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

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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

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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

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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

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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).

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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

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