A hitchhiker's guide to myeloid cell subsets: practical implementation of a novel mononuclear phagocyte classification system

The classification of mononuclear phagocytes as either dendritic cells or macrophages has been mainly based on morphology, the expression of surface markers, and assumed functional specialization. We have recently proposed a novel classification system of mononuclear phagocytes based on their ontogeny. Here, we discuss the practical application of such a classification system through a number of prototypical examples we have encountered while hitchhiking from one subset to another, across species and between steady-state and inflammatory settings. Finally, we discuss the advantages and drawbacks of such a classification system and propose a number of improvements to move from theoretical concepts to concrete guidelines

Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data.

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org)

DC Respond to Cognate T Cell Interaction in the Antigen-Challenged Lymph Node

Dendritic cells (DC) are unrivaled in their potential to prime naive T cells by presenting antigen and providing costimulation. DC are furthermore believed to decode antigen context by virtue of pattern recognition receptors and to polarize T cells through cytokine secretion toward distinct effector functions. Diverse polarized T helper (TH) cells have been explored in great detail. In contrast, studies of instructing DC have to date largely been restricted to in vitro settings or adoptively transferred DC. Here we report efforts to unravel the DC response to cognate T cell encounter in antigen-challenged lymph nodes (LN). Mice engrafted with antigen-specific T cells were immunized with nanoparticles (NP) entrapping adjuvants and absorbed with antigen to study the immediate DC response to T cell encounter using bulk and single cell RNA-seq profiling. NP induced robust antigen-specific TH1 cell responses with minimal bystander activation. Fluorescent-labeled NP allowed identification of antigen-carrying DC and focus on transcriptional changes in DC that encounter T cells. Our results support the existence of a bi-directional crosstalk between DC and T cells that promotes TH1 responses, including involvement of the ubiquitin-like molecule Isg15 that merits further study

Hepatic macrophage responses in inflammation, a function of plasticity, heterogeneity or both?

peer-reviewedWith the increasing availability and accessibility of single cell technologies, much attention has been given to delineating the specific populations of cells present in any given tissue. In recent years, hepatic macrophage heterogeneity has also begun to be examined using these strategies. While previously any macrophage in the liver was considered to be a Kupffer cell (KC), several studies have recently revealed the presence of distinct subsets ofhepatic macrophages, including those distinct from KCs both under homeostatic and non-homeostatic conditions. This heterogeneity has brought the concept of macrophage plasticity into question. Are KCs really as plastic as once thought, being capable of responding efficiently and specifically to any given stimuli? Or are the differential responses observed from hepatic macrophages in distinct settings due to the presence of multiple subsets of these cells? With these questions in mind, here we examine what is currently understood regarding hepatic macrophage heterogeneity in mouse and human and examine the role of heterogeneity vs plasticity in regards to hepatic macrophage responses in settings of both pathogen-induced and sterile inflammation

USP18-Based Negative Feedback Control Is Induced by Type I and Type III Interferons and Specifically Inactivates Interferon α Response

Type I interferons (IFN) are cytokines that are rapidly secreted upon microbial infections and regulate all aspects of the immune response. In humans 15 type I IFN subtypes exist, of which IFN α2 and IFN β are used in the clinic for treatment of different pathologies. IFN α2 and IFN β are non redundant in their expression and in their potency to exert specific bioactivities. The more recently identified type III IFNs (3 IFN λ or IL-28/IL-29) bind an unrelated cell-type restricted receptor. Downstream of these two receptor complexes is a shared Jak/Stat pathway. Several mechanisms that contribute to the shut down of the IFN-induced signaling have been described at the molecular level. In particular, it has long been known that type I IFN induces the establishment of a desensitized state. In this work we asked how the IFN-induced desensitization integrates into the network built by the multiple type I IFN subtypes and type III IFNs. We show that priming of cells with either type I IFN or type III IFN interferes with the cell's ability to further respond to all IFN α subtypes. Importantly, primed cells are differentially desensitized in that they retain sensitivity to IFN β. We show that USP18 is necessary and sufficient to induce differential desensitization, by impairing the formation of functional binding sites for IFN α2. Our data highlight a new type of differential between IFNs α and IFN β and underline a cross-talk between type I and type III IFN. This cross-talk could shed light on the reported genetic variation in the IFN λ loci, which has been associated with persistence of hepatitis C virus and patient's response to IFN α2 therapy

Mapping determinants of cytokine signaling via protein engineering

Cytokines comprise a large family of secreted ligands that are critical for the regulation of immune homeostasis. Cytokines initiate signaling via dimerization or oligomerization of the cognate receptor subunits, triggering the activation of the Janus Kinases (JAKs)/ signal transducer and activator of transcription (STATs) pathway and the induction of specific gene expression programs and bioactivities. Deregulation of cytokines or their downstream signaling pathways are at the root of many human disorders including autoimmunity and cancer. Identifying and understanding the mechanistic principles that govern cytokine signaling will, therefore, be highly important in order to harness the therapeutic potential of cytokines. In this review, we will analyze how biophysical (ligand-receptor binding geometry and affinity) and cellular (receptor trafficking and intracellular abundance of signaling molecules) parameters shape the cytokine signalosome and cytokine functional pleiotropy; from the initial cytokine binding to its receptor to the degradation of the cytokine receptor complex in the proteasome and/or lysosome. We will also discuss how combining advanced protein engineering with detailed signaling and functional studies has opened promising avenues to tackle complex questions in the cytokine signaling field

IFN-Lambda (IFN-λ) Is Expressed in a Tissue-Dependent Fashion and Primarily Acts on Epithelial Cells In Vivo

Interferons (IFN) exert antiviral, immunomodulatory and cytostatic activities. IFN-α/β (type I IFN) and IFN-λ (type III IFN) bind distinct receptors, but regulate similar sets of genes and exhibit strikingly similar biological activities. We analyzed to what extent the IFN-α/β and IFN-λ systems overlap in vivo in terms of expression and response. We observed a certain degree of tissue specificity in the production of IFN-λ. In the brain, IFN-α/β was readily produced after infection with various RNA viruses, whereas expression of IFN-λ was low in this organ. In the liver, virus infection induced the expression of both IFN-α/β and IFN-λ genes. Plasmid electrotransfer-mediated in vivo expression of individual IFN genes allowed the tissue and cell specificities of the responses to systemic IFN-α/β and IFN-λ to be compared. The response to IFN-λ correlated with expression of the α subunit of the IFN-λ receptor (IL-28Rα). The IFN-λ response was prominent in the stomach, intestine and lungs, but very low in the central nervous system and spleen. At the cellular level, the response to IFN-λ in kidney and brain was restricted to epithelial cells. In contrast, the response to IFN-α/β was observed in various cell types in these organs, and was most prominent in endothelial cells. Thus, the IFN-λ system probably evolved to specifically protect epithelia. IFN-λ might contribute to the prevention of viral invasion through skin and mucosal surfaces

Clustering of small - sample single - cell RNA - seq data via feature clustering and selection

We present FeatClust, a software tool for clustering small sample size single-cell RNA-Seq datasets. The FeatClust approach is based on feature selection. It divides features into several groups by performing agglomerative hierarchical clustering and then iteratively clustering the samples and removing features belonging to groups with the least variance across samples. The optimal number of feature groups is selected based on silhouette analysis on the clustered data, i.e., selecting the clustering with the highest average silhouette coefficient. FeatClust also allows one to visually choose the number of clusters if it is not known, by generating silhouette plot for a chosen number of groupings of the dataset. We cluster five small sample single-cell RNA-seq datasets and use the adjusted rand index metric to compare the results with other clustering packages. The results are promising and show the effectiveness of FeatClust on small sample size datasets