Quantitative analyses of T cell motion in tissue reveals factors driving T cell search in tissues

  1. David J Torres  Is a corresponding author
  2. Paulus Mrass
  3. Janie Byrum
  4. Arrick Gonzales
  5. Dominick N Martiniez
  6. Evelyn Juarez
  7. Emily Thompson
  8. Vaiva Vezys
  9. Melanie E Moses
  10. Judy L Cannon  Is a corresponding author
  1. Northern New Mexico College, United States
  2. University of New Mexico, United States
  3. University of Minnesota, United States

Abstract

T cells are required to clear infection, moving first in lymph nodes to interact with antigen bearing dendritic cells leading to activation. T cells then move to sites of infection to find and clear infection. T cell motion plays a role in how quickly a T cell finds its target, from initial natiıve T cell activation by a dendritic cell to interaction with target cells in infected tissue. To better understand how different tissue environments might affect T cell motility, we compared multiple features of T cell motion including speed, persistence, turning angle, directionality, and confinement of motion from T cells moving in multiple tissues using tracks collected with microscopy from murine tissues. We quantitatively analyzed natiıve T cell motility within the lymph node and compared motility parameters with activated CD8 T cells moving within the villi of small intestine and lung under different activation conditions. Our motility analysis found that while the speeds and the overall displacement of T cells vary within all tissues analyzed, T cells in all tissues tended to persist at the same speed, particularly if the previous speed is very slow (less than 2 μm/min) or very fast (greater than 8 μm/min) with the exception of T cells in the villi for speeds greater than 10 μm/min. Interestingly, we found that turning angles of T cells in the lung show a marked population of T cells turning at close to 180o, while T cells in lymph nodes and villi do not exhibit this 'reversing' movement. Additionally, T cells in the lung showed significantly decreased meandering ratios and increased confinement compared to T cells in lymph nodes and villi. The combination of these differences in motility patterns led to a decrease in the total volume scanned by T cells in lung compared to T cells in lymph node and villi. These results suggest that the tissue environment in which T cells move can impact the type of motility and ultimately, the efficiency of T cell search for target cells within specialized tissues such as the lung.

Data availability

Datasets are available as Supplementary Materials under the Biorxiv preprint BIORXIV/2022/516891 https://www.biorxiv.org/content/10.1101/2022.11.17.516891v2.supplementary-material. The code used for analysis can be downloaded at: https://github.com/davytorres/T-cell-analysis-tool .

Article and author information

Author details

  1. David J Torres

    Northern New Mexico College, Espanola, United States
    For correspondence
    davytorres@nnmc.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2469-5284
  2. Paulus Mrass

    Department of Molecular Genetics and Microbiology, University of New Mexico, Albuquerque, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Janie Byrum

    Department of Molecular Genetics and Microbiology, University of New Mexico, Albuquerque, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Arrick Gonzales

    Northern New Mexico College, Espanola, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Dominick N Martiniez

    Northern New Mexico College, Espanola, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Evelyn Juarez

    Northern New Mexico College, Espanola, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Emily Thompson

    Department of Microbiology and Immunology, University of Minnesota, Minneapolis, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Vaiva Vezys

    Department of Microbiology and Immunology, University of Minnesota, Minneapolis, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Melanie E Moses

    Department of Computer Science, University of New Mexico, Albuquerque, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Judy L Cannon

    Department of Molecular Genetics and Microbiology, University of New Mexico, Albuquerque, United States
    For correspondence
    JuCannon@salud.unm.edu
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institutes of Health (P20GM103451)

  • David J Torres

University of New Mexico (NCI P30CA118100)

  • Paulus Mrass

National Institutes of Health (P20GM121176)

  • Paulus Mrass

University of New Mexico (School of Medicine)

  • Paulus Mrass

National Institutes of Health (1R01AI097202)

  • Judy L Cannon

National Institutes of Health (P50 GM085273)

  • Judy L Cannon

National Institutes of Health (5P20GM103452)

  • Judy L Cannon

National Institutes of Health (P20GM121176)

  • Judy L Cannon

National Institutes of Health (5 T32 AI007538-19)

  • Janie Byrum

University of New Mexico (School of Medicine)

  • Judy L Cannon

University of New Mexico (DARPA/AFRL FA8650-18-C-6898)

  • Judy L Cannon

University of New Mexico (NCI P30CA118100)

  • Judy L Cannon

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 work was done in accordance with approved protocols per IACUC institutional approvals, IACUC Animal approval #: 21-201165-HS

Copyright

© 2023, Torres 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.

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  1. David J Torres
  2. Paulus Mrass
  3. Janie Byrum
  4. Arrick Gonzales
  5. Dominick N Martiniez
  6. Evelyn Juarez
  7. Emily Thompson
  8. Vaiva Vezys
  9. Melanie E Moses
  10. Judy L Cannon
(2023)
Quantitative analyses of T cell motion in tissue reveals factors driving T cell search in tissues
eLife 12:e84916.
https://doi.org/10.7554/eLife.84916

Share this article

https://doi.org/10.7554/eLife.84916

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