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

Decision letter after peer review:

Thank you for submitting your article "Quantitative analyses of T cell motion in tissue reveals factors driving T cell search in tissues" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Michael L Dustin as Reviewing Editor and Reviewer #1 and the evaluation has been overseen by Satyajit Rath as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Emmanuel Donnadieu (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

The reviewers agree that the analysis performed is valuable and solid. The importance of the work can be increased by addressing specific suggestions of the reviewers that may require the introduction of additional data that hopefully was already collected.

1) All reviewers wondered about the microanatomical differences in the lung, villi, and lymph nodes. If second harmonic signals were collected then including some of this data would be a helpful start and such data was often collected alongside fluorescence data.

2) There were also questions about the statistical analysis that should be straightforward to address given the authors' background.

3) There was a concern about having the same T cells in each compartment. This may be difficult to achieve, but perhaps the phenotype of T cells in each compartment could be assessed for relevant parameters, or perhaps such data already exists in the literature- for example, levels of CD11a, CD103, CD44, CD62L, CCR7 and CXCR4, for example, could be investigated for T cells in each site to allow potentially informative comparisons.

Reviewer #1 (Recommendations for the authors):

The turning angle distribution in the lymph node has a bias toward small turning angles and thus displays features of a correlated random walk as previously described by this group and others. It is interesting that the turning angle distribution is flatter in the lung, particularly the LPS-treated lung, which is more similar to Brownian motion in this respect. The authors seem to reject this, but this aspect was striking and surprising.

Movement of the T cells in the lymph node and villi is likely scaffolded on stromal cells. Is this also true in the lung or is the connective tissue more similar to a dense collagen gel? Was second harmonic data collected in each study or any other analysis of the tissue context that might help understand the movement pattern in the lung? Even if this cannot be fully explored, it could provide a direction for future research to provide some examples, if available.

Some of the p-values from the Mann-Whitney type test suggest a very high likelihood that the behaviours are different across sites, but some of this may be due to the very large N from the analysis of individual T cells. It's not clear that these are really independent measurements as multiple T cells from one field will share microanatomical niches and may also share technical artefacts such as drift. Another way to look at these data are to bin the values by field or experimental days and retest if the mean values are still significantly different across sites. If the values are still significant when values from different animals/preparations are compared, then the p values will likely be more modest, but there would be greater confidence that the values are independent.

Reviewer #2 (Recommendations for the authors):

1) This study would have been even more important if the authors had provided information on the reasons why T-cell migration in the lung is so different than in other organs. For instance, the back-and-forth migration observed in the lung is striking. Can the authors try to correlate this feature with some external determinants of the lung tissue? It can be assumed that this type of motion is dependent on structural elements (vessels, matrix fibers) that constrain T cells forcing them to migrate back and forth. In a previous study (PMID: 29044117), the authors immunostained the lung vasculature and thereafter monitor T cells in relation to vessels. Can they use such methods together with second harmonic generation microscopy to identify external factors that can possibly control T cell migration in the lung? This would strengthen the impact of the study.

2) The persistence plot shown in figure 1C is interesting as such analysis has rarely been done. However, I am not entirely convinced by some claims of the authors. They conclude that T cells with the lowest and highest speed persist in moving at the same speed. It is likely that T cells at the lowest speeds are arrested and remain arrested during the recording. However, if this first point (T cells with the lowest speed) is removed from the graph the correlation is not evident. This should be commented on.

3) The data presented are mostly histograms comparing the motility parameters of T cells in different tissues. The study would benefit from including videos and illustrations from videos (e.g., tracks with x and y coordinates). These will help to convey some of the critical aspects reported in this study.

Reviewer #3 (Recommendations for the authors):

Specific corrections to the text and figures are required:

1) There is insufficient data in Table 1 to allow the reader to understand the differences between the different models. The simple naïve versus activated designation is misleading and fails to convey the distinct differences in the experimental design of each group. Additional columns should include: 1) mode of activation e.g., in vitro versus in vivo; 2) specificity e.g., polyclonal versus monoclonal; and 3) in situ imaging e.g., intravital versus tissue explant.

2) There is no reference to Figure 1 in the text. Data start at Figure 2.

3) More information is needed as to the micro-anatomical location of the imaging volumes in the two lung models – the lung is a large organ, so specifying where the imaging volumes were taken in the two models (e.g., depth, lobe location, distance from the epithelium of the airway or bronchi, etc.). It will be important to determine if motility differences in the lung could be attributed to a difference in the micro-anatomical position chosen for image acquisition.

Essential revisions:

The reviewers agree that the analysis performed is valuable and solid. The importance of the work can be increased by addressing specific suggestions of the reviewers that may require the introduction of additional data that hopefully was already collected.

We thank the reviewers for their insightful and useful comments to improve the manuscript. We have addressed all their comments below and hope that the reviewers and editor agree that the manuscript is now suitable for publication in eLife.

1) All reviewers wondered about the microanatomical differences in the lung, villi, and lymph nodes. If second harmonic signals were collected then including some of this data would be a helpful start and such data was often collected alongside fluorescence data.

While we agree that this would be the important and logical next step to this work, the data to specify microanatomical differences between lung, villi, and lymph nodes is not available and we were not able to complete additional image collection in a timely manner to include with the current revision. In addition, part of the reason we performed the analysis in this manuscript is to NOT consider the microanatomical differences within tissue but to compare across tissues. Significant molecular and cellular differences across tissues are well documented, including differences in extracellular matrix composition, chemokine expression, and cell types, just to name a few. It is likely that multiple cell types and molecules contribute to potential differences in T cell motility, and it was not possible to test individual molecules’ contribution to motility changes. We compared motility patterns rather than the effect of individual molecular effects on motility to be able to compare across tissues. We purposefully did NOT choose specific regions within any tissue other than areas infiltrated by T cells. This unbiased approach allowed us to compare how T cells move in different tissues without selecting for any specific regions or potentially biasing towards specific types of interactions.

We use quantitative motility parameters that are independent of molecular factors in order to compare across tissues. The question of how specific molecular and physical interactions within different tissues affect motility remains a question for additional work outside the scope of this manuscript. In fact, different microanatomical features and multiple molecules likely all contribute to the differences in motility observed in T cell motion. As our overarching question remained to identify whether distinct tissues result in any differences in T cell motion, we specifically analyzed all T cells without “selecting” specific areas based on microanatomical differences.

In response to concerns by both reviewers 1 and 2 regarding intra-tissue variability, we have now performed an additional analysis shown in Table 2. Our new analysis with ANOVA shows that within tissue motility can be highly statistically significantly different (see Column 3, Rows 3-6 of Table 2). The most significant variation within tissue is in the lymph nodes, but all tissues show significant differences within tissue. These new analyses suggest that microanatomical areas within tissues do indeed mediate different types of T cell motion.

Importantly, our new analyses show that even if we remove the statistically significantly different frames from the overall sample, the motility patterns of the remaining cells within any one tissue are similar to the motility patterns with all the cells included in the original analyses. We now show the new analyses with statistically significantly different frames removed as a comparison in Supplementary Data (Figure 1 – supplement 3, Figures 2-6 supplement 2 and associated tables). A detailed description of the comparison between the full dataset (Figures 1-6 and Tables 3-11) with the reduced dataset (Figure 1 – supplement 3, Figures 2-6 supplement 2, and associated tables) is now included in the “Response to multiple reviewers” on page 5 of this file. We have added a discussion of this point to the manuscript in the new Section 4.8 within the Materials and methods and on page 5 of Results and page 16 in the Discussion. Due to the desire to not eliminate intra-tissue variation, we have not removed cells and frames from our analyses in the main manuscript. We have kept all the frames as we have no scientific reason to exclude their analyses as we believe intra-tissue variability is important to consider and include in the analysis.

2) There were also questions about the statistical analysis that should be straightforward to address given the authors' background.

We performed an Analysis of Variance (ANOVA) study which computes a ratio of variance using a ratio of differences in means between tissues and within tissues. The ANOVA test uses a F-distribution which computes a ratio of between-groups variance and within-groups variance. The larger the differences from the mean between tissues and the smaller the differences in the means within tissues, the larger the F-value and the smaller the p-value. The p-values are shown in Table 2. We have added an additional section to the Materials and methods, Section 4.8 Statistical Methods as well as a more extensive explanation in the caption of Table 2.

3) There was a concern about having the same T cells in each compartment. This may be difficult to achieve, but perhaps the phenotype of T cells in each compartment could be assessed for relevant parameters, or perhaps such data already exists in the literature- for example, levels of CD11a, CD103, CD44, CD62L, CCR7 and CXCR4, for example, could be investigated for T cells in each site to allow potentially informative comparisons.

The reviewers are correct to point out that we expect differences between T cells in each compartment due to migration properties. Expression of all the markers specified are different in each tissue. The CD8 T cells used in each model have previously been described in detail and form well-established models for T cell activation and T cell function. For example, chemokine receptors and some integrins are differentially expressed in order to mediate migration into different tissues; for example, CCR7 is critical for homing to lymph nodes ([2],[3],[4]) while CXCR3 and other chemokine receptors are important for lung homing ([5], [6], [7], [8], [9]). We have confirmed these findings and find that naive T cells in LNs have high CCR7, high CD62L, and low CD44 as previously shown (Author response image 1).

Author response image 1

Download asset Open asset

P14 CD8 T cells activated in the LCMV model have been extensively characterized and published, with multiple publications showing expression of canonical activation markers such as CD44 as well as gut specific homing markers including CCR9 and a4b7 integrins ([10], [11], [12]). The data used here has also been previously published demonstrating expression of CD69 and CD103 [1]. Because characterization of these T cells has been published, we do not reproduce the activation markers here.

CD8 T cells in LPS-inflamed lung were activated in vitro by anti-CD3/anti-CD28, then adoptively transferred into animals treated with LPS intranasally. T cells isolated from the lung were analyzed by flow cytometry after adoptive transfer and uniformly CD44hiCD62Llo (Author response image 2). CD8 T cells responding to influenza infection at d8 post infection in the lung express CD44hi as well as CXCR4 with very little CXCR3 at d8 (Author response image 3).

Author response image 2

Download asset Open asset

Author response image 3

Download asset Open asset

The list of markers from reviewers and editors are only some of the many likely differences between cell types and the tissues studied in the manuscript. The differences in these and many other markers are well established and not novel, so we do not include these panels in the manuscript and include them here for reviewers. In this manuscript, we establish differences in motility patterns. Testing the specific role of individual molecules on T cell motility is well beyond the scope of this manuscript. In fact, it is likely that a combination of multiple interactions as well as structural features of tissues contribute to the motility differences we observe, and we now discuss this in more detail in the revised manuscript in both the introduction, results, and discussion.

Response to multiple reviewers: Both reviewers 1 and 2 brought up the possibility that within tissue differences may account for significant variability and asked for comparison of within tissue versus between tissue variability.

We performed an ANOVA analysis of each cell-based speeds within tissue (see column 3 of Table 2). Imaging of each individual tissue includes capturing data from multiple animals on different days with different frames. ANOVA analysis shows that within the same tissue, there are highly statistically significant differences.

Our analysis shows that “within” tissue variability Table 2 (Rows 3-6, Column 3) could exceed “between” tissue variability, for example, “within” LN variability p-value was p=1.7 x 10^-204 which exceeded “between” tissue variability with p=3.5 x 10^-11 (Row 2, Column 3).

To decrease the “within” tissue variability, we identified the most variable fields within each tissue and removed those fields. The “within” tissue variability and “between” tissue variability was then recalculated (Table 2, Column 5). Removing the outlier frames significantly decreased the “within” tissue variability to p=4.8 x 10^-5 in LNs, demonstrating that these removed frames were outliers compared with the data from the same tissue.

The result of eliminating the cells from frames that were statistical outliers resulted in a reduced set of files from the same tissue. 19 frames (1659 cells) of the original 40 frames (4400 cells) were retained for the LN; 8 frames (296 cells) were retained of the original 10 frames (425 cells) for the villi; and 4 frames (297 cells) were retained of the original 5 frames (355 cells) for the Lung (flu). No frames were removed from the Lung (LPS) because no frame was shown to be statistically significantly different from the others. Removal of outlier frames resulted in a large increase in the p-value within tissue and decrease in p-value between tissue (for example, compare Table 2 Column 3 and Column 5; within LN p=4.8 x 10^-5 while between tissue p=2.1 x 10^-20). The cell numbers reported in Table 1 in the original submission were also adjusted to account for cells that were excluded from analysis because they were not tracked long enough (e.g. shorter than 150 seconds).

We then re-analyzed all the intra-tissue comparisons for the major motility parameters, including key parameters of cell-based speed, displacement speed, volume per time, and meandering ratio p-values. Figures in the revised manuscript Supplementary Data, Figure 1 – supplement 3, Figures 2-6 – supplement 2 with their accompanying tables show statistical differences between tissues after removal of outlier frames. All motility analyses were performed on the reduced data sets in the Supplementary Data.

Interestingly, very few between tissue motility parameters changed with the outlier frames removed (compare main manuscript Figures 1-6 with complete dataset; supplemental Figures 1 – supplement 3, Figures 2-6 – supplement 2 with the reduced dataset). The largest changes were in cell-based speed and displacement speed shown in Figure 1 and Figure 1 – supplement 3. For example, the cell-based median speed of T cells in LN increased from 6.2 um/min (all frames, Figure 1A) to 7.2 um/min (reduced frames, Figure 1 – supplement 3A). However, the conclusion that T cells in the LN and villi moved with similar speeds while T cells in the lung moved more slowly remains the same. The same trends remained the same for all of the motility parameters analyzed between the complete and reduced datasets.

The p-values in both sets are also similar when comparing cell-based speed across tissues. We note a few differences. With the full dataset, there is no statistical difference between cell-based speed between T cells in LN, villi and influenza-infected lung while with the reduced dataset, the influenza infected lung is slightly slower than the villi (Table 3 vs Figure 1D – supplement 3). The same was seen for turning angles between LN and villi (Table 6 vs Figure 3C – supplement 2) and confined time (Table 9 vs Figure 5D – supplement 2). MSD showed no difference between LN and villi in the full dataset while the villi showed significantly lower MSD in the reduced dataset (Table 5 vs Figure 2C – supplement 2). The full dataset also showed no statistical difference between the MSD of T cells from influenza-infected lung versus the LPS inflamed lung while the reduced dataset did show statistically significant difference (Table 5 vs Figure 2C – supplement 2).

Since there is no scientific reason to exclude the outlier frames (as stated in the response to main critiques above) and because the main conclusions do not change for between tissue comparisons, we have included all the data in the main text, while including the analysis of the reduced dataset in the Supplementary Data section.

Reviewer #1 (Recommendations for the authors):

The turning angle distribution in the lymph node has a bias toward small turning angles and thus displays features of a correlated random walk as previously described by this group and others. It is interesting that the turning angle distribution is flatter in the lung, particularly the LPS-treated lung, which is more similar to Brownian motion in this respect. The authors seem to reject this, but this aspect was striking and surprising.

While the turning angle distribution in the LPS-treated lung is flatter, it still can be considered bimodal. If the motion was Brownian, the turning angle distribution should be uniform. We have now added a sentence to discuss this point on page 9 (Section 2.4) and page 16 of the Discussion.

Movement of the T cells in the lymph node and villi is likely scaffolded on stromal cells. Is this also true in the lung or is the connective tissue more similar to a dense collagen gel? Was second harmonic data collected in each study or any other analysis of the tissue context that might help understand the movement pattern in the lung? Even if this cannot be fully explored, it could provide a direction for future research to provide some examples, if available.

We previously published that T cells move along vasculature in the lung in LPS-inflamed lungs [13]. We did not collect second harmonic data in the lung. We agree with the reviewer that a more detailed analysis in each individual tissue of the types of cells and structures that are likely to “guide” T cell movement will be important for future studies.

Some of the p-values from the Mann-Whitney type test suggest a very high likelihood that the behaviours are different across sites, but some of this may be due to the very large N from the analysis of individual T cells. it's not clear that these are really independent measurements as multiple T cells from one field will share microanatomical niches and may also share technical artefacts such as drift. Another way to look at these data are to bin the values by field or experimental days and retest if the mean values are still significantly different across sites. If the values are still significant when values from different animals/preparations are compared then the p values will likely be more modest, but there would be greater confidence that the values are independent.

This was discussed above at the end of the response to essential revisions requested by the editor as well as response to multiple reviewers. Please see above for a detailed discussion of how we tested for within tissue differences and the rationale for keeping all data within the dataset.

Reviewer #2 (Recommendations for the authors):

1) This study would have been even more important if the authors had provided information on the reasons why T-cell migration in the lung is so different than in other organs. For instance, the back-and-forth migration observed in the lung is striking. Can the authors try to correlate this feature with some external determinants of the lung tissue? It can be assumed that this type of motion is dependent on structural elements (vessels, matrix fibers) that constrain T cells forcing them to migrate back and forth. In a previous study (PMID: 29044117), the authors immunostained the lung vasculature and thereafter monitor T cells in relation to vessels. Can they use such methods together with second harmonic generation microscopy to identify external factors that can possibly control T cell migration in the lung? This would strengthen the impact of the study.

In response to point #1 of reviewer #1, we have now added more discussion of potential differences in molecules, cells, and structures including mechanical tension and next steps in the introduction and discussion.

2) The persistence plot shown in figure 1C is interesting as such analysis has rarely been done. However, I am not entirely convinced by some claims of the authors. They conclude that T cells with the lowest and highest speed persist in moving at the same speed. It is likely that T cells at the lowest speeds are arrested and remain arrested during the recording. However, if this first point (T cells with the lowest speed) is removed from the graph the correlation is not evident. This should be commented on.

To address this concern, we increased the number of speed bins on the horizontal axis for speeds up to 3 um/min and assessed persistence (see Author response image 4). We find that there is still a slight increased probability for T cells to move persistently at lower speeds even for T cells moving at < 0.75 um/min. We now include a more detailed discussion of persistence analysis in Section 2.2 on pages 7 and 8.

Author response image 4

Download asset Open asset

3) The data presented are mostly histograms comparing the motility parameters of T cells in different tissues. The study would benefit from including videos and illustrations from videos (e.g., tracks with x and y coordinates). These will help to convey some of the critical aspects reported in this study.

We have now added videos for movement in each tissue. Some have been previously published (videos from naive T cells in LN published in Fricke et al. 2016 Video 1 [14]; d8 villi from Thompson et al. 2019 [1], Video 2; d8 from LPS-inflamed lung from Mrass et al. 2017 [13], Video 3). Videos from these publications have been reproduced with appropriate attribution. Video 4 from T cells on d8 post infection in influenza-infected lung is also included.

Reviewer #3 (Recommendations for the authors):

Specific corrections to the text and figures are required:

1) There is insufficient data in Table 1 to allow the reader to understand the differences between the different models. The simple naïve versus activated designation is misleading and fails to convey the distinct differences in the experimental design of each group. Additional columns should include: 1) mode of activation e.g., in vitro versus in vivo; 2) specificity e.g., polyclonal versus monoclonal; and 3) in situ imaging e.g., intravital versus tissue explant.

We have now added greater detail to both methods (Revised Table 1) as well as basic characterization of cells (described above in the response). We have also added a comparison of d5 and d8 activated CD8 T cells in the villi (see Supplementary Data).

2) There is no reference to Figure 1 in the text. Data start at Figure 2.

We have now added a reference to Figure 1 in the methods.

3) More information is needed as to the micro-anatomical location of the imaging volumes in the two lung models – the lung is a large organ, so specifying where the imaging volumes were taken in the two models (e.g., depth, lobe location, distance from the epithelium of the airway or bronchi, etc.). It will be important to determine if motility differences in the lung could be attributed to a difference in the micro-anatomical position chosen for image acquisition.

We discuss this at great length above in the response to the editor. We captured images at multiple depths with the limitation being the depth of the two-photon laser penetration into tissue. However, we performed no “selection” based on “location” within tissue other than where CD8 T cells localize. Microanatomical differences clearly play a role in tissue motility but the overall motility characteristics remain the same despite removing fields from each dataset.

References

[1] Thompson EA, Mitchell JS, Beura LK, Torres DJ, Mrass P, Pierson MJ, Cannon JL, Masopust D, Fife BT, Vezys V (2019) Interstitial Migration of CD8abT 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.

[2] Förster R, Davalos-Misslitz AC, Rot A (2008) CCR7 and its ligands: balancing immunity and tolerance. Nat Rev Immunol. 8(5):362-71. doi: 10.1038/nri2297.

[3] Asperti-Boursin F, Real E, Bismuth G, Trautmann A, Donnadieu E (2007) CCR7 ligands control basal T cell motility within lymph node slices in a phosphoinositide 3-kinase-independent manner. Journal of Experimental Medicine. 204(5):1167-1179. https://doi: 10.1084/jem.20062079.

[4] Worbs T, Mempel TR, Bölter J, von Andrian UH, Förster R (2007) CCR7 ligands stimulate the intranodal motility of T lymphocytes in vivo. J Exp Med. 204(3):489-95. doi: 10.1084/jem.20061706.

[5] Ozga AJ, Chow MT, Lopes ME, Servis RL, Di Pilato M, Dehio P, Lian J, Mempel TR, Luster AD (2022) CXCL10 chemokine regulates heterogeneity of the CD8⁺ T cell response and viral set point during chronic infection. Immunity. 55(1):82-97. https://doi: 10.1016/j.immuni.2021.11.002.

[6] Kohlmeier JE, Cookenham T, Miller SC, Roberts AD, Christensen JP, Thomsen AR, Woodland DL (2009) CXCR3 directs antigen-specific effector CD4⁺ T cell migration to the lung during parainfluenza virus infection. J Immunol. 183(7):4378-84. https://doi: 10.4049/jimmunol.0902022.

[7] Fadel SA, Bromley SK, Medoff BD, Luster AD (2008) CXCR3-deficiency protects influenza-infected CCR5-deficient mice from mortality. Eur J Immunol. 38(12):3376-87. https://doi: 10.1002/eji.200838628.

[8] Wein AN, McMaster SR, Takamura S, Dunbar PR, Cartwright EK, Hayward SL, McManus DT, Shimaoka T, Ueha S, Tsukui T, Masumoto T, Kurachi M, Matsushima K, Kohlmeier JE (2019) CXCR6 regulates localization of tissue-resident memory CD8 T cells to the airways. J Exp Med. 216(12):2748-2762. https://doi: 10.1084/jem.20181308.

[9] Mikhak Z, Strassner JP, Luster AD (2013) Lung dendritic cells imprint T cell lung homing and promote lung immunity through the chemokine receptor CCR4. J Exp Med. 210(9):1855-69. https://doi: 10.1084/jem.20130091.

[10] Masopust D, Choo D, Vezys V, Wherry EJ, Duraiswamy J, Akondy R, Wang J, Casey KA, Barber DL, Kawamura KS, Fraser KA, Webby RJ, Brinkmann V, Butcher EC, Newell KA, Ahmed R (2010) Dynamic T cell migration program provides resident memory within intestinal epithelium. J Exp Med. 207(3):553-64. https://doi: 10.1084/jem.20090858.

[11] Olson MR, McDermott DS, 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 Pathog. 8(12):e1003054. https://doi: 10.1371/journal.ppat.1003054.

[12] Masopust D, Murali-Krishna K, Ahmed R (2007) Quantitating the magnitude of the lymphocytic choriomeningitis virus-specific CD8 T-cell response: it is even bigger than we thought. J Virol. 81(4):2002-11. https://doi: 10.1128/JVI.01459-06.

[13] Mrass P, Oruganti SR, Fricke GM, Tafoya J, Byrum JR, Yang L, Hamilton SL, Miller MJ, Moses ME, Cannon JL (2017) ROCK regulates the intermittent mode of interstitial T cell migration in inflamed lungs. Nature Communications. 8(1):1010. https://doi: 10.1038/s41467-107-010

[14] Fricke GM, Letendre KA, Moses ME, Cannon JL (2016) Persistence and adaptation in immunity: T cells balance the extent and thoroughness of search. PLoS Comput Biol. 12(3):e1004818. https://doi:10.1371/journal.pcbi.1004818.32-2.

[15] Paulus M, Ichiko K, Byrum JR, Torres D, Baker SF, Cannon JL (2022) CXCR4 controls movement and degranulation of CD8⁺ T cells in the influenza-infected lung via differential effects on interaction and tissue scanning. bioRxiv. https://doi.org/10.1101/2022.09.13.507813.

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

• 396

  Page views

• 84

  Downloads

• 0

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.