 35 for Rab5, n = 31 for Rab7, n = 36 for Rab11. Scale bars, 0.5 µm. Numerical source data are provided in Source data.

Source data

Extended Data Fig. 4 GFP–Swip1 co-localizes with Arf1–HA at the TIRF plane and regulates β1-integrin endocytosis via the CG pathway.

(a) MDA-MB-231 cells were co-transfected with GFP-Swip1 and Arf1-HA constructs, fixed, stained with probes for DNA paint and imaged using SMLM. Examples of the structures formed by GFP-Swip1 at the proximity of the ECM interphase are shown. Representative pictures of n = 2 independent experiments. Scale bars, 2 µm (main images) and 0.5 µm (insets). (b) Representative immunoblots of control- and Swip1-silenced MDA-MB-231 cell lysates blotted as indicated, calnexin is included as a loading control. Blots are representative of n = 3 independent experiments. (c) FACS analyses of cell surface β1-integrin in control- and Swip1-silenced MDA-MB-231 cells using the indicated antibodies. Bar charts show data as geometric mean values of 10,000 cells ± SEM over n = 3 independent experiments. Statistical significance was assessed with two-tailed Wilcoxon matched-pairs signed-rank tests (n = 3 independent experiments), ns = not significant. (d) Quantification of biotinylated α2-, α3- or αv- integrin internalization in Swip1-silenced MDA-MB-231 cells (siRNA #2) after the indicated times determined with ELISA in the presence or absence of 100 µM primaquine. Bar charts show data as mean values ± SEM. Statistical significance was assessed using multiple-comparison t-tests for paired data, with the post-hoc Holm–Sidak method, with alpha = 5.000%. Each row was analysed individually, without assuming a consistent SD.*P = 0.01540; n = 3 biologically independent experiments. Unprocessed blots and numerical source data are provided in Source data.

Source data

Extended Data Fig. 5 Active β1-integrins are endocytosed via the CG pathway and CME.

(a) Representative micrographs and quantification of β1-integrin uptake in control- or IRSp53- or Arf1-silenced MDA-MB-231 cells. Representative immunoblots to validate Swip1 silencing. Scale bars, 10 µm. (b) Representative micrographs and quantification of endocytosed or surface (no endocytosis or acid wash) of murine β1-integrin (9EG7 antibody) in isogenic IRSp53−/− MEFs: IRSp53-KO-pBABE (-/-) and IRSp53-KO-pBABE-IRSp53 (WT) in which the expression of IRSp53 has been restored. Representative immunoblots of cell lysates blotted as indicated. Scale bars, 10 µm. (c) Representative micrographs of β1-integrin uptake in GFP- and GFP-Swip1-expressing cells and quantification of integrin uptake at the indicated times. Scale bars, 10 µm. (d) PLA with the indicated antibodies (from Figure 4i) in MDA-MB-231 cells expressing either GFP or GFP-Swip1. Plot shows the background controls for each. (e) Representative images and quantification of co-localization of β1-integrin-AF488 (12G10) with TMR-10 kDa dextran or AF647-transferrin after 1 min simultaneous uptake in MDA-MB-231 cells. Yellow arrows show regions of co-localization between β1-integrin and TMR-dextran and cyan arrows show regions where β1-integrin co-localizes with AF647-transferrin. Scale bars, 10 µm. For all plots, data are presented as mean values ± 95% CI. Statistical significance was assessed with two-sided Mann–Whitney tests, where n is the total number of cells pooled from 3 independent experiments (a-d) or from 2 independent experiments (e). (a) ****P<0.0001, (b) ****P<0.0001, ns = not significant, (c) *P = 0.0478, **P = 0.0058, (c) ***P = 0.0006, (d) ***P = 0.0001, ****P<0.0001, (e) ****P<0.0001. Number of analysed cells: (a) siCTRL, n = 103 cells, Arf1 siRNA #1, n = 177 cells, Arf1 siRNA #2, n = 157 cells, siCTRL, n = 100, IRSp53 siRNA #1, n = 142 cells, IRSp53 siRNA #2, n = 124 cells. (b) Endocytosed and surface β1-integrin, respectively: IRSp53 WT, n = 108 & n = 55 cells, IRSp53 KO, n = 100 & n = 60 cells. (c) GFP, n = 10, n = 22, n = 30 and n = 24 cells and GFP-Swip1, n = 15, n = 22, n = 32 and n = 26 cells, respectively. (d) For GFP, 12G10 only, n = 75 cells; IRSp53 only, n = 113 cells; 12G10 + IRSp53, n = 106 cells. For GFP-Swip1, 12G10 only, n = 78 cells; IRSp53 only, n = 92 cells; 12G10 + IRSp53, n = 111 cells. (e) β1-integrin-dextran, n = 22 cells; β1-integrin-transferrin, n = 22 cells and transferrin-dextran, n = 18 cells. Unprocessed blots and numerical source data are provided in Source data.

Source data

Extended Data Fig. 6 Swip1 regulates β1-integrin endocytosis via the CG pathway.

(a) Representative micrographs and quantification of β1-integrin and MHCI uptake at the 15 min time point in control or Swip1 (siRNA#1 or #2) silenced MDA-MB-468 and BT-20 cells. Representative immunoblots of cell lysates blotted for Swip1. Calnexin is included as a loading control. Scale bars, 10 µm. (b) Representative immunoblot of MDA-MB-231, BT-20 and MDA-MB-468 cell lysates blotted for Swip1. α-tubulin is included as a loading control. (c) Representative micrographs and quantification of transferrin uptake at the 15 min time point in control- or Swip1-silenced MDA-MB-231 cells. Representative immunoblots of cell lysates blotted as indicated. Calnexin is included as a loading control. Scale bars, 10 µm. (d) Cell-surface labelled EGFR uptake in control and Swip1-silenced MDA-MB-231 cells treated with 10 ng/ml EGF was analysed using flow cytometry in 10 000 non-permeabilized cells per measurement. Plot shows 4 measurements from n = 2 independent experiments. Immunoblot to validate swip1 silencing is representative of 2 independent experiments. GAPDH is included as a loading control. Immunoblots in panels (a-c) and scatter dot-plots (a-c) are representative of 3 independent experiments. Data are presented as mean values ± 95% CI. Statistical significance was assessed with two-sided Mann–Whitney tests, where n is the total number of cells pooled across 3 independent experiments. P values calculated compared to siCTRL condition: *P = 0.0137, **P = 0.0007, ***P = 0.0006, ****P<0.0001, ns = not significant. Number of analysed cells over 3 independent experiments: (a) For BT-20 12G10 and MHCI uptake, respectively, siCTRL, n = 79 & 91 cells; Swip1 siRNA#1, n = 76 & 91 cells; Swip1 siRNA#2, n = 66 & 75 cells. For MDA-MB-468 12G10 and MHCI uptake, respectively, siCTRL, n = 134 & 132 cells; Swip1 siRNA#1, n = 98 & 117 cells; Swip1 siRNA#2, n = 98 & 101 cells. (c) siCTRL, n = 74 cells; Swip1 siRNA#1, n = 103 cells. Unprocessed blots and numerical source data are provided in Source data.

Source data

Extended Data Fig. 7 Swip1 is required for specific entry of β1-integrin via the CG pathway following changes in membrane tension; Swip1 does not directly interact with IRSp53 and it does not affect the number of Rab21-containing vesicles.

(a) Representative images of endocytosed AF488-anti-β1-integrin (12G10 antibody) and TMR-10 kDa dextran in MDA-MB-231 cells after treatment of cells with hypotonic media followed by a shift to isotonic media. Scale bars, 10 µm. Pictures are representative of n = 3 independent experiments. Quantification of these experiments is shown in Figure 5e. (b) Recombinant purified his-Swip1 or his-VASP (positive control) proteins were spotted at the indicated concentrations on nitrocellulose filters. BSA was used as negative control. Nitrocellulose membranes were incubated with recombinant purified IRSp53 at the indicated concentrations and immunoblotted with an anti-IRSp53-specific antibody. (c) Recombinant purified his-Swip1 (500 nM) or his-VASP (50 nM, positive control) proteins were spotted at the indicated concentrations on nitrocellulose filters. BSA was used as negative control. Nitrocellulose membranes were incubated with recombinant purified IRSp53 at the indicated concentrations and immunoblotted with an anti-IRSp53-specific antibody. Pictures are representative of n = 3 independent experiments (a-c). (d) Representative micrographs of MDA-MB-231 cells immunostained for endogenous Rab21 and counterstained with DAPI and WGA-AF647 as a plasma membrane marker to define the cell outlines. The outlines of each cell are indicated in green dashed lines. The number of vesicles in 100 MDA-MB-231 cells per condition was quantified using the IMARIS vesicle detection function. Data are presented as mean values ± 95 % CI. Statistical significance was assessed with two-sided Mann–Whitney tests, where n is the total number of cells pooled across 3 independent experiments (n = 100 cells per condition). P values calculated compared to siCTRL condition: *** P < 0.0001, ns: not significant. Scale bar, 10 µm. Numerical source data are provided in Source data.

Source data

Extended Data Fig. 8 Arf1 and IRSp53 regulate focal adhesion formation and Swip1 and Rab21 regulate cell migration.

(a) Representative immunoblots to validate Swip1 silencing and mScarlet-I-Swip1 or mScarlet-I-∆EF1 overexpression in the experiments shown in Figure 6i. Blot is representative of 3 independent experiments. (b) Representative masks of vinculin-containing focal adhesions in control-, IRSp53- or Arf1-silenced MDA-MB-231 cells and quantification of adhesion number and total area per cell in cells plated on collagen I. Data are presented as mean values ± 95% CI. Statistical significance was assessed with two-sided Mann–Whitney tests, where n is the total number of cells pooled across 3 independent experiments. P values calculated compared to siCTRL condition: ***P = 0.0003, ****P<0.0001. Number of analysed cells over 3 independent experiments: siCTRL, n = 62 cells; Arf1 siRNA #1, n = 60 cells; Arf1 siRNA #2, n = 61 cells; siCTRL, n = 64 cells, IRSp53 siRNA #1, n = 62 cells and IRSp53 siRNA #2, n = 65 cells. Representative immunoblots of 3 independent experiments to validate IRSp53 and Arf1 silencing are shown. Scale bars, 10 µm. (c) Representative phase contrast pictures of the scratch wound after 18 h. Pictures are representative of 3 independent experiments. Quantification of these experiments is shown in Figure 7d. Scale bar, 100 µm. Unprocessed blots and numerical source data are provided in Source data.

Source data

Extended Data Fig. 9 Swip1 is required for random cell migration.

Random cell migration speed of MDA-MB-231 (a) and MDA-MB-468 (b) cells upon Swip1 silencing. In all cases, cells were labelled using sir-DNA and their migration behaviour recorded over time using a widefield microscope. Cell nuclei were automatically tracked using StarDist and TrackMate. Cell track speed and mean displacement plots are displayed. Boxplots display the median and quartiles of the data. Whiskers display the 2nd percentile and the 98th percentile. Mean displacement plots show mean values ± standard deviation. For all panels, P-values were determined using a randomization test, where n is the total number of cells pooled across 3 independent experiments, ***P = <0.001. Number of cells analysed over 3 independent experiments: MDA-MB-231, siCTRL, n = 869 cells; siSwip1 #1, n = 553 cells; siSwip1 #2, n = 506 cells; MDA-MB-468, siCTRL, n = 331 cells; siSwip1 #1, n = 206 cells; siSwip1 #2, n = 235 cells. Numerical source data are provided in Source data.

Source data

Extended Data Fig. 10 EFHD2 (Swip1) mRNA expression correlates with a more metastatic breast cancer phenotype and Swip1 immunostaining at the plasma membrane correlates with higher death risk and metastasis incidence in TNBC.

(a) Analysis of Swip1 mRNA expression using qPCR in 192 breast tumours. Tumours were categorized into two groups: high EFHD2 expression (higher than the median of the entire collection) or low EFHD2 expression (lower expression than the median). The χ2-test was used for statistical analyses. EFHD2 mRNA levels based on tumour type and tumour grade are shown. (b) Validation of anti-Swip1 antibody specificity in IHC was carried out with agarose-embedded and formalin-fixed paraffin-embedded cell pellets from control- or Swip1-silenced MDA-MB-231 cells (siRNA #1 and #2). Scale bars, 50 µm. (c) Quantification of the percentage of breast cancer tumours from TNBC tissue microarray with high, medium or low Swip1 staining at the plasma membrane. (d) Kaplan–Meier plot shows overall survival of 133 triple negative breast cancer patients with high (H, red) or medium–low (M-L, blue) membranal staining of Swip1 in their tumour centre sample. The hazard ratio of high vs. medium–low Swip1 immuno-positivity was 2.34 (95% CI 1.25 to 4.41). The hazard ratios after adjustment for Ki67, 2.51 (95% CI 1.26 to 5.01); for tumour size, 2.12 (95% CI 1.08 to 4.18); for lymph node metastasis status 2.26 (95% CI 1.19 to 4.31) and for tumour grade 2.44 (95% CI 1.25 to 4.74). (e) Lymph node metastasis incidence in 130 TNBC patients from the same cohort, Fisher’s exact test p = 0.037. A higher proportion of patients with high membranal staining of Swip1 (> = 80%) had lymph node metastasis. Numerical source data are provided in Source data.

Source data

Supplementary information

Supplementary Information

Supplementary Fig. 1.

Reporting Summary

Peer Review Information

Supplementary Table 1

Ratios (log10 transformed) of the proteins identified by mass spectrometry and antibodies used in this study.

Supplementary Video 1

MDA-MB-231 cells transiently co-expressing V1–Rab21 and V2–Swip1 (A) or expressing Venus control (B) were imaged using TIRF microscopy (84.5° angle) every 500 ms for 5 min.

Supplementary Video 2

MDA-MB-231 cell transiently co-expressing V1–Rab21, V2–Swip1 and mCherry–IRSp53, imaged using TIRF microscopy (84.5° angle) every 500 ms for 2.5 min. The white squares show the ROI, where V1–Rab21 + V2–Swip1 puncta (green) travel to reach mCherry–IRSp53 puncta (magenta) before exiting the TIRF plane. One of the ROI is depicted as a time-lapse in Fig. 3g.

Supplementary Video 3

MDA-MB-231 cell transiently expressing GFP–Swip1 and treated with SiR-Actin, imaged using semi-super-resolution microscopy.

Supplementary Video 4

MDA-MB-231 cell (siCTRL) stably expressing GFP–Rab21 and imaged using TIRF microscopy (76.43° angle) every 500 ms for 2 min.

Supplementary Video 5

MDA-MB-231 cell (Swip1 siRNA2) stably expressing GFP–Rab21 and imaged using TIRF microscopy (76.43° angle) every 500 ms for 2 min.

Supplementary Video 6

MDA-MB-231 cell transiently co-expressing V1–Rab21, V2–Swip1 and mKate2–paxillin, imaged using TIRF microscopy (84.5° angle) every 500 ms for 50 s.

Source data

Source Data Fig. 1

Unprocessed blots.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Unprocessed blots.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Unprocessed blots.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Unprocessed blots.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Unprocessed blots.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 7

Unprocessed blots.

Source Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 1

Unprocessed blots.

Source Data Extended Data Fig. 2

Unprocessed blots.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Unprocessed blots.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Unprocessed blots.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Unprocessed blots.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 8

Unprocessed blots.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Source Data Extended Data Fig. 10

Statistical source data.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moreno-Layseca, P., Jäntti, N.Z., Godbole, R. et al. Cargo-specific recruitment in clathrin- and dynamin-independent endocytosis. Nat Cell Biol 23, 1073–1084 (2021). https://doi.org/10.1038/s41556-021-00767-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41556-021-00767-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer