Chemotactic Migration of T Cells towards Dendritic Cells Promotes the Detection of Rare Antigens

Abstract

In many immunological processes chemoattraction is thought to play a role in guiding cells to their sites of action. However, based on in vivo two-photon microscopy experiments in the absence of cognate antigen, T cell migration in lymph nodes (LNs) has been roughly described as a random walk. Although it has been shown that dendritic cells (DCs) carrying cognate antigen in some circumstances attract T cells chemotactically, it is currently still unclear whether chemoattraction of T cells towards DCs helps or hampers scanning. Chemoattraction towards DCs could on the one hand help T cells to rapidly find DCs. On the other hand, it could be deleterious if DCs become shielded by a multitude of attracted yet non-specific T cells. Results from a recent simulation study suggested that the deleterious effect dominates. We re-addressed the question whether T cell chemoattraction towards DCs is expected to promote or hamper the detection of rare antigens using the Cellular Potts Model, a formalism that allows for dynamic, flexible cellular shapes and cell migration. Our simulations show that chemoattraction of T cells enhances the DC scanning efficiency, leading to an increased probability that rare antigen-specific T cells find DCs carrying cognate antigen. Desensitization of T cells after contact with a DC further improves the scanning efficiency, yielding an almost threefold enhancement compared to random migration. Moreover, the chemotaxis-driven migration still roughly appears as a random walk, hence fine-tuned analysis of cell tracks will be required to detect chemotaxis within microscopy data.

A spatial model of the efficiency of T cell search in the influenza-infected lung

“…CD8 T cells conduct two ‘searches’ in two different tissue environments, first to encounter antigen-loaded dendritic cells in the lymph node, and second to encounter local inflammatory signals in the infected lung. Search problems in the lymph node have been simulated using live cell imaging data to provide reliable parameters of cell movement (Mirsky et al, 2011; Vroomans et al, 2012). Our model focused on the second search, i.e.…”

“…The use of an ABM can complement spatially homogeneous differential equation models (Beltman et al, 2007; Textor et al, 2014; Vroomans et al, 2012; Zheng et al, 2008). In this study, we tested how chemokine-directed T cell search contributes to infection clearance.…”

“…Ordinary differential equation (ODE) models that are fit to empirical data (Handel and Antia, 2008; Lee et al, 2009; Miao et al, 2010; Saenz et al, 2010; Murillo et al, 2013; Crauste et al, 2015; Price et al, 2015) have elucidated viral population level dynamics but are unable to perceive localized spatial effects. Spatial models, on the other hand, have been developed to examine interactions between dendritic cells and T cells in the lymph node (Beauchemin et al, 2005; Beltman et al, 2007; Zheng et al, 2008; Mirsky et al, 2011; Celli et al, 2012; Vroomans et al, 2012; Textor et al, 2014), but have not been extended to consider local conditions in the lung. In this paper, we examine how the unique properties of three different strains of influenza, in the presence of chemokines, affect T-cell search in the human lung.…”

Subtle CXCR3-Dependent Chemotaxis of CTLs within Infected Tissue Allows Efficient Target Localization

“…The presence of substantial numbers of nonspecific T cells could reduce the efficiency of cognate CTL-target cell contacts by steric hindrance. For the similar case of T cell scanning of dendritic cells within lymph nodes, we recently demonstrated that such competition can be alleviated if bystander T cells lose their sensitivity to chemokine upon reaching the APC (33). By the same token, in the case of skin infection, this 'logistic' problem could be reduced when effector T cells that do not encounter their cognate Ag were to leave the infected area, a concept that is supported by our in silico simulations of bystander cells.…”

Modeling Lymph Flow and Fluid Exchange with Blood Vessels in Lymph Nodes

“…39 Several other models of LNs have been developed using agent-based modeling, and cellular Potts model techniques, and have been able to simulate cellular motions, cell-cell interactions, and cell influx and efflux. [40][41][42] All the techniques described so far are useful in studying cellular interactions, but they all fail to account for the transport of lymph and the fluid exchange in the node. A computational fluid dynamics model based on the experimental data can investigate the lymph flow in the mouse LNs, and further expand our knowledge of antigen and chemokine transport in the node.…”