Directed acyclic graphs (DAGs), which offer systematic representations of causal relationships, have become an established framework for the analysis of causal inference in epidemiology, often being used to determine covariate adjustment sets for minimizing confounding bias. DAGitty is a popular web application for drawing and analysing DAGs. Here we introduce the R package ‘dagitty’, which provides access to all of the capabilities of the DAGitty web application within the R platform for statistical computing, and also offers several new functions. We describe how the R package ‘dagitty’ can be used to: evaluate whether a DAG is consistent with the dataset it is intended to represent; enumerate ‘statistically equivalent’ but causally different DAGs; and identify exposure-outcome adjustment sets that are valid for causally different but statistically equivalent DAGs. This functionality enables epidemiologists to detect causal misspecifications in DAGs and make robust inferences that remain valid for a range of different DAGs. The R package ‘dagitty’ is available through the comprehensive R archive network (CRAN) at [https://cran.r-project.org/web/packages/dagitty/]. The source code is available on github at [https://github.com/jtextor/dagitty]. The web application ‘DAGitty’ is free software, licensed under the GNU general public licence (GPL) version 2 and is available at [http://dagitty.net/].

Directed acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require conditioning when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparency and utility in future research. Original health research articles published during 1999–2017 mentioning ‘directed acyclic graphs’ (or similar) or citing DAGitty were identified from Scopus, Web of Science, Medline and Embase. Data were extracted on the reporting of: estimands, DAGs and adjustment sets, alongside the characteristics of each article’s largest DAG. A total of 234 articles were identified that reported using DAGs. A fifth (n = 48, 21%) reported their target estimand(s) and half (n = 115, 48%) reported the adjustment set(s) implied by their DAG(s). Two-thirds of the articles (n = 144, 62%) made at least one DAG available. DAGs varied in size but averaged 12 nodes [interquartile range (IQR): 9–16, range: 3–28] and 29 arcs (IQR: 19–42, range: 3–99). The median saturation (i.e. percentage of total possible arcs) was 46% (IQR: 31–67, range: 12–100). 37% (n = 53) of the DAGs included unobserved variables, 17% (n = 25) included ‘super-nodes’ (i.e. nodes containing more than one variable) and 34% (n = 49) were visually arranged so that the constituent arcs flowed in the same direction (e.g. top-to-bottom). There is substantial variation in the use and reporting of DAGs in applied health research. Although this partly reflects their flexibility, it also highlights some potential areas for improvement. This review hence offers several recommendations to improve the reporting and use of DAGs in future research.

Therapies targeting immune checkpoint molecules CTLA-4 and PD-1/PD-L1 have advanced the field of cancer immunotherapy. New mAbs targeting different immune checkpoint molecules, such as TIM3, CD27, and OX40, are being developed and tested in clinical trials. To make educated decisions and design new combination treatment strategies, it is vital to learn more about coexpression of both inhibitory and stimulatory immune checkpoints on individual cells within the tumor microenvironment. Recent advances in multiple immunolabeling and multispectral imaging have enabled simultaneous analysis of more than three markers within a single formalin-fixed paraffin-embedded tissue section, with accurate cell discrimination and spatial information. However, multiplex immunohistochemistry with a maximized number of markers presents multiple difficulties. These include the primary Ab concentrations and order within the multiplex panel, which are of major importance for the staining result. In this article, we report on the development, optimization, and application of an eight-color multiplex immunohistochemistry panel, consisting of PD-1, PD-L1, OX40, CD27, TIM3, CD3, a tumor marker, and DAPI. This multiplex panel allows for simultaneous quantification of five different immune checkpoint molecules on individual cells within different tumor types. This analysis revealed major differences in the immune checkpoint expression patterns across tumor types and individual tumor samples. This method could ultimately, by characterizing the tumor microenvironment of patients who have been treated with different immune checkpoint modulators, form the rationale for the design of immune checkpoint-based immunotherapy in the future.

Impaired immune responses in the elderly lead to reduced vaccine efficacy and increased susceptibility to viral infections. Although several groups have documented age-dependent defects in adaptive immune priming, the deficits that occur prior to antigen encounter remain largely unexplored. Herein, we identify novel mechanisms for compromised adaptive immunity that occurs with aging in the context of infection with West Nile virus (WNV), an encephalitic flavivirus that preferentially causes disease in the elderly. An impaired IgM and IgG response and enhanced vulnerability to WNV infection during aging was linked to delayed germinal center formation in the draining lymph node (DLN). Adoptive transfer studies and two-photon intravital microscopy revealed a decreased trafficking capacity of donor naïve CD4⁺ T cells from old mice, which manifested as impaired T cell diapedesis at high endothelial venules and reduced cell motility within DLN prior to antigen encounter. Furthermore, leukocyte accumulation in the DLN within the first few days of WNV infection or antigen-adjuvant administration was diminished more generally in old mice and associated with a second aging-related defect in local cytokine and chemokine production. Thus, age-dependent cell-intrinsic and environmental defects in the DLN result in delayed immune cell recruitment and antigen recognition. These deficits compromise priming of early adaptive immune responses and likely contribute to the susceptibility of old animals to acute WNV infection. While West Nile virus (WNV) infection preferentially causes severe neuroinvasive disease in elderly humans, the basis for this epidemiological linkage has remained uncertain. Here, we studied the impact of aging on WNV pathogenesis and immune responses using a mouse model of infection. Old mice showed increased lethality after WNV infection compared to adult mice, and this phenotype was associated with delayed antibody responses, and higher levels of virus infection in the blood, spleen, and brain. Detailed immunological and microscopic analyses revealed defects in germinal center development in the draining lymph node and impaired migratory capacity of naïve CD4⁺ T cells within days of WNV infection. This deficit was worsened by a separate age-related deficiency in the production of several chemokines that recruit leukocytes to inflamed lymph nodes. Thus, age-dependent defects in naïve CD4⁺ T cell trafficking and in the draining lymph node environment result in delayed initiation of antiviral responses and a failure to control WNV, which ultimately contributes to higher rates of mortality after infection.

Cell migration is a complex process involving many intracellular and extracellular factors, with different cell types adopting sometimes strikingly different morphologies. Modeling realistically behaving cells in tissues is computationally challenging because it implies dealing with multiple levels of complexity. We extend the Cellular Potts Model with an actin-inspired feedback mechanism that allows small stochastic cell rufflings to expand to cell protrusions. This simple phenomenological model produces realistically crawling and deforming amoeboid cells, and gliding half-moon shaped keratocyte-like cells. Both cell types can migrate randomly or follow directional cues. They can squeeze in between other cells in densely populated environments or migrate collectively. The model is computationally light, which allows the study of large, dense and heterogeneous tissues containing cells with realistic shapes and migratory properties. Cell migration is involved in vital processes like morphogenesis, regeneration and immune system responses, but can also play a central role in pathological processes like metastasization. Computational models have been successfully employed to explain how single cells migrate, and to study how diverse cell-cell interactions contribute to tissue level behavior. However, there are few models that implement realistic cell shapes in multicellular simulations. The method we present here is able to reproduce two different types of motile cells—amoeboid and keratocyte-like cells. Amoeboid cells are highly motile and deform frequently; many cells can act amoeboid in certain circumstances e.g., immune system cells, epithelial cells, individually migrating cancer cells. Keratocytes are (fish) epithelial cells which are famous for their ability to preserve their shape and direction when migrating individually; during wound healing, keratocytes migrate collectively, in sheets, to the site needing reepithelialization. Our method is computationally simple, improves the realism of multicellular simulations and can help assess the tissue level impact of specific cell shapes. For example, it can be employed to study the tissue scanning strategies of leukocytes, the circumstances in which cancer cells adopt amoeboid migration strategies, or the collective migration of keratocytes.