Chemotaxis: Dendritic cells as trendsetters of the immune response.

A new study reports that dendritic cells actively shape the CCL19 chemokine gradient to which they respond and that the chemokine receptor CCR7 both senses CCL19 and mediates its internalisation. Generation of local changes in chemokines allows coordination of movement over longer distances than previous models could explain.

Cotransfer of antigen and contextual information harmonizes peripheral and lymph node conventional dendritic cell activation.

T cell responses against infections and cancer are directed by conventional dendritic cells (cDCs) in lymph nodes distant from the site of challenge. Migratory cDCs, which travel from the tissue to the lymph node, not only drive initial T cell activation but also transfer antigen to lymph node-resident cDCs. These resident cells have essential roles defining the character of the resulting T cell response; however, it is unknown how they can appropriately process and present antigens to suitably direct responses given their spatial separation. Here, using a novel strain of influenza A and a modified melanoma model, we show that tissue and lymph node cDC activation is harmonized and that this is driven by cotransfer of contextual cues. In the tumor, incomplete cDC activation in the tumor microenvironment is mirrored by lymph node-resident cDCs, whereas during influenza infection, pathogen-associated molecular patterns cotransferred with antigen drive TLR signaling in resident cDCs and their subsequent robust activation. This cotransfer mechanism explains how individual antigens can be handled distinctly by resident cDCs and how signals driving poor tumoral cDC activation further impact the lymph node. Our findings clarify how tissue context dictates antigenic and, consequently, T cell fate in the lymph node.

Superinfection exclusion creates spatially distinct influenza virus populations.

Interactions between viruses during coinfections can influence viral fitness and population diversity, as seen in the generation of reassortant pandemic influenza A virus (IAV) strains. However, opportunities for interactions between closely related viruses are limited by a process known as superinfection exclusion (SIE), which blocks coinfection shortly after primary infection. Using IAVs, we asked whether SIE, an effect which occurs at the level of individual cells, could limit interactions between populations of viruses as they spread across multiple cells within a host. To address this, we first measured the kinetics of SIE in individual cells by infecting them sequentially with 2 isogenic IAVs, each encoding a different fluorophore. By varying the interval between addition of the 2 IAVs, we showed that early in infection SIE does not prevent coinfection, but that after this initial lag phase the potential for coinfection decreases exponentially. We then asked how the kinetics of SIE onset controlled coinfections as IAVs spread asynchronously across monolayers of cells. We observed that viruses at individual coinfected foci continued to coinfect cells as they spread, because all new infections were of cells that had not yet established SIE. In contrast, viruses spreading towards each other from separately infected foci could only establish minimal regions of coinfection before reaching cells where coinfection was blocked. This created a pattern of separate foci of infection, which was recapitulated in the lungs of infected mice, and which is likely to be applicable to many other viruses that induce SIE. We conclude that the kinetics of SIE onset segregate spreading viral infections into discrete regions, within which interactions between virus populations can occur freely, and between which they are blocked.

Building a healthy mouse model ecosystem to interrogate cancer biology.

In a recent study, Sargent et al. characterise several novel Rag1-/- mouse strains and demonstrate that genetic background strongly influences xenograft development and phenotype. Here, we discuss this work within the broader context of cancer mouse modelling. We argue that new technologies will enable insights into how specific models align with human disease states and that this knowledge can be used to develop a diverse ecosystem of complementary mouse models of cancer. By utilising these diverse, well-characterised models to provide multiple perspectives on specific cancers, it should be possible to reduce the inappropriate attrition of sound hypotheses while protecting against false positives. Furthermore, careful re-introduction of biological variation, be that through outbred populations, environmental diversity or including animals of both sexes, can ensure that results are more broadly applicable and are less impacted by particular traits of homogeneous experimental populations. Thus, careful characterisation and judicious use of an array of mouse models provides an opportunity to address some of the issues surrounding both the reproducibility and translatability crises often referenced in pre-clinical cancer research.