Cell migration is a key feature of cellular function, and T cells are particularly specialized to migrate in different tissue types as infection can occur in any tissue and T cell movement in individual tissues is crucial to clear infection. Prior to infection, naive T cells move within the paracortex of the lymph node (LN), and upon interaction with cognate antigen bearing dendritic cells (Miller et al., 2004), T cells activate and effector CD8 T cells move to peripheral tissue sites of infection. In tissue sites, CD8 T cells enter infected tissues and move within tissues in order to find and kill target cells, including virally infected cells or tumor cells. Interestingly, T cells can move through many tissue environments that differ in cell type, structure, and chemical cues such as chemokines each of which may affect T cell motility patterns. While many studies have identified key molecules that regulate CD8 T cell effector function in different tissues, still relatively little quantitative analysis has been done to analyze the way CD8 T cells navigate multiple different types of tissue environments to find target cells.

CD8 T cell motility is a key feature of CD8 T cell function, particularly in searching through complex tissue environments to identify and interact with target cells. Motility of T cells is a function of a combination of T cell-intrinsic mechanisms, the extracellular environment, and chemical signals in the milieu (Krummel et al., 2016). Tissue environments include a complex and heterogeneous system of cell types, extracellular matrix components, and soluble factors which have been shown to impact T cell motion; for example, structural cells within the tissue environment provide signals to feed back to immune cells in the central nervous system (Bartholomäus et al., 2009; Tomlin and Piccinini, 2018). Additionally, multiple studies have shown that naive T cells in the LN paracortex use interactions with fibroblastic reticular cells to mediate movement, as well as receive soluble signals such as IL-7 for survival (Bajénoff et al., 2006; Katakai et al., 2004; Link et al., 2007). In addition to extracellular influences, many studies have defined intrinsic molecular regulators of T cell movement, particularly speed in multiple tissues including LNs (Banigan et al., 2015; Fricke et al., 2016; Katakai et al., 2004; Gérard et al., 2014; Eckert et al., 2019; Devi et al., 2021; Onder et al., 2012), skin (Fowell and Kim, 2021; Fernandes et al., 2020; Overstreet et al., 2013; Ariotti et al., 2015), Female Reproductive Tract (FRT) (Beura et al., 2018), liver (Guidotti et al., 2015; McNamara et al., 2017; Rajakaruna et al., 2022), lung (Mrass et al., 2017; Alon et al., 2021; Lambert Emo et al., 2016) just to name a few. High speeds have been linked to integrins (e.g. LFA-1 and VLA-4) (Katakai et al., 2013; Overstreet et al., 2013), chemokine receptors (Asperti-Boursin et al., 2007; Okada and Cyster, 2007; Ariotti et al., 2015), as well as signaling molecules such as regulators of the actin cytoskeleton (Mrass et al., 2017; Nombela-Arrieta et al., 2007; Petrie and Yamada, 2012; Cannon et al., 2013). These studies identified key molecular drivers and structures that mediate T cell movement within individual tissues, but there remains a gap in analysis to compare how T cell motility patterns might differ between tissues.

Quantitative analysis of cell motion provides a powerful tool to determine underlying mechanisms that drive how cells, including T cells, move. While structures, cell types, and chemical cues may differ in tissues that can impact T cell motility patterns, studies performed both in vitro and in vivo have found that all cell movement, including T cells, use actomyosin contractility and actin flow to couple directional persistence and speed, pointing to a universal mechanism for cells to move faster and more persistently in a direction (Jerison and Quake, 2020; Maiuri et al., 2015). This universal coupling of directional persistence and speed is most clearly shown in cells moving in vitro and on 2D surfaces. How T cells navigate complex tissue environments in three dimensions is still not well understood where cells use multiple modes of migration (Yamada and Sixt, 2019).

In this study, we quantitatively analyze T cell movement as one way to interrogate potential environment influences from different tissues. We previously used quantitative analyses of T cell movement in tissue to reveal specific types of motility patterns leading to more effective T cell responses (Fricke et al., 2016; Mrass et al., 2017; Thompson et al., 2019). In this paper, we compare multiple features of T cell motion in different tissues: speed, tendency to persist at a speed, dependence of speed on turning angle, mean squared displacement (MSD), directionality, confined ratio and time, and volume patrolled within the LN with naive T cells and activated CD8 T cells within the small intestine and lung. By comparing T cell movement in different tissues, we identify tissue-specific effects on T cell motility. Our results suggest that tissue environments may contribute to different modes of T cell movement, which can impact the efficiency of T cell searches for target cells in tissues.

Cell movement through tissue is an important feature in immunity, particularly for T cell-mediated immunity as T cells must make direct cell–cell contact with target cells. A distinguishing feature of T cells is their ability to move through many different tissue environments, which include varying cell types, structural features, and chemokines, all of which may contribute to differences in T cell motility (Donovan and Lythe, 2012). T cell activation also leads to significant changes in expression of cell surface proteins as well as cytoskeletal machinery that impact T cell motility. Naive T cells express CCR7 and CD62L (Asperti-Boursin et al., 2007; Förster et al., 2008; Worbs et al., 2007). Upon activation, effector T cells upregulate different chemokine receptors depending on the tissue the effector T cells home to as well as integrins and CD44 (Fadel et al., 2008; Kohlmeier et al., 2009; Masopust et al., 2010; Masopust et al., 2007; Mikhak et al., 2013; Olson et al., 2012; Ozga et al., 2022; Thompson et al., 2019; Wein et al., 2019; Wherry et al., 2007). Despite these differences, we can use motility parameters to compare the types of movement T cells take independently of the specific molecular interactions that drive these movement patterns.

We analyzed motility parameters to determine what key factors drive the ability of T cells to move through tissues to effectively mount immune responses. Our analysis more fully captures key features of movement that enable T cells to effectively move in tissues, promoting T cell responses. Our study included naive CD8 and CD4 T cells in the LN, antigen-specific effector CD8 T cells responding to infection in the villi and lung (influenza-infected lung), and non-antigen specifically activated effector CD8 T cells in the LPS-inflamed lung in a model of acute lung injury (Mrass et al., 2017).

We identified similarities and differences that contribute to the ability of T cells to search tissue for target cells. Interestingly, T cells tend to exhibit similar persistence trends in a range of speeds in all tissue types regardless of activation status. In regards to cell-based speed, non-antigen-specific naive T cells in LNs and antigen-specific effector CD8 T cells in villi move fastest while effector CD8 T cells that are non-antigen specific move slowest in the LPS-inflamed lung. These data suggest that the speed at which T cells move can be independent of antigen-specific interactions as well as activation status (Bousso et al., 2002). These results are supported by previous work showing that effector CD8 T cells in the skin appear to move slowly (Ariotti et al., 2015) while effector CD8 T cells in the female reproductive tract move at speeds similar to naive T cells in LNs (Beura et al., 2018).

Previous work quantitating cell motility in both non-T cells and T cells observed a ‘universal coupling’ between speed and directional persistence, showing that fast moving cells show directional persistence while slow moving cells do not (Jerison and Quake, 2020; Maiuri et al., 2015). In our analysis, we find that T cells moving in LN, small intestine villi, and influenza-infected lung all show this coupling. However, we have also identified an exception to this rule for T cells moving in the LPS-inflamed lung, which showed no change in directional persistence as measured by turning angle between fast moving and slow moving cells (Figure 3B). Thus, while our data confirm that while speed can be coupled to directional persistence for T cells moving in all tissues analyzed, this coupling is not necessarily ‘universal’. Recently, it has been shown that the correlation between speed and turning angle can arise from differences in sampling rates (Ganusov et al., 2023). Our data are unlikely to be affected by sampling rate as we equalized and normalized the sampling rate for all the T cells (see Materials and methods).

We find that T cells moving in the lung show specific motility features that differ from T cells moving in LNs or villi. T cells in the lung tend to displace less, turn at higher angles, particularly at angles >140°, meander more, and linger at locations longer. Confinement can occur in the lung independent of antigen, as CD8 T cells activated in vitro in the LPS-inflamed lung can still show confinement without specific antigen activation. In particular, T cells in the lung exhibit back and forth motion, with a peak in turning angles occurring near 160°. This peak in turning angle is consistent with the ‘back and forth’ motion we previously observed for T cells in the LPS-inflamed lung, as well as a stop-and-go motion (Mrass et al., 2017). Stop-and-go behavior has also been observed albeit for shorter time periods for T cells in LNs by Miller et al., 2003; Wei et al., 2003, and Beltman et al., 2007. We also find that the slope of the MSD versus time for T cells moving in LNs and villi show superdiffusive motion as previously observed for effector T cells in the brain (Harris et al., 2012). In contrast, the slope of T cells moving in lung is slightly <1, suggesting Brownian type motion. However, because the angle distribution is not uniform, the cell motion cannot be considered strictly Brownian. Together these motility parameters suggest that the lung environment may lead to specific types of motion taken by T cells, potentially due to the particular physical environment of the lung.

Our results also show that there can be significant variability of T cell movement patterns within an individual tissue (Table 2). However, despite large differences amongst individual T cell movements within any specific tissue, the overall patterns of T cell motility remain similar within tissue and across tissues.

The volume patrolled by a T cell is dependent not only on its speed but also on turning angles, the cell’s tendency to meander, and the amount of time the cell spends confined to a location. The higher percentage of T cells turning at smaller angles in the LNs and villi suggests that these organs allow a broader range of turning motion, potentially enabling broader search areas as reflected in the larger volumes patrolled. We found that naive T cells and CD8 effector cells in the villi at d8 post infection patrol a larger volume (9.4 $u{m}^3$/s) due to a combination of fast speed, less confinement, more superdiffusive motion, and smaller turning angles. Analysis of effector T cells at days 5 and 8 post infection in the gut suggest that antigen abundance likely increases confinement and thus decrease T cell speed, leading to a smaller volume patrolled at d5 post infection despite comparable values in meandering ratio and MSD. The patrol volume is significantly smaller for effector T cells in the lung due to greater confinement, more Brownian-like motion, and a greater proportion of large turning angles, particularly angles that suggest a ‘back and forth’ motion. However, T cells in the influenza-infected lung and LPS-inflamed lung show differences in the frequency distribution of volume per time, with T cells in the influenza-infected lung showing more T cells patrolling at higher volumes than T cells in LPS-inflamed lung. These results suggest that a combination of back and forth motion and slightly faster cell-based speeds can affect the search efficiency for T cells in the influenza-infected lung. Previous results using computational modeling suggests that intermittent and back and forth motion can improve search times (Bénichou et al., 2011). Effector T cells moving in LPS-infected lung show the lowest volume covered (5.1 $u{m}^3$/s) due to low speeds, higher turning angles, low directionality and high confinement times. The lack of coupling between speed and turning angle may also lead to low search efficiency.

Three-dimensional migration of T cells is a complex interplay of internal cell signaling, the surrounding extracellular tissue environment, molecular signaling and chemokines. We have quantitatively analyzed how T cells move in different tissues using multiple metrics. These metrics provide a way of quantitatively capturing underlying complex features of three-dimensional T cell movement.

Table 1 summarizes the number of T cells tracked within the LN, small intestine villi, and lung which were analyzed. Cell tracks were obtained from at least two separate fields from at least two independent experiments. The data come in the form of x-, y-, and z-coordinates for each T cell at different time frames.

The LN T cell tracks were obtained from data in Fricke et al., 2016 and Tasnim et al., 2018. Video 1 in the Supplementary Data shows a representative movie from one field from data analyzed in Fricke et al., 2016. The T cell tracking in the small intestine was previously described in Thompson et al., 2019. Video 2 reproduces day 8 (d8) T cell movement in villi from Video S1 shown in Thompson et al., 2019. T cells from LPS-inflamed lung were previously described in Mrass et al., 2017. Video 3 shows CD8 T cells moving in LPS-inflamed lung from Mrass et al., 2017.

For T cells from influenza-infected lung, mice were infected intranasally with 1 × 10³ PFU HKx31 (Charles River). To ensure sedimentation of the virus into the lower respiratory tract, infection was performed while mice were under anesthesia with 90 mg/kg ketamine and 8.1 mg/kg xylazine. In some experiments mice received polyclonal naive GFP+CD8+ T cells from Ubiquitin-GFP animals before infection with influenza. GFP+CD8+ naive T cells were derived from single cell suspensions isolated from spleen and LNs of Ubiquitin-GFP animals, and CD8+ T cells isolated using the CD8a+ T Cell Isolation Kit (Miltenyi Biotec), then transferred via the tail vein into recipient mice. Recipient mice received approximately 10⁴ GFP+CD8+ T cells. All work was done in accordance with approved protocols per IACUC institutional approvals.

For imaging of GFP+CD8+ T cells in influenza-infected lungs, mice were euthanized and lungs from influenza-infected mice were removed at days 7 or 8 post infection. After opening the chest cavity, the lungs were inflated with 2% low melting agarose (Sigma-Aldrich, #A0701) at a temperature of 37°C. We injected one ml of the solution via a catheter through an incision of the trachea. After inflation, the opening in the trachea was sealed with a knot and agarose solidification was induced by exposing the lungs to a phosphate-buffered saline solution with a temperature of 4°C. After harvest, the lungs were transferred into an incubator and transferred within a biosafety cabinet into a POC-R imaging chamber (LaCon). Imaging was performed with a Zeiss LSM800 Airyscan Confocal Microscope. Due to the transparency of the prepared lung tissue, it was possible to visualize at tissue depths of more than 60 µm. Video 4 shows GFP+CD8+ T cells moving at d8 post infection in influenza-infected lung. In some experiments, imaging was performed with similar setups using a Prairie Ultima Two-photon microscope or a Zeiss LSM 510 microscope. We captured equivalent T cell behavior with the different microscope setups.

Below we summarize the time step sampling and the various metrics used to analyze the T cell motion. These metrics include speed (cell-based and displacement speed), tendency to persist at the same speed, MSD, directionality through the meandering ratio, confined ratio and time, and volume patrolled per time.

Speed

If $(x_i,y_i,z_i)$ refers to the position of a cell at time t_i and $(x_{i+1},y_{i+1},z_{i+1})$ refers to the position of the cell at time $t_{i+1}$, let $d_{i,i+1}$ represent the distance between the two positions

(1) $d_{i,i+1}=\sqrt{(x_{i+1}-x_i{)}^2+(y_{i+1}-y_i{)}^2+(z_{i+1}-z_i{)}^2}.$

Two different types of speeds are computed using the positions and times from a T cell: a cell-based speed and displacement-based speed.

Cell-based speed

Request a detailed protocol

If a cell is tracked for $n$ positions and times, the cell-based speed $s_{cell}$ of a cell is computed by summing all the distances traveled by the cell and dividing by the total elapsed time $t_n-t_1$,

(2) $s_{cell}=\frac{\sum _{i=1}^{n-1}{d}_{i,i+1}}{t_n-t_1}.$

Displacement speed

Request a detailed protocol

The displacement speed $s_{displacement}$ is computed using the first and last locations of the cell,

(3) $s_{displacement}=\frac{d_{1,n}}{t_n-t_1}.$

Turning angle and directionality

Request a detailed protocol

The turning angle $\theta$ for a T cell given the three positions of the cell {1,2,3} enclosed within circles is shown in Figure 7A. If ${\displaystyle {\mathbf{v}}_1}$ is the vector formed from positions $(x_1,y_1,z_1)$ and $(x_2,y_2,z_2)$, ${\displaystyle {\mathbf{v}}_1=(x_2-x_1,y_2-y_1,z_2-z_1)}$ and ${\displaystyle {\mathbf{v}}_2}$ is the vector formed from positions $(x_2,y_2,z_2)$ and $(x_3,y_3,z_3)$, ${\displaystyle {\mathbf{v}}_2=(x_3-x_2,y_3-y_2,z_3-z_2)}$, the turning angle $\theta$ is computed using

(4) $\theta =arccos\left(\frac{{\mathbf{v}}_2\cdot {\mathbf{v}}_1}{||{\mathbf{v}}_1||||{\mathbf{v}}_2||}\right).$

Figure 7

Download asset Open asset

We did not include speeds <1 µm/min as these cells are likely to be considered stopped and turning angles with very small speeds can lead to artifactual angle measurements; for example, small speeds will emphasize turning angles in increments of 45° due to the pixel resolution of the microscope.

Suppose a T cell visits the four locations enclosed within the circles (Figure 7B). One can compute the total distance traveled by summing up the distance from location 1 to location 2, $d_{1,2}$, the distance from location 2 to location 3, $d_{2,3}$, and the distance from location 3 to location 4, $d_{3,4}$. The straight line distance can also be computed from the original location 1 to the final location 4, $d_{1,4}$. One measure of a cell’s tendency to maintain its direction is the meandering ratio (Lambert Emo et al., 2016)

$M=\frac{d_{1,4}}{d_{1,2}+d_{2,3}+d_{3,4}}.$

If the ratio $M$ is close to 1, the cell deviates very little from one direction, whereas if $M$ is much <1, the cell moves along a meandering path. In general for $n$ locations, directionality can be measured using the ratio

(5) $M=\frac{d_{1,n}}{\sum _{i=1}^{n-1}{d}_{i,i+1}}.$

Datasets are attached as Supplementary Materials. The code used for analysis can be downloaded at: https://github.com/davytorres/T-cell-analysis-tool (copy archived at Torres, 2023).

1. 1. Alon R
   2. Sportiello M
   3. Kozlovski S
   4. Kumar A
   5. Reilly EC
   6. Zarbock A
   7. Garbi N
   8. Topham DJ

   (2021) Leukocyte trafficking to the lungs and beyond: lessons from influenza for COVID-19

   Nature Reviews. Immunology 21:49–64.

   https://doi.org/10.1038/s41577-020-00470-2

   • PubMed
   • Google Scholar
2. 1. Ariotti S
   2. Beltman JB
   3. Borsje R
   4. Hoekstra ME
   5. Halford WP
   6. Haanen J
   7. de Boer RJ
   8. Schumacher TNM

   (2015) Subtle CXCR3-dependent chemotaxis of CTLs within infected tissue allows efficient target localization

   Journal of Immunology 195:5285–5295.

   https://doi.org/10.4049/jimmunol.1500853

   • PubMed
   • Google Scholar
3. 1. Asperti-Boursin F
   2. Real E
   3. Bismuth G
   4. Trautmann A
   5. Donnadieu E

   (2007) CCR7 ligands control basal T cell motility within lymph node slices in a phosphoinositide 3-kinase-independent manner

   The Journal of Experimental Medicine 204:1167–1179.

   https://doi.org/10.1084/jem.20062079

   • PubMed
   • Google Scholar
4. 1. Bajénoff M
   2. Egen JG
   3. Koo LY
   4. Laugier JP
   5. Brau F
   6. Glaichenhaus N
   7. Germain RN

   (2006) Stromal cell networks regulate lymphocyte entry, migration, and territoriality in lymph nodes

   Immunity 25:989–1001.

   https://doi.org/10.1016/j.immuni.2006.10.011

   • PubMed
   • Google Scholar
5. 1. Banigan EJ
   2. Harris TH
   3. Christian DA
   4. Hunter CA
   5. Liu AJ

   (2015) Heterogeneous CD8+ T cell migration in the lymph node in the absence of inflammation revealed by quantitative migration analysis

   PLOS Computational Biology 11:e1004058.

   https://doi.org/10.1371/journal.pcbi.1004058

   • PubMed
   • Google Scholar
6. 1. Bartholomäus I
   2. Kawakami N
   3. Odoardi F
   4. Schläger C
   5. Miljkovic D
   6. Ellwart JW
   7. Klinkert WEF
   8. Flügel-Koch C
   9. Issekutz TB
   10. Wekerle H
   11. Flügel A

   (2009) Effector T cell interactions with meningeal vascular structures in nascent autoimmune CNS lesions

   Nature 462:94–98.

   https://doi.org/10.1038/nature08478

   • PubMed
   • Google Scholar
7. 1. Beltman JB
   2. Marée AFM
   3. Lynch JN
   4. Miller MJ
   5. de Boer RJ

   (2007) Lymph node topology dictates T cell migration behavior

   The Journal of Experimental Medicine 204:771–780.

   https://doi.org/10.1084/jem.20061278

   • PubMed
   • Google Scholar
8. 1. Bénichou O
   2. Loverdo C
   3. Moreau M
   4. Voituriez R

   (2011) Intermittent search strategies

   Reviews of Modern Physics 83:81–129.

   https://doi.org/10.1103/RevModPhys.83.81

   • Google Scholar
9. 1. Beura LK
   2. Mitchell JS
   3. Thompson EA
   4. Schenkel JM
   5. Mohammed J
   6. Wijeyesinghe S
   7. Fonseca R
   8. Burbach BJ
   9. Hickman HD
   10. Vezys V
   11. Fife BT
   12. Masopust D

   (2018) Intravital mucosal imaging of CD8⁺ resident memory T cells shows tissue-autonomous recall responses that amplify secondary memory

   Nature Immunology 19:173–182.

   https://doi.org/10.1038/s41590-017-0029-3

   • PubMed
   • Google Scholar
10. 1. Bousso P
    2. Bhakta NR
    3. Lewis RS
    4. Robey E

    (2002) Dynamics of thymocyte-stromal cell interactions visualized by two-photon microscopy

    Science 296:1876–1880.

    https://doi.org/10.1126/science.1070945

    • PubMed
    • Google Scholar
11. 1. Cannon JL
    2. Asperti-Boursin F
    3. Letendre KA
    4. Brown IK
    5. Korzekwa KE
    6. Blaine KM
    7. Oruganti SR
    8. Sperling AI
    9. Moses ME
    10. Houtman JCD

    (2013) PKCθ Regulates T cell motility via ezrin-radixin-moesin localization to the uropod

    PLOS ONE 8:e78940.

    https://doi.org/10.1371/journal.pone.0078940

    • Google Scholar
12. 1. Devi S
    2. Alexandre YO
    3. Loi JK
    4. Gillis R
    5. Ghazanfari N
    6. Creed SJ
    7. Holz LE
    8. Shackleford D
    9. Mackay LK
    10. Heath WR
    11. Sloan EK
    12. Mueller SN

    (2021) Adrenergic regulation of the vasculature impairs leukocyte interstitial migration and suppresses immune responses

    Immunity 54:1219–1230.

    https://doi.org/10.1016/j.immuni.2021.03.025

    • PubMed
    • Google Scholar
13. 1. Donovan GM
    2. Lythe G

    (2012) T-cell movement on the reticular network

    Journal of Theoretical Biology 295:59–67.

    https://doi.org/10.1016/j.jtbi.2011.11.001

    • PubMed
    • Google Scholar
14. 1. Eckert N
    2. Permanyer M
    3. Yu K
    4. Werth K
    5. Förster R

    (2019) Chemokines and other mediators in the development and functional organization of lymph nodes

    Immunological Reviews 289:62–83.

    https://doi.org/10.1111/imr.12746

    • PubMed
    • Google Scholar
15. 1. Fadel SA
    2. Bromley SK
    3. Medoff BD
    4. Luster AD

    (2008) CXCR3-deficiency protects influenza-infected CCR5-deficient mice from mortality

    European Journal of Immunology 38:3376–3387.

    https://doi.org/10.1002/eji.200838628

    • PubMed
    • Google Scholar
16. 1. Fernandes NRJ
    2. Reilly NS
    3. Schrock DC
    4. Hocking DC
    5. Oakes PW
    6. Fowell DJ

    (2020) CD4⁺ T Cell interstitial migration controlled by fibronectin in the inflamed skin

    Frontiers in Immunology 11:1501.

    https://doi.org/10.3389/fimmu.2020.01501

    • PubMed
    • Google Scholar
17. 1. Förster R
    2. Davalos-Misslitz AC
    3. Rot A

    (2008) CCR7 and its ligands: balancing immunity and tolerance

    Nature Reviews. Immunology 8:362–371.

    https://doi.org/10.1038/nri2297

    • PubMed
    • Google Scholar
18. 1. Fowell DJ
    2. Kim M

    (2021) The spatio-temporal control of effector T cell migration

    Nature Reviews. Immunology 21:582–596.

    https://doi.org/10.1038/s41577-021-00507-0

    • PubMed
    • Google Scholar
19. 1. Fricke GM
    2. Letendre KA
    3. Moses ME
    4. Cannon JL

    (2016) Persistence and adaptation in immunity: t cells balance the extent and thoroughness of search

    PLOS Computational Biology 12:e1004818.

    https://doi.org/10.1371/journal.pcbi.1004818

    • PubMed
    • Google Scholar
20. 1. Ganusov VV
    2. Zenkov VS
    3. Majumder B

    (2023) Correlation between speed and turning naturally arises for sparsely sampled cell movements

    Physical Biology 20:025001.

    https://doi.org/10.1088/1478-3975/acb18c

    • Google Scholar
21. 1. Gérard A
    2. Patino-Lopez G
    3. Beemiller P
    4. Nambiar R
    5. Ben-Aissa K
    6. Liu Y
    7. Totah FJ
    8. Tyska MJ
    9. Shaw S
    10. Krummel MF

    (2014) Detection of rare antigen-presenting cells through T cell-intrinsic meandering motility, mediated by Myo1g

    Cell 158:492–505.

    https://doi.org/10.1016/j.cell.2014.05.044

    • PubMed
    • Google Scholar
22. 1. Guidotti LG
    2. Inverso D
    3. Sironi L
    4. Di Lucia P
    5. Fioravanti J
    6. Ganzer L
    7. Fiocchi A
    8. Vacca M
    9. Aiolfi R
    10. Sammicheli S
    11. Mainetti M
    12. Cataudella T
    13. Raimondi A
    14. Gonzalez-Aseguinolaza G
    15. Protzer U
    16. Ruggeri ZM
    17. Chisari FV
    18. Isogawa M
    19. Sitia G
    20. Iannacone M

    (2015) Immunosurveillance of the liver by intravascular effector CD8(+) T cells

    Cell 161:486–500.

    https://doi.org/10.1016/j.cell.2015.03.005

    • PubMed
    • Google Scholar
23. 1. Halle S
    2. Keyser KA
    3. Stahl FR
    4. Busche A
    5. Marquardt A
    6. Zheng X
    7. Galla M
    8. Heissmeyer V
    9. Heller K
    10. Boelter J
    11. Wagner K
    12. Bischoff Y
    13. Martens R
    14. Braun A
    15. Werth K
    16. Uvarovskii A
    17. Kempf H
    18. Meyer-Hermann M
    19. Arens R
    20. Kremer M
    21. Sutter G
    22. Messerle M
    23. Förster R

    (2016) In Vivo Killing Capacity of Cytotoxic T Cells Is Limited and Involves Dynamic Interactions and T Cell Cooperativity

    Immunity 44:233–245.

    https://doi.org/10.1016/j.immuni.2016.01.010

    • PubMed
    • Google Scholar
24. 1. Harris TH
    2. Banigan EJ
    3. Christian DA
    4. Konradt C
    5. Tait Wojno ED
    6. Norose K
    7. Wilson EH
    8. John B
    9. Weninger W
    10. Luster AD
    11. Liu AJ
    12. Hunter CA

    (2012) Generalized Lévy walks and the role of chemokines in migration of effector CD8+ T cells

    Nature 486:545–548.

    https://doi.org/10.1038/nature11098

    • PubMed
    • Google Scholar
25. 1. Jerison ER
    2. Quake SR

    (2020) Heterogeneous T cell motility behaviors emerge from a coupling between speed and turning in vivo

    eLife 9:e53933.

    https://doi.org/10.7554/eLife.53933

    • PubMed
    • Google Scholar
26. 1. Katakai T
    2. Hara T
    3. Sugai M
    4. Gonda H
    5. Shimizu A

    (2004) Lymph node fibroblastic reticular cells construct the stromal reticulum via contact with lymphocytes

    The Journal of Experimental Medicine 200:783–795.

    https://doi.org/10.1084/jem.20040254

    • PubMed
    • Google Scholar
27. 1. Katakai T
    2. Habiro K
    3. Kinashi T

    (2013) Dendritic cells regulate high-speed interstitial T cell migration in the lymph node via LFA-1/ICAM-1

    Journal of Immunology 191:1188–1199.

    https://doi.org/10.4049/jimmunol.1300739

    • PubMed
    • Google Scholar
28. 1. Kohlmeier JE
    2. Cookenham T
    3. Miller SC
    4. Roberts AD
    5. Christensen JP
    6. Thomsen AR
    7. Woodland DL

    (2009) CXCR3 directs antigen-specific effector CD4+ T cell migration to the lung during parainfluenza virus infection

    Journal of Immunology 183:4378–4384.

    https://doi.org/10.4049/jimmunol.0902022

    • PubMed
    • Google Scholar
29. 1. Krummel MF
    2. Bartumeus F
    3. Gérard A

    (2016) T cell migration, search strategies and mechanisms

    Nature Reviews. Immunology 16:193–201.

    https://doi.org/10.1038/nri.2015.16

    • PubMed
    • Google Scholar
30. 1. Lambert Emo K
    2. Hyun Y
    3. Reilly E
    4. Barilla C
    5. Gerber S
    6. Fowell D
    7. Kim M
    8. Topham DJ
    9. Rimmelzwaan GF

    (2016) Live imaging of influenza infection of the trachea reveals dynamic regulation of CD8+ T cell motility by antigen

    PLOS Pathogens 12:e1005881.

    https://doi.org/10.1371/journal.ppat.1005881

    • Google Scholar
31. 1. Letendre K
    2. Donnadieu E
    3. Moses ME
    4. Cannon JL

    (2015) Bringing statistics up to speed with data in analysis of lymphocyte motility

    PLOS ONE 10:e0126333.

    https://doi.org/10.1371/journal.pone.0126333

    • PubMed
    • Google Scholar
32. 1. Link A
    2. Vogt TK
    3. Favre S
    4. Britschgi MR
    5. Acha-Orbea H
    6. Hinz B
    7. Cyster JG
    8. Luther SA

    (2007) Fibroblastic reticular cells in lymph nodes regulate the homeostasis of naive T cells

    Nature Immunology 8:1255–1265.

    https://doi.org/10.1038/ni1513

    • PubMed
    • Google Scholar
33. 1. Maiuri P
    2. Rupprecht JF
    3. Wieser S
    4. Ruprecht V
    5. Bénichou O
    6. Carpi N
    7. Coppey M
    8. De Beco S
    9. Gov N
    10. Heisenberg CP
    11. Lage Crespo C
    12. Lautenschlaeger F
    13. Le Berre M
    14. Lennon-Dumenil AM
    15. Raab M
    16. Thiam HR
    17. Piel M
    18. Sixt M
    19. Voituriez R

    (2015) Actin flows mediate a universal coupling between cell speed and cell persistence

    Cell 161:374–386.

    https://doi.org/10.1016/j.cell.2015.01.056

    • PubMed
    • Google Scholar
34. 1. Masopust D
    2. Murali-Krishna K
    3. Ahmed R

    (2007) Quantitating the magnitude of the lymphocytic choriomeningitis virus-specific CD8 T-cell response: it is even bigger than we thought

    Journal of Virology 81:2002–2011.

    https://doi.org/10.1128/JVI.01459-06

    • PubMed
    • Google Scholar
35. 1. Masopust D
    2. Choo D
    3. Vezys V
    4. Wherry EJ
    5. Duraiswamy J
    6. Akondy R
    7. Wang J
    8. Casey KA
    9. Barber DL
    10. Kawamura KS
    11. Fraser KA
    12. Webby RJ
    13. Brinkmann V
    14. Butcher EC
    15. Newell KA
    16. Ahmed R

    (2010) Dynamic T cell migration program provides resident memory within intestinal epithelium

    The Journal of Experimental Medicine 207:553–564.

    https://doi.org/10.1084/jem.20090858

    • PubMed
    • Google Scholar
36. 1. McNamara HA
    2. Cai Y
    3. Wagle MV
    4. Sontani Y
    5. Roots CM
    6. Miosge LA
    7. O’Connor JH
    8. Sutton HJ
    9. Ganusov VV
    10. Heath WR
    11. Bertolino P
    12. Goodnow CG
    13. Parish IA
    14. Enders A
    15. Cockburn IA

    (2017) Up-regulation of LFA-1 allows liver-resident memory T cells to patrol and remain in the hepatic sinusoids

    Science Immunology 2:eaaj1996.

    https://doi.org/10.1126/sciimmunol.aaj1996

    • PubMed
    • Google Scholar
37. 1. Mikhak Z
    2. Strassner JP
    3. Luster AD

    (2013) Lung dendritic cells imprint T cell lung homing and promote lung immunity through the chemokine receptor CCR4

    The Journal of Experimental Medicine 210:1855–1869.

    https://doi.org/10.1084/jem.20130091

    • PubMed
    • Google Scholar
38. 1. Miller MJ
    2. Wei SH
    3. Cahalan MD
    4. Parker I

    (2003) Autonomous T cell trafficking examined in vivo with intravital two-photon microscopy

    PNAS 100:2604–2609.

    https://doi.org/10.1073/pnas.2628040100

    • PubMed
    • Google Scholar
39. 1. Miller MJ
    2. Hejazi AS
    3. Wei SH
    4. Cahalan MD
    5. Parker I

    (2004) T cell repertoire scanning is promoted by dynamic dendritic cell behavior and random T cell motility in the lymph node

    PNAS 101:998–1003.

    https://doi.org/10.1073/pnas.0306407101

    • PubMed
    • Google Scholar
40. 1. Mrass P
    2. Oruganti SR
    3. Fricke GM
    4. Tafoya J
    5. Byrum JR
    6. Yang L
    7. Hamilton SL
    8. Miller MJ
    9. Moses ME
    10. Cannon JL

    (2017) ROCK regulates the intermittent mode of interstitial T cell migration in inflamed lungs

    Nature Communications 8:1010.

    https://doi.org/10.1038/s41467-017-01032-2

    • PubMed
    • Google Scholar
41. 1. Nombela-Arrieta C
    2. Mempel TR
    3. Soriano SF
    4. Mazo I
    5. Wymann MP
    6. Hirsch E
    7. Martínez-A C
    8. Fukui Y
    9. von Andrian UH
    10. Stein JV

    (2007) A central role for DOCK2 during interstitial lymphocyte motility and sphingosine-1-phosphate-mediated egress

    The Journal of Experimental Medicine 204:497–510.

    https://doi.org/10.1084/jem.20061780

    • PubMed
    • Google Scholar
42. 1. Okada T
    2. Cyster JG

    (2007) CC chemokine receptor 7 contributes to Gi-dependent T cell motility in the lymph node

    Journal of Immunology 178:2973–2978.

    https://doi.org/10.4049/jimmunol.178.5.2973

    • PubMed
    • Google Scholar
43. 1. Olson MR
    2. McDermott DS
    3. Varga SM

    (2012) The initial draining lymph node primes the bulk of the CD8 T cell response and influences memory T cell trafficking after a systemic viral infection

    PLOS Pathogens 8:e1003054.

    https://doi.org/10.1371/journal.ppat.1003054

    • PubMed
    • Google Scholar
44. 1. Onder L
    2. Narang P
    3. Scandella E
    4. Chai Q
    5. Iolyeva M
    6. Hoorweg K
    7. Halin C
    8. Richie E
    9. Kaye P
    10. Westermann J
    11. Cupedo T
    12. Coles M
    13. Ludewig B

    (2012) IL-7-producing stromal cells are critical for lymph node remodeling

    Blood 120:4675–4683.

    https://doi.org/10.1182/blood-2012-03-416859

    • PubMed
    • Google Scholar
45. 1. Overstreet MG
    2. Gaylo A
    3. Angermann BR
    4. Hughson A
    5. Hyun YM
    6. Lambert K
    7. Acharya M
    8. Billroth-Maclurg AC
    9. Rosenberg AF
    10. Topham DJ
    11. Yagita H
    12. Kim M
    13. Lacy-Hulbert A
    14. Meier-Schellersheim M
    15. Fowell DJ

    (2013) Inflammation-induced interstitial migration of effector CD4

    Nature Immunology 14:949–958.

    https://doi.org/10.1038/ni.2682

    • PubMed
    • Google Scholar
46. 1. Ozga AJ
    2. Chow MT
    3. Lopes ME
    4. Servis RL
    5. Di Pilato M
    6. Dehio P
    7. Lian J
    8. Mempel TR
    9. Luster AD

    (2022) CXCL10 chemokine regulates heterogeneity of the CD8⁺ T cell response and viral set point during chronic infection

    Immunity 55:82–97.

    https://doi.org/10.1016/j.immuni.2021.11.002

    • PubMed
    • Google Scholar
47. 1. Petrie RJ
    2. Yamada KM

    (2012) At the leading edge of three-dimensional cell migration

    Journal of Cell Science 125:5917–5926.

    https://doi.org/10.1242/jcs.093732

    • PubMed
    • Google Scholar
48. 1. Rajakaruna H
    2. O’Connor JH
    3. Cockburn IA
    4. Ganusov VV

    (2022) Liver environment-imposed constraints diversify movement strategies of liver-localized CD8 T cells

    Journal of Immunology 208:1292–1304.

    https://doi.org/10.4049/jimmunol.2100842

    • PubMed
    • Google Scholar
49. 1. Tasnim H
    2. Fricke GM
    3. Byrum JR
    4. Sotiris JO
    5. Cannon JL
    6. Moses ME

    (2018) Quantitative Measurement of Naïve T Cell Association With Dendritic Cells, FRCS, and Blood Vessels in Lymph Nodes

    Frontiers in Immunology 9:1571.

    https://doi.org/10.3389/fimmu.2018.01571

    • PubMed
    • Google Scholar
50. 1. Thompson EA
    2. Mitchell JS
    3. Beura LK
    4. Torres DJ
    5. Mrass P
    6. Pierson MJ
    7. Cannon JL
    8. Masopust D
    9. Fife BT
    10. Vezys V

    (2019) Interstitial Migration of CD8αβ T cells in the small intestine is dynamic and is dictated by environmental cues

    Cell Reports 26:2859–2867.

    https://doi.org/10.1016/j.celrep.2019.02.034

    • PubMed
    • Google Scholar
51. 1. Tomlin H
    2. Piccinini AM

    (2018) A complex interplay between the extracellular matrix and the innate immune response to microbial pathogens

    Immunology 155:186–201.

    https://doi.org/10.1111/imm.12972

    • PubMed
    • Google Scholar
52. Software

    1. Torres DJ

    (2023) T-cell-analysis-tool, version swh:1:rev:f67486fc540bce2314b158c1e97bd539421caff6

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:f8d2b3a2c1cefd24f0992b42fac96aa0664f3c2e;origin=https://github.com/davytorres/T-cell-analysis-tool;visit=swh:1:snp:7101ea56da9bab67795a95906ccb9529e1d05ff4;anchor=swh:1:rev:f67486fc540bce2314b158c1e97bd539421caff6
53. 1. Wei SH
    2. Parker I
    3. Miller MJ
    4. Cahalan MD

    (2003) A stochastic view of lymphocyte motility and trafficking within the lymph node

    Immunological Reviews 195:136–159.

    https://doi.org/10.1034/j.1600-065x.2003.00076.x

    • PubMed
    • Google Scholar
54. 1. Wein AN
    2. McMaster SR
    3. Takamura S
    4. Dunbar PR
    5. Cartwright EK
    6. Hayward SL
    7. McManus DT
    8. Shimaoka T
    9. Ueha S
    10. Tsukui T
    11. Masumoto T
    12. Kurachi M
    13. Matsushima K
    14. Kohlmeier JE

    (2019) CXCR6 regulates localization of tissue-resident memory CD8 T cells to the airways

    The Journal of Experimental Medicine 216:2748–2762.

    https://doi.org/10.1084/jem.20181308

    • PubMed
    • Google Scholar
55. 1. Wherry EJ
    2. Ha SJ
    3. Kaech SM
    4. Haining WN
    5. Sarkar S
    6. Kalia V
    7. Subramaniam S
    8. Blattman JN
    9. Barber DL
    10. Ahmed R

    (2007) Molecular signature of CD8+ T cell exhaustion during chronic viral infection

    Immunity 27:670–684.

    https://doi.org/10.1016/j.immuni.2007.09.006

    • PubMed
    • Google Scholar
56. 1. Worbs T
    2. Mempel TR
    3. Bölter J
    4. von Andrian UH
    5. Förster R

    (2007) CCR7 ligands stimulate the intranodal motility of T lymphocytes in vivo

    The Journal of Experimental Medicine 204:489–495.

    https://doi.org/10.1084/jem.20061706

    • PubMed
    • Google Scholar
57. 1. Yamada KM
    2. Sixt M

    (2019) Mechanisms of 3D cell migration

    Nature Reviews. Molecular Cell Biology 20:738–752.

    https://doi.org/10.1038/s41580-019-0172-9

    • PubMed
    • Google Scholar

Author details

1. David J Torres

   Northern New Mexico College, Española, United States

   Contribution

   Conceptualization, Software, Formal analysis, Funding acquisition, Visualization, Writing - original draft, Writing - review and editing

   For correspondence

   davytorres@nnmc.edu

   Competing interests

   No competing interests declared

   "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 School of Medicine, Albuquerque, United States

   Contribution

   Resources, Data curation, Methodology

   Competing interests

   No competing interests declared

3. Janie Byrum

   Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine, Albuquerque, United States

   Contribution

   Data curation, Methodology

   Competing interests

   No competing interests declared

4. Arrick Gonzales

   Northern New Mexico College, Española, United States

   Contribution

   Software

   Competing interests

   No competing interests declared

5. Dominick N Martinez

   Northern New Mexico College, Española, United States

   Contribution

   Software

   Competing interests

   No competing interests declared

6. Evelyn Juarez

   Northern New Mexico College, Española, United States

   Contribution

   Software

   Competing interests

   No competing interests declared

7. Emily Thompson

   Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, United States

   Contribution

   Data curation

   Competing interests

   No competing interests declared

8. Vaiva Vezys

   Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, United States

   Contribution

   Data curation

   Competing interests

   No competing interests declared

9. Melanie E Moses

   Department of Computer Science, University of New Mexico, Albuquerque, United States

   Contribution

   Conceptualization, Methodology

   Competing interests

   No competing interests declared

10. Judy L Cannon

    1. Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine, Albuquerque, United States
    2. Autophagy, Inflammation, and Metabolism Center of Biomedical Research Excellence, University of New Mexico School of Medicine, Albuquerque, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    JuCannon@salud.unm.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0069-8106

• 970

  views

• 147

  downloads

• 1

  citations

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