T cell migration, search strategies and mechanisms

Abstract

T cell migration is essential for T cell responses; it allows for the detection of cognate antigen at the surface of antigen-presenting cells and for interactions with other cells involved in the immune response. Although appearing random, growing evidence suggests that T cell motility patterns are strategic and governed by mechanisms that are optimized for both the activation stage of the cell and for environment-specific cues. In this Opinion article, we discuss how the combined effects of T cell-intrinsic and -extrinsic forces influence T cell motility patterns in the context of highly complex tissues that are filled with other cells involved in parallel motility. In particular, we examine how insights from 'search theory' can be used to describe T cell movement across an 'exploitation-exploration trade-off' in the context of activation versus effector function and lymph nodes versus peripheral tissues.

Figure 1. T cell motility according to its state of activation and the microenvironment

A- Naïve T cells migrate primarily in lymphoid organs, looking for their cognate peptide (in yellow) presented by an APC. The frequency of the cognate APC within the midst of APCs is extremely rare, and there is no information on the location of the cognate APC. Naïve T cells in lymph nodes use a diffusive (e.g. Brownian-type) or subdiffusive random walk. T cell migration is in part cell-autonomous, supported by guiding stromal structures and relies on chemokinesis. B- The location of Recently activated T cells, i.e T cells that found their cognate peptide, is restricted to the secondary lymphoid organ in which they got primed. However, their migration is now directionally biased. Chemotaxis signals are sent by the cognate APC, most likely to attract recently activated T cells to a distinct differentiating environment. C- Once primed T cells efficiently differentiate in effector T cells, they leave secondary lymphoid organs to reach the site of injury in peripheral tissues. Within this tissue, effector T cells will have to find their cognate APC again during antigen reencounter. Depending on the tissue and injury, effector T cell migration in peripheral sites has been described as a diffusive (e.g. Brownian-type) or superdiffusive (e.g. Lévy-type) random walk. Haptokinesis, haptotaxis and chemotaxis have been shown to shape this walk in some tissues.

Figure 2. Factors influencing T cell motility features and platforms used to study T cell migration characteristics

Figure describes the different T cell motility behaviors, from normal (Brownian-type) (left) to ballistic (highly directional) (right) passing through anomalous (superdiffusive) regimes and the different factors that are known to dictate this behavior. Strategic motility is influenced by cell-autonomous features, as well as environmental guidance structures, which include confinement, chemokinesis and haptokinesis. Depending on the pattern of the guiding structures themselves, directional bias will be observed. Strategic motility will also be influenced by the APC itself. In particular, the frequency and location of cognate APCs in relationship to total APCs and the signals they send out to be found (mainly integrins and chemokines) will affect T cell motility patterns. In this context, chemotaxis will efficiently promote directional T cell migration. Finally, different platforms can be used to study features of T cell motility, each platform allowing for the study of specific migration parameters. Microchannels allow for the study of cell-autonomous migration, confinement, chemokinesis or chemotaxis. 3-Dimension (3D) matrices resemble fibers found in tissues and allow control over rigidity and chemotaxis studies.

Figure 3. System biology and integrated studies to unravel search in biology

Figure describes how the integration of current search theories, empirical testing and the analysis of patterns of migration are necessary to understand what governs cell migration and how it relates to search efficiency.

Figure. Mean squared displacement (MSD) over time for different types of random walks: diffusive, subdiffusive and superdiffusive walks