A Quantitative Multivariate Model of Human Dendritic Cell-T Helper Cell Communication.

Cell-cell communication involves a large number of molecular signals that function as words of a complex language whose grammar remains mostly unknown. Here, we describe an integrative approach involving (1) protein-level measurement of multiple communication signals coupled to output responses in receiving cells and (2) mathematical modeling to uncover input-output relationships and interactions between signals. Using human dendritic cell (DC)-T helper (Th) cell communication as a model, we measured 36 DC-derived signals and 17 Th cytokines broadly covering Th diversity in 428 observations. We developed a data-driven, computationally validated model capturing 56 already described and 290 potentially novel mechanisms of Th cell specification. By predicting context-dependent behaviors, we demonstrate a new function for IL-12p70 as an inducer of Th17 in an IL-1 signaling context. This work provides a unique resource to decipher the complex combinatorial rules governing DC-Th cell communication and guide their manipulation for vaccine design and immunotherapies.

Publisher Correction: The discovery of human TSLP as a critical epithelial cytokine in type 2 immunity and allergic disease.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Adjustment of dendritic cells to the breast-cancer microenvironment is subset specific.

The functions and transcriptional profiles of dendritic cells (DCs) result from the interplay between ontogeny and tissue imprinting. How tumors shape human DCs is unknown. Here we used RNA-based next-generation sequencing to systematically analyze the transcriptomes of plasmacytoid pre-DCs (pDCs), cell populations enriched for type 1 conventional DCs (cDC1s), type 2 conventional DCs (cDC2s), CD14+ DCs and monocytes-macrophages from human primary luminal breast cancer (LBC) and triple-negative breast cancer (TNBC). By comparing tumor tissue with non-invaded tissue from the same patient, we found that 85% of the genes upregulated in DCs in LBC were specific to each DC subset. However, all DC subsets in TNBC commonly showed enrichment for the interferon pathway, but those in LBC did not. Finally, we defined transcriptional signatures specific for tumor DC subsets with a prognostic effect on their respective breast-cancer subtype. We conclude that the adjustment of DCs to the tumor microenvironment is subset specific and can be used to predict disease outcome. Our work also provides a resource for the identification of potential targets and biomarkers that might improve antitumor therapies.

Diversification of human plasmacytoid predendritic cells in response to a single stimulus.

Innate immune cells adjust to microbial and inflammatory stimuli through a process termed environmental plasticity, which links a given individual stimulus to a unique activated state. Here, we report that activation of human plasmacytoid predendritic cells (pDCs) with a single microbial or cytokine stimulus triggers cell diversification into three stable subpopulations (P1-P3). P1-pDCs (PD-L1+CD80-) displayed a plasmacytoid morphology and specialization for type I interferon production. P3-pDCs (PD-L1-CD80+) adopted a dendritic morphology and adaptive immune functions. P2-pDCs (PD-L1+CD80+) displayed both innate and adaptive functions. Each subpopulation expressed a specific coding- and long-noncoding-RNA signature and was stable after secondary stimulation. P1-pDCs were detected in samples from patients with lupus or psoriasis. pDC diversification was independent of cell divisions or preexisting heterogeneity within steady-state pDCs but was controlled by a TNF autocrine and/or paracrine communication loop. Our findings reveal a novel mechanism for diversity and division of labor in innate immune cells.

Of Human DC Migrants and Residents.

Migration from peripheral tissues to lymph nodes is a key feature of dendritic cells (DCs), but little is known about the migration patterns of human DCs. By analyzing multiple lymphoid organs and tissues from the same donors, Granot et al. propose that the two main subsets of human DCs display different migratory capacity.

ICELLNET v2: a versatile method for cell-cell communication analysis from human transcriptomic data.

SUMMARY: Several methods have been developed in the past years to infer cell-cell communication networks from transcriptomic data based on ligand and receptor expression. Among them, ICELLNET is one of the few approaches to consider the multiple subunits of ligands and receptors complexes to infer and quantify cell communication. In here, we present a major update of ICELLNET. As compared to its original implementation, we (i) drastically expanded the ICELLNET ligand-receptor database from 380 to 1669 biologically curated interactions, (ii) integrated important families of communication molecules involved in immune crosstalk, cell adhesion, and Wnt pathway, (iii) optimized ICELLNET framework for single-cell RNA sequencing data analyses, (iv) provided new visualizations of cell-cell communication results to facilitate prioritization and biological interpretation. This update will broaden the use of ICELLNET by the scientific community in different biological fields. AVAILABILITY AND IMPLEMENTATION: ICELLNET package is implemented in R. Source code, documentation and tutorials are available on GitHub (https://github.com/soumelis-lab/ICELLNET).

Extracellular vesicles from triple negative breast cancer promote pro-inflammatory macrophages associated with better clinical outcome.

Tumor associated macrophages (TAMs), which differentiate from circulating monocytes, are pervasive across human cancers and comprise heterogeneous populations. The contribution of tumor-derived signals to TAM heterogeneity is not well understood. In particular, tumors release both soluble factors and extracellular vesicles (EVs), whose respective impact on TAM precursors may be different. Here, we show that triple negative breast cancer cells (TNBCs) release EVs and soluble molecules promoting monocyte differentiation toward distinct macrophage fates. EVs specifically promoted proinflammatory macrophages bearing an interferon response signature. The combination in TNBC EVs of surface CSF-1 promoting survival and cargoes promoting cGAS/STING or other activation pathways led to differentiation of this particular macrophage subset. Notably, macrophages expressing the EV-induced signature were found among patients' TAMs. Furthermore, higher expression of this signature was associated with T cell infiltration and extended patient survival. Together, this data indicates that TNBC-released CSF-1-bearing EVs promote a tumor immune microenvironment associated with a better prognosis in TNBC patients.

GM-CSF-activated human dendritic cells promote type 1 T follicular helper cell polarization in a CD40-dependent manner.

T follicular helper (Tfh) cells regulate humoral responses and present a marked phenotypic and functional diversity. Type 1 Tfh (Tfh1) cells were recently identified and associated with disease severity in infection and autoimmune diseases. The cellular and molecular requirements to induce human Tfh1 differentiation are not known. Here, using single-cell RNA sequencing (scRNAseq) and protein validation, we report that human blood CD1c+ dendritic cells (DCs) activated by GM-CSF (also known as CSF2) drive the differentiation of naive CD4+ T cells into Tfh1 cells. These Tfh1 cells displayed typical Tfh molecular features, including high levels of PD-1 (encoded by PDCD1), CXCR5 and ICOS. They co-expressed BCL6 and TBET (encoded by TBX21), and secreted large amounts of IL-21 and IFN-γ (encoded by IFNG). Mechanistically, GM-CSF triggered the emergence of two DC sub-populations defined by their expression of CD40 and ICOS ligand (ICOS-L), presenting distinct phenotypes, morphologies, transcriptomic signatures and functions. CD40High ICOS-LLow DCs efficiently induced Tfh1 differentiation in a CD40-dependent manner. In patients with mild COVID-19 or latent Mycobacterium tuberculosis infection, Tfh1 cells were positively correlated with a CD40High ICOS-LLow DC signature in scRNAseq of peripheral blood mononuclear cells or blood transcriptomics, respectively. Our study uncovered a novel CD40-dependent Tfh1 axis with potential physiopathological relevance to infection. This article has an associated First Person interview with the first author of the paper.

Mitochondrial stress in advanced fibrosis and cirrhosis associated with chronic hepatitis B, chronic hepatitis C, or nonalcoholic steatohepatitis.

BACKGROUND AND AIMS: Hepatitis B virus (HBV) infection causes oxidative stress (OS) and alters mitochondria in experimental models. Our goal was to investigate whether HBV might alter liver mitochondria also in humans, and the resulting mitochondrial stress might account for the progression of fibrosis in chronic hepatitis B (CHB). APPROACH AND RESULTS: The study included 146 treatment-naïve CHB mono-infected patients. Patients with CHB and advanced fibrosis (AF) or cirrhosis (F3-F4) were compared to patients with no/mild-moderate fibrosis (F0-F2). Patients with CHB were further compared to patients with chronic hepatitis C (CHC; n = 33), nonalcoholic steatohepatatis (NASH; n = 12), and healthy controls ( n = 24). We detected oxidative damage to mitochondrial DNA (mtDNA), including mtDNA strand beaks, and identified multiple mtDNA deletions in patients with F3-F4 as compared to patients with F0-F2. Alterations in mitochondrial function, mitochondrial unfolded protein response, biogenesis, mitophagy, and liver inflammation were observed in patients with AF or cirrhosis associated with CHB, CHC, and NASH. In vitro , significant increases of the mitochondrial formation of superoxide and peroxynitrite as well as mtDNA damage, nitration of the mitochondrial respiratory chain complexes, and impairment of complex I occurred in HepG2 cells replicating HBV or transiently expressing hepatitits B virus X protein. mtDNA damage and complex I impairment were prevented with the superoxide-scavenging Mito-Tempo or with inducible nitric oxide synthase (iNOS)-specific inhibitor 1400 W. CONCLUSIONS: Our results emphasized the importance of mitochondrial OS, mtDNA damage, and associated alterations in mitochondrial function and dynamics in AF or cirrhosis in CHB and NASH. Mitochondria might be a target in drug development to stop fibrosis progression.

A mathematical model of Familial Mediterranean Fever predicts mechanisms controlling inflammation.

BACKGROUND: Familial Mediterranean Fever (FMF) is a monogenic disease caused by gain-of-function mutations in the MEditerranean FeVer (MEFV) gene. The molecular dysregulations induced by these mutations and the associated causal mechanisms are complex and intricate. OBJECTIVE: We sought to provide a computational model capturing the mechanistic details of biological pathways involved in FMF physiopathology and enabling the study of the patient's immune cell dynamics. METHODS: We carried out a literature survey to identify experimental studies published from January 2000 to December 2020, and integrated its results into a molecular map and a mathematical model. Then, we studied the network of molecular interactions and the dynamic of monocytes to identify key players for inflammation phenotype in FMF patients. RESULTS: We built a molecular map of FMF integrating in a structured manner the current knowledge regarding pathophysiological processes participating in the triggering and perpetuation of the disease flares. The mathematical model derived from the map reproduced patient's monocyte behavior, in particular its proinflammatory role via the Pyrin inflammasome activation. Network analysis and in silico experiments identified NF-κB and JAK1/TYK2 as critical to modulate IL-1ß- and IL-18-mediated inflammation. CONCLUSION: The in silico model of FMF monocyte proved its ability to reproduce in vitro observations. Considering the difficulties related to experimental settings and financial investments to test combinations of stimuli/perturbation in vitro, this model could be used to test complex hypotheses in silico, thus narrowing down the number of in vitro and ex vivo experiments to perform.

Hubness reduction improves clustering and trajectory inference in single-cell transcriptomic data.

MOTIVATION: Single-cell RNA-seq (scRNAseq) datasets are characterized by large ambient dimensionality, and their analyses can be affected by various manifestations of the dimensionality curse. One of these manifestations is the hubness phenomenon, i.e. existence of data points with surprisingly large incoming connectivity degree in the datapoint neighbourhood graph. Conventional approach to dampen the unwanted effects of high dimension consists in applying drastic dimensionality reduction. It remains unexplored if this step can be avoided thus retaining more information than contained in the low-dimensional projections, by correcting directly hubness. RESULTS: We investigated hubness in scRNAseq data. We show that hub cells do not represent any visible technical or biological bias. The effect of various hubness reduction methods is investigated with respect to the clustering, trajectory inference and visualization tasks in scRNAseq datasets. We show that hubness reduction generates neighbourhood graphs with properties more suitable for applying machine learning methods; and that it outperforms other state-of-the-art methods for improving neighbourhood graphs. As a consequence, clustering, trajectory inference and visualization perform better, especially for datasets characterized by large intrinsic dimensionality. Hubness is an important phenomenon characterizing data point neighbourhood graphs computed for various types of sequencing datasets. Reducing hubness can be beneficial for the analysis of scRNAseq data with large intrinsic dimensionality in which case it can be an alternative to drastic dimensionality reduction. AVAILABILITY AND IMPLEMENTATION: The code used to analyze the datasets and produce the figures of this article is available from https://github.com/sysbio-curie/schubness. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Definition of a novel breast tumor-specific classifier based on secretome analysis.

BACKGROUND: During cancer development, the normal tissue microenvironment is shaped by tumorigenic events. Inflammatory mediators and immune cells play a key role during this process. However, which molecular features most specifically characterize the malignant tissue remains poorly explored. METHODS: Within our institutional tumor microenvironment global analysis (T-MEGA) program, we set a prospective cohort of 422 untreated breast cancer patients. We established a dedicated pipeline to generate supernatants from tumor and juxta-tumor tissue explants and quantify 55 soluble molecules using Luminex or MSD. Those analytes belonged to five molecular families: chemokines, cytokines, growth factors, metalloproteinases, and adipokines. RESULTS: When looking at tissue specificity, our dataset revealed some breast tumor-specific characteristics, as IL-16, as well as some juxta-tumor-specific secreted molecules, as IL-33. Unsupervised clustering analysis identified groups of molecules that were specific to the breast tumor tissue and displayed a similar secretion behavior. We identified a tumor-specific cluster composed of nine molecules that were secreted fourteen times more in the tumor supernatants than the corresponding juxta-tumor supernatants. This cluster contained, among others, CCL17, CCL22, and CXCL9 and TGF-ß1, 2, and 3. The systematic comparison of tumor and juxta-tumor secretome data allowed us to mathematically formalize a novel breast cancer signature composed of 14 molecules that segregated tumors from juxta-tumors, with a sensitivity of 96.8% and a specificity of 96%. CONCLUSIONS: Our study provides the first breast tumor-specific classifier computed on breast tissue-derived secretome data. Moreover, our T-MEGA cohort dataset is a freely accessible resource to the biomedical community to help advancing scientific knowledge on breast cancer.

Compartmentalized multicellular crosstalk in lymph nodes coordinates the generation of potent cellular and humoral immune responses.

Distributed throughout the body, lymph nodes (LNs) constitute an important crossroad where resident and migratory immune cells interact to initiate antigen-specific immune responses supported by a dynamic 3-dimensional network of stromal cells, that is, endothelial cells and fibroblastic reticular cells (FRCs). LNs are organized into four major subanatomically separated compartments: the subcapsular sinus (SSC), the paracortex, the cortex, and the medulla. Each compartment is underpinned by particular FRC subsets that physically support LN architecture and delineate functional immune niches by appropriately providing environmental cues, nutrients, and survival factors to the immune cell subsets they interact with. In this review, we discuss how FRCs drive the structural and functional organization of each compartment to give rise to prosperous interactions and coordinate immune cell activities. We also discuss how reciprocal communication makes FRCs and immune cells perfect compatible partners for the generation of potent cellular and humoral immune responses.

Transcriptome-based identification of novel endotypes in adult atopic dermatitis.

BACKGROUND: Atopic dermatitis (AD) is a frequent and heterogeneous inflammatory skin disease, for which personalized medicine remains a challenge. High-throughput approaches have improved understanding of the complex pathophysiology of AD. However, a purely data-driven AD classification is still lacking. METHODS: To address this question, we applied an original unsupervised approach on the largest available transcriptome dataset of AD lesional (n = 82) and healthy (n = 213) skin biopsies. RESULTS: Taking into account pathological and physiological state, a variance-based filtering revealed 222 AD-specific hyper-variable genes that efficiently classified the AD samples into 4 clusters that turned out to be clinically and biologically distinct. Comparison of gene expressions between clusters identified 3 sets of upregulated genes used to derive metagenes (MGs): MG-I (19 genes) was associated with IL-1 family signaling (including IL-36A and 36G) and skin remodeling, MG-II (23 genes) with negative immune regulation (including IL-34 and 37) and skin architecture, and MG-III (17 genes) with B lymphocyte immunity. Sample clusters differed in terms of disease severity (p = .02) and S. aureus (SA) colonization (p = .02). Cluster 1 contained the most severe AD, highest SA colonization, and overexpressed MG-I. Cluster 2 was characterized by less severe AD, low SA colonization, and high MG-II expression. Cluster 3 included mild AD, mild SA colonization, and mild expression of all MGs. Cluster 4 had the same clinical features as cluster 3 but had hyper-expression of MG-III. Last, we successfully validated our method and results in an independent cohort. CONCLUSION: Our study revealed unrecognized AD endotypes with specific underlying biological pathways, highlighting novel pathophysiological mechanisms. These data could provide new insights into personalized treatment strategies.

Human inflammatory dendritic cells induce Th17 cell differentiation.

Dendritic cells (DCs) are critical regulators of immune responses. Under noninflammatory conditions, several human DC subsets have been identified. Little is known, however, about the human DC compartment under inflammatory conditions. Here, we characterize a DC population found in human inflammatory fluids that displayed a phenotype distinct from macrophages from the same fluids and from steady-state lymphoid organ and blood DCs. Transcriptome analysis showed that they correspond to a distinct DC subset and share gene signatures with in vitro monocyte-derived DCs. Moreover, human inflammatory DCs, but not inflammatory macrophages, stimulated autologous memory CD4(+) T cells to produce interleukin-17 and induce T helper 17 (Th17) cell differentiation from naive CD4(+) T cells through the selective secretion of Th17 cell-polarizing cytokines. We conclude that inflammatory DCs represent a distinct human DC subset and propose that they are derived from monocytes and are involved in the induction and maintenance of Th17 cell responses.

TLR1/2 orchestrate human plasmacytoid predendritic cell response to gram+ bacteria.

Gram+ infections are worldwide life-threatening diseases in which the pathological role of type I interferon (IFN) has been highlighted. Plasmacytoid predendritic cells (pDCs) produce high amounts of type I IFN following viral sensing. Despite studies suggesting that pDCs respond to bacteria, the mechanisms underlying bacterial sensing in pDCs are unknown. We show here that human primary pDCs express toll-like receptor 1 (TLR1) and 2 (TLR2) and respond to bacterial lipoproteins. We demonstrated that pDCs differentially respond to gram+ bacteria through the TLR1/2 pathway. Notably, up-regulation of costimulatory molecules and pro-inflammatory cytokines was TLR1 dependent, whereas type I IFN secretion was TLR2 dependent. Mechanistically, we demonstrated that these differences relied on diverse signaling pathways activated by TLR1/2. MAPK and NF-κB pathways were engaged by TLR1, whereas the Phosphoinositide 3-kinase (PI3K) pathway was activated by TLR2. This dichotomy was reflected in a different role of TLR2 and TLR1 in pDC priming of naïve cluster of differentiation 4+ (CD4+) T cells, and T helper (Th) cell differentiation. This work provides the rationale to explore and target pDCs in bacterial infection.

Multiobjective semisupervised learning with a right-censored endpoint adapted to the multiple imputation framework.

Semisupervised learning aims to use additional knowledge in the search for data structure. In clinical applications, including predictive information in the construction of a data-driven classification is of major importance. This work was motivated by a study that aimed to identify different patterns of immune parameters that would be associated with relapse-free survival in a cohort of breast cancer patients. Supervised and unsupervised objectives can be concomitantly optimized using multiobjective optimization. We propose such a procedure that addresses two challenges in the semisupervised approach, that is, missing data and additional knowledge based on survival time. The former was handled by using multiple imputation and consensus clustering. Survival information was incorporated in the supervised objective through the estimation of a cross-validation error of a Cox regression. A simulation study was performed to assess the performance of the proposed procedure. On complete datasets, the performances were compared to those of an existing modified multiobjective semisupervised learning method. The added value of including the survival data in the learning process was assessed by comparing the procedure to unsupervised learning. The proposed procedure showed better performance than the existing method, notably in the selection of the number of clusters. On incomplete datasets, the procedure showed little sensitivity to most of its parameters, even though a high number of imputations and partition initialization seeds improved the performance. The performance was degraded with a high proportion of missing data (40%) and with more ambiguous data structures. Simulation results and application on real data support the conclusion that our procedure enables the construction of a classification associated with a right-censored endpoint on a possibly incomplete dataset.

Plasmacytoid pre-dendritic cells (pDC): from molecular pathways to function and disease association.

Plasmacytoid pre-dendritic cells (pDC) are a specialized DC population with a great potential to produce large amounts of type I interferon (IFN). pDC are involved in the initiation of antiviral immune responses through their interaction with innate and adaptive immune cell populations. In a context-dependent manner, pDC activation can induce their differentiation into mature DC able to induce both T cell activation or tolerance. In this review, we described pDC functions during immune responses and their implication in the clearance or pathogenicity of human diseases during infection, autoimmunity, allergy and cancer. We discuss recent advances in the field of pDC biology and their implication for future studies.