Cytotoxic T cells swarm by homotypic chemokine signalling

  1. Jorge Luis Galeano Niño
  2. Sophie V Pageon
  3. Szun S Tay
  4. Feyza Colakoglu
  5. Daryan Kempe
  6. Jack Hywood
  7. Jessica K Mazalo
  8. James Cremasco
  9. Matt A Govendir
  10. Laura F Dagley
  11. Kenneth Hsu
  12. Simone Rizzetto
  13. Jerzy Zieba
  14. Gregory Rice
  15. Victoria Prior
  16. Geraldine M O'Neill
  17. Richard J Williams
  18. David R Nisbet
  19. Belinda Kramer
  20. Andrew I Webb
  21. Fabio Luciani
  22. Mark N Read
  23. Maté Biro  Is a corresponding author
  1. University of New South Wales, Australia
  2. The Walter and Eliza Hall Institute of Medical Research, Australia
  3. The Children's Hospital at Westmead, Australia
  4. University of Waterloo, Canada
  5. Kids Research, Australia
  6. Deakin University, Australia
  7. Australian National University, Australia
  8. University of Sydney, Australia

Abstract

Cytotoxic T lymphocytes (CTLs) are thought to arrive at target sites either via random search or following signals by other leukocytes. Here, we reveal independent emergent behaviour in CTL populations attacking tumour masses. Primary murine CTLs coordinate their migration in a process reminiscent of the swarming observed in neutrophils. CTLs engaging cognate targets accelerate the recruitment of distant T cells through long-range homotypic signalling, in part mediated via the diffusion of chemokines CCL3 and CCL4. Newly arriving CTLs augment the chemotactic signal, further accelerating mass recruitment in a positive feedback loop. Activated effector human T cells and chimeric antigen receptor (CAR) T cells similarly employ intra-population signalling to drive rapid convergence. Thus, CTLs recognising a cognate target can induce a localised mass response by amplifying the direct recruitment of additional T cells independently of other leukocytes.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files with extensive statistical information have been provided for all figures containing bar, box or violin plots. Complete transcriptomics and secretomics data are available in Supplementary Files 1 and 2 respectively. Custom code and notes are available at http://www.matebiro.com/software/motilisim

Article and author information

Author details

  1. Jorge Luis Galeano Niño

    EMBL Australia, Single Molecule Science node, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  2. Sophie V Pageon

    EMBL Australia, Single Molecule Science node, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1701-5551
  3. Szun S Tay

    EMBL Australia, Single Molecule Science node, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0186-8154
  4. Feyza Colakoglu

    EMBL Australia, Single Molecule Science node, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  5. Daryan Kempe

    EMBL Australia, Single Molecule Science node, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  6. Jack Hywood

    Sydney Medical School, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  7. Jessica K Mazalo

    EMBL Australia, Single Molecule Science node, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  8. James Cremasco

    EMBL Australia, Single Molecule Science node, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  9. Matt A Govendir

    EMBL Australia, Single Molecule Science node, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  10. Laura F Dagley

    Advanced Technology and Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4171-3712
  11. Kenneth Hsu

    Children's Cancer Research Unit, The Children's Hospital at Westmead, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  12. Simone Rizzetto

    Sydney Medical School, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3881-8759
  13. Jerzy Zieba

    School of Medical Sciences, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  14. Gregory Rice

    Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada
    Competing interests
    The authors declare that no competing interests exist.
  15. Victoria Prior

    Children's Cancer Research Unit, Kids Research, Westmead, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2285-5398
  16. Geraldine M O'Neill

    Children's Cancer Research Unit, The Children's Hospital at Westmead, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  17. Richard J Williams

    School of Medicine, Deakin University, Melbourne, Australia
    Competing interests
    The authors declare that no competing interests exist.
  18. David R Nisbet

    Advanced Biomaterials Lab, Research School of Engineering, Australian National University, Canberra, Australia
    Competing interests
    The authors declare that no competing interests exist.
  19. Belinda Kramer

    Children's Cancer Research Unit, The Children's Hospital at Westmead, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  20. Andrew I Webb

    Advanced Technology and Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
    Competing interests
    The authors declare that no competing interests exist.
  21. Fabio Luciani

    School of Medical Sciences, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  22. Mark N Read

    Charles Perkins Centre, University of Sydney, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  23. Maté Biro

    EMBL Australia, Single Molecule Science node, University of New South Wales, Sydney, Australia
    For correspondence
    m.biro@unsw.edu.au
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5852-3726

Funding

National Sciences and Engineering Research Council Canada (RGPIN 50503-10477 and 50503-10476)

  • Gregory Rice

National Health and Medical Research Council (GNT1135687)

  • David R Nisbet

University of Sydney CoE in Advanced Food Enginomics

  • Mark N Read

EMBL Australia

  • Maté Biro

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

Ethics

Animal experimentation: All animal breeding and experimentation were conducted in accordance with New South Wales state and Australian federal laws and animal ethics protocols overseen and approved by the University of New South Wales Animal Care and Ethics Committee (ACEC) under protocols 16/83B and 19/133B.

Human subjects: Human peripheral blood mononuclear cells (PBMCs) were obtained from healthy donors after informed consent and were used in experiments under a Human Research Ethics Committee (HREC) approved protocol (Sydney Children's Hospitals Network, LNR/13/SCHN/241).

Copyright

© 2020, Galeano Niño et al.

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

Metrics

  • 8,277
    views
  • 915
    downloads
  • 52
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Jorge Luis Galeano Niño
  2. Sophie V Pageon
  3. Szun S Tay
  4. Feyza Colakoglu
  5. Daryan Kempe
  6. Jack Hywood
  7. Jessica K Mazalo
  8. James Cremasco
  9. Matt A Govendir
  10. Laura F Dagley
  11. Kenneth Hsu
  12. Simone Rizzetto
  13. Jerzy Zieba
  14. Gregory Rice
  15. Victoria Prior
  16. Geraldine M O'Neill
  17. Richard J Williams
  18. David R Nisbet
  19. Belinda Kramer
  20. Andrew I Webb
  21. Fabio Luciani
  22. Mark N Read
  23. Maté Biro
(2020)
Cytotoxic T cells swarm by homotypic chemokine signalling
eLife 9:e56554.
https://doi.org/10.7554/eLife.56554

Share this article

https://doi.org/10.7554/eLife.56554

Further reading

    1. Computational and Systems Biology
    Matthew Millard, David W Franklin, Walter Herzog
    Research Article

    The force developed by actively lengthened muscle depends on different structures across different scales of lengthening. For small perturbations, the active response of muscle is well captured by a linear-time-invariant (LTI) system: a stiff spring in parallel with a light damper. The force response of muscle to longer stretches is better represented by a compliant spring that can fix its end when activated. Experimental work has shown that the stiffness and damping (impedance) of muscle in response to small perturbations is of fundamental importance to motor learning and mechanical stability, while the huge forces developed during long active stretches are critical for simulating and predicting injury. Outside of motor learning and injury, muscle is actively lengthened as a part of nearly all terrestrial locomotion. Despite the functional importance of impedance and active lengthening, no single muscle model has all these mechanical properties. In this work, we present the viscoelastic-crossbridge active-titin (VEXAT) model that can replicate the response of muscle to length changes great and small. To evaluate the VEXAT model, we compare its response to biological muscle by simulating experiments that measure the impedance of muscle, and the forces developed during long active stretches. In addition, we have also compared the responses of the VEXAT model to a popular Hill-type muscle model. The VEXAT model more accurately captures the impedance of biological muscle and its responses to long active stretches than a Hill-type model and can still reproduce the force-velocity and force-length relations of muscle. While the comparison between the VEXAT model and biological muscle is favorable, there are some phenomena that can be improved: the low frequency phase response of the model, and a mechanism to support passive force enhancement.

    1. Computational and Systems Biology
    2. Evolutionary Biology
    Kara Schmidlin, Sam Apodaca ... Kerry Geiler-Samerotte
    Research Article

    There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.