Biology subjects

Cannon, J. L.

Publications and source records attributed to Cannon, J. L..

3 recordsLinked to original sources

bioRxiv2022

Quantitative analyses of T cell motion in tissue reveals factors driving T cell search in tissues

T cells are required to clear infection, moving first in lymph nodes to interact with antigen bearing dendritic cells leading to activation. T cells then move to sites of infection to find and clear infection. T cell motion plays a role in how quickly a T cell finds its target, from initial naive T cell activation by a dendritic cell to interaction with target cells in infected tissue. To better understand how different tissue environments might affect T cell motility, we compared multiple features of T cell motion including speed, persistence, turning angle, directionality, and confinement of motion from T cells moving in multiple tissues using tracks collected with microscopy from murine tissues. We quantitatively analyzed naive T cell motility within the lymph node and compared motility parameters with activated CD8 T cells moving within the villi of small intestine and lung under different activation conditions. Our motility analysis found that while the speeds and the overall displacement of T cells vary within all tissues analyzed, T cells in all tissues tended to persist at the same speed, particularly if the previous speed is very slow (less than 2 {micro}m/min) or very fast (greater than 8 {micro}m/min) with the exception of T cells in the villi for speeds greater than 10 {micro}m/min. Interestingly, we found that turning angles of T cells in the lung show a marked population of T cells turning at close to 180o, while T cells in lymph nodes and villi do not exhibit this "reversing" movement. Additionally, T cells in the lung showed significantly decreased meandering ratios and increased confinement compared to T cells in lymph nodes and villi. The combination of these differences in motility patterns led to a decrease in the total volume scanned by T cells in lung compared to T cells in lymph node and villi. These results suggest that the tissue environment in which T cells move can impact the type of motility and ultimately, the efficiency of T cell search for target cells within specialized tissues such as the lung.

immunology↗

bioRxiv2022

CXCR4 controls movement and degranulation of CD8+ T cells in the influenza-infected lung via differential effects on interaction and tissue scanning

Effector CD8+ T cell interactions are critical in controlling viral infection by directly killing infected cells but overabundant or sustained activation also exacerbates tissue damage. Chemokines promote the trafficking of effector CD8+ T cells into infected tissues, but we know little about how chemokines regulate the function of CD8+ T cells within tissues. Using a murine model of influenza A virus infection, we found that expression of the chemokine receptor CXCR4 by lung-infiltrating cytotoxic T cells correlated with the expression of the degranulation marker CD107a. Inhibition of CXCR4 reduced activation, adhesion, and degranulation of cytotoxic T cells in vitro and in vivo. Moreover, in live influenza-infected lung tissue, T cells stopped moving in lung regions with high levels of influenza antigen, and CXCR4 was essential for CD8+ T cells to execute this arrest signal fully. In contrast, CXCR4 increased the motility of CD8+ T cells in low-influenza areas of the lung. We also found that CXCR4 stimulated the effector function of lung-infiltrating cytotoxic T cells even after clearance of influenza virus, and inhibition of CXCR4 expedited the recovery of influenza-infected mice, despite delayed clearance of the replication-competent virus. Our results suggest that CXCR4 promotes the interaction strength of cytotoxic T cells in lung tissue through combined effects on T cell movement and interaction with virally infected target cells in influenza infected-lungs.

immunology↗

bioRxiv2021

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection, and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed ODE model. These results illustrate how realistic spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection. SummaryA key question in SARS-CoV-2 infection is why viral loads and patient outcomes are so different across individuals. Because its difficult to see how the virus spreads in the lungs of infected people, we developed Spatial Immune Model of Coronavirus (SIMCoV), a computational model that simulates hundreds of millions of cells, including lung cells and immune cells. SIMCoV simulates how virus grows and then declines, and the simulations match data observed in patients. SIMCoV shows that when there are more initial infection sites, the virus grows to a higher peak. The model also shows how the timing of the immune response, particularly the T cell response, can affect how long the virus persists and whether it is ultimately cleared from the lungs. SIMCoV shows that the different viral loads in different patients can be explained by how many different places the virus is initially seeded inside their lungs. We explicitly add the branching airway structure of the lung into the model and show that virus spreads slightly faster than it would in a two-dimensional layer of lung cells, but much slower than traditional mathematical models based on differential equations. These results illustrate how realistic spatial computational models can improve understanding of how SARS-CoV-2 infection spreads in the lung.

immunology↗