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

Abstract

Pathogen-specific CD8 T cells face the problem of finding rare cells that present their cognate antigen either in the lymph node or infected tissue. To optimize the search for rare targets it has been proposed that T cells might perform a random walk with long displacements called Levy walks enabling superdiffusive behavior and shorter search times1–3. Many agents ranging from cells to large animals have been found to perform Levy walks3–5 suggesting that Levy walk-based search strategies may be evolutionary selected6,7. However, whether random walk patterns are driven by agent-intrinsic programs or shaped by environmental factors remains largely unknown8. To address this problem we examined the behavior of activated CD8 T cells in the liver where both the movement of the cells and the underlying structural constraints can be clearly defined. We show that Plasmodium-specific liver-localized CD8 T cells perform short displacement, Brownian-like walks and yet display superdiffusive overall displacement, the cardinal feature of efficient Levy walks. Because liver-localized CD8 T cells are mainly associated with liver sinusoids, simulations of Brownian or Levy walkers in structures derived from the liver sinusoids illustrate that structure alone can enforce superdiffusive movement. Moreover, Brownian walkers require less time to find a rare target when T cells search for the infection in physiologically-derived liver structures. Importantly, analysis of fibroblastic reticular cell networks on which CD8 T cells move in lymph nodes also allows for superdiffusion in simulations, though this is not observed experimentally, suggesting that structure is not the only factor determining movement patterns of T cells. Our results strongly suggest that observed patterns of movement of CD8 T cells are likely to result from a combination of cell-intrinsic movement programs, physical constraints imposed by the environmental structures, and other environmental cues. Future work needs to focus on quantifying the relative contributions of these factors to the overall observed movement patterns of biological agents.

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

“…Lymphocyte movement is important to locate infections at peripheral sites, but how cells coordinate movements to achieve such a goal remains poorly defined [2, 3]. Typically, movement of cells in tissues in vivo is recorded using intravital microscopy at a particular frame rate (e.g., a small 3D volume of the liver of 500 × 500 × 50 µ m can be scanned every 20 sec, [3, 4]) and by segmenting individual time frames with software (e.g., ImageJ from the NIH or Imaris from Bitplane), 3D coordinates of multiple cells over time can be obtained. Several parameters such as mean square displacement and movement length distribution can be then generated from such digitized data, sometimes providing important insights into cell movement strategies [3, 5].…”

“…Typically, movement of cells in tissues in vivo is recorded using intravital microscopy at a particular frame rate (e.g., a small 3D volume of the liver of 500 × 500 × 50 µ m can be scanned every 20 sec, [3, 4]) and by segmenting individual time frames with software (e.g., ImageJ from the NIH or Imaris from Bitplane), 3D coordinates of multiple cells over time can be obtained. Several parameters such as mean square displacement and movement length distribution can be then generated from such digitized data, sometimes providing important insights into cell movement strategies [3, 5]. From cell displacements per time interval or from overall displacement of the cell over the course of the whole movie, one can calculate the cell’s speed.…”

“…In the Pareto distribution, r ≥ r min . In simulations, we assumed α = 5.5, corresponding to a Brownian-like distribution of movement lengths [3], , and . Thus, κ t determines the degree of walk persistence (i.e., the average turning angle) and r min and α determine the speed of the cell movement.…”

“…However, it is possible that cells differ in their ability to turn, e.g., either because of cell-intrinsic program or because of the environmental constrains by physical barriers or chemical cues in specific locations of the tissue [3]. Therefore, in the third set of simulations we allowed every cell to have an intrinsic turning ability (defined by individual κ t ) drawn from a lognormal distribution (to allow for a broader range of κ t ).…”

“…Here we argue that this negative correlation is a natural consequence of fact that both cell's speed and average turning angle are estimated from the data; however, the frequency of sampling does not allow to discriminate between the alternatives. Propensity of moving cells to keep forward movement when environment and conditions are constant can be postulated as a type of "biological inertia" (or "biological conservation of momentum") specific to the cells [3]. Per such postulate changes in the environment (e.g., shape of the surface on which cells move, cues, or change in nutrients) are responsible for cells to change movement, e.g., to stop and/or turn.…”