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. EMBL Australia, Australia
  2. University of New South Wales, Australia
  3. The University of Sydney, Australia
  4. The Walter and Eliza Hall Institute of Medical Research, Australia
  5. The Children's Hospital at Westmead, Australia
  6. University of Waterloo, Canada
  7. Kids Research, Australia
  8. Deakin University, Australia
  9. Australian National University, Australia
  10. 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

    Single Molecule Science node, School of Medical Sciences, EMBL Australia, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  2. Sophie V Pageon

    Medicine, 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

    Single Molecule Science node, School of Medical Sciences, EMBL Australia, 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

    Single Molecule Science node, School of Medical Sciences, EMBL Australia, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  5. Daryan Kempe

    Single Molecule Science node, School of Medical Sciences, EMBL Australia, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  6. Jack Hywood

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

    Single Molecule Science node, School of Medical Sciences, EMBL Australia, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  8. James Cremasco

    Single Molecule Science node, School of Medical Sciences, EMBL Australia, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  9. Matt A Govendir

    Single Molecule Science node, School of Medical Sciences, EMBL Australia, 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

    Medicine, 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

    Single Molecule Science node, School of Medical Sciences, EMBL Australia, 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

  • 8,229
    views
  • 913
    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
    2. Ecology
    Lenore Pipes, Rasmus Nielsen
    Tools and Resources

    Environmental DNA (eDNA) is becoming an increasingly important tool in diverse scientific fields from ecological biomonitoring to wastewater surveillance of viruses. The fundamental challenge in eDNA analyses has been the bioinformatical assignment of reads to taxonomic groups. It has long been known that full probabilistic methods for phylogenetic assignment are preferable, but unfortunately, such methods are computationally intensive and are typically inapplicable to modern Next-Generation Sequencing data. We here present a fast approximate likelihood method for phylogenetic assignment of DNA sequences. Applying the new method to several mock communities and simulated datasets, we show that it identifies more reads at both high and low taxonomic levels more accurately than other leading methods. The advantage of the method is particularly apparent in the presence of polymorphisms and/or sequencing errors and when the true species is not represented in the reference database.

    1. Computational and Systems Biology
    2. Physics of Living Systems
    Natanael Spisak, Gabriel Athènes ... Aleksandra M Walczak
    Tools and Resources

    B-cell repertoires are characterized by a diverse set of receptors of distinct specificities generated through two processes of somatic diversification: V(D)J recombination and somatic hypermutations. B cell clonal families stem from the same V(D)J recombination event, but differ in their hypermutations. Clonal families identification is key to understanding B-cell repertoire function, evolution and dynamics. We present HILARy (High-precision Inference of Lineages in Antibody Repertoires), an efficient, fast and precise method to identify clonal families from single- or paired-chain repertoire sequencing datasets. HILARy combines probabilistic models that capture the receptor generation and selection statistics with adapted clustering methods to achieve consistently high inference accuracy. It automatically leverages the phylogenetic signal of shared mutations in difficult repertoire subsets. Exploiting the high sensitivity of the method, we find the statistics of evolutionary properties such as the site frequency spectrum and 𝑑𝑁∕𝑑𝑆 ratio do not depend on the junction length. We also identify a broad range of selection pressures spanning two orders of magnitude.