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).

Large-scale analysis of cell-cell communication reveals angiogenin-dependent tumor progression in clear cell renal cell carcinoma.

Cellular crosstalk in the tumor microenvironment (TME) is still largely uncharacterized, while it plays an essential role in shaping immunosuppression or anti-tumor response. Large-scale analyses are needed to better decipher cell-cell communication in cancer. In this work, we used original and publicly available single-cell RNA sequencing (scRNAseq) data to characterize in-depth the communication networks in human clear cell renal cell carcinoma (ccRCC). We identified 50 putative communication channels specifically used by cancer cells to interact with other cells, including two novel angiogenin-mediated interactions. Expression of angiogenin and its receptors was validated at the protein level in primary ccRCC. Mechanistically, angiogenin enhanced ccRCC cell line proliferation and down-regulated secretion of IL-6, IL-8, and MCP-1 proinflammatory molecules. This study provides novel biological insights into molecular mechanisms of ccRCC, and suggests angiogenin and its receptors as potential therapeutic targets in clear cell renal cancer.

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.

Meta-Analysis of Human Cancer Single-Cell RNA-Seq Datasets Using the IMMUcan Database.

The development of single-cell RNA sequencing (scRNA-seq) technologies has greatly contributed to deciphering the tumor microenvironment (TME). An enormous amount of independent scRNA-seq studies have been published representing a valuable resource that provides opportunities for meta-analysis studies. However, the massive amount of biological information, the marked heterogeneity and variability between studies, and the technical challenges in processing heterogeneous datasets create major bottlenecks for the full exploitation of scRNA-seq data. We have developed IMMUcan scDB (https://immucanscdb.vital-it.ch), a fully integrated scRNA-seq database exclusively dedicated to human cancer and accessible to nonspecialists. IMMUcan scDB encompasses 144 datasets on 56 different cancer types, annotated in 50 fields containing precise clinical, technological, and biological information. A data processing pipeline was developed and organized in four steps: (i) data collection; (ii) data processing (quality control and sample integration); (iii) supervised cell annotation with a cell ontology classifier of the TME; and (iv) interface to analyze TME in a cancer type-specific or global manner. This framework was used to explore datasets across tumor locations in a gene-centric (CXCL13) and cell-centric (B cells) manner as well as to conduct meta-analysis studies such as ranking immune cell types and genes correlated to malignant transformation. This integrated, freely accessible, and user-friendly resource represents an unprecedented level of detailed annotation, offering vast possibilities for downstream exploitation of human cancer scRNA-seq data for discovery and validation studies. SIGNIFICANCE: The IMMUcan scDB database is an accessible supportive tool to analyze and decipher tumor-associated single-cell RNA sequencing data, allowing researchers to maximally use this data to provide new insights into cancer biology.

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.

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.

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.

Lipid-Associated Macrophages Are Induced by Cancer-Associated Fibroblasts and Mediate Immune Suppression in Breast Cancer.

Tumor-associated macrophages (TAM) play a detrimental role in triple-negative breast cancer (TNBC). In-depth analysis of TAM characteristics and interactions with stromal cells, such as cancer-associated fibroblast (CAF), could provide important biological and therapeutic insights. Here we identify at the single-cell level a monocyte-derived STAB1+TREM2high lipid-associated macrophage (LAM) subpopulation with immune suppressive capacities that is expanded in patients resistant to immune checkpoint blockade (ICB). Genetic depletion of this LAM subset in mice suppressed TNBC tumor growth. Flow cytometry and bulk RNA sequencing data demonstrated that coculture with TNBC-derived CAFs led to reprogramming of blood monocytes towards immune suppressive STAB1+TREM2high LAMs, which inhibit T-cell activation and proliferation. Cell-to-cell interaction modeling and assays in vitro demonstrated the role of the inflammatory CXCL12-CXCR4 axis in CAF-myeloid cell cross-talk and recruitment of monocytes in tumor sites. Altogether, these data suggest an inflammation model whereby monocytes recruited to the tumor via the CAF-driven CXCL12-CXCR4 axis acquire protumorigenic LAM capacities to support an immunosuppressive microenvironment. SIGNIFICANCE: This work identifies a novel lipid-associated macrophage subpopulation with immune suppressive functions, offering new leads for therapeutic interventions in triple-negative breast cancer.

Context-Dependent Effects Explain Divergent Prognostic Roles of Tregs in Cancer.

Assessing cancer prognosis is a challenging task, given the heterogeneity of the disease. Multiple features (clinical, environmental, genetic) have been used for such assessments. The tumor immune microenvironment (TIME) is a key feature, and describing the impact of its many components on cancer prognosis is an active field of research. The complexity of the tumor microenvironment context makes it difficult to use the TIME to assess prognosis, as demonstrated by the example of regulatory T cells (Tregs). The effect of Tregs on prognosis is ambiguous, with different studies considering them to be negative, positive or neutral. We focused on five different cancer types (breast, colorectal, gastric, lung and ovarian). We clarified the definition of Tregs and their utility for assessing cancer prognosis by taking the context into account via the following parameters: the Treg subset, the anatomical location of these cells, and the neighboring cells. With a meta-analysis on these three parameters, we were able to clarify the prognostic role of Tregs. We found that CD45RO+ Tregs had a reproducible negative effect on prognosis across cancer types, and we gained insight into the contributions of the anatomical location of Tregs and of their neighboring cells on their prognostic value. Our results suggest that Tregs play a similar prognostic role in all cancer types. We also establish guidelines for improving the design of future studies addressing the pathophysiological role of Tregs in cancer.

Influence of FLG loss-of-function mutations in host-microbe interactions during atopic skin inflammation.

BACKGROUND: Loss-of-function mutations in the filaggrin (FLG) gene directly alter skin barrier function and critically influence atopic inflammation. While skin barrier dysfunction, Th2-associated inflammation and bacterial dysbiosis are well-known characteristics of atopic dermatitis (AD), the mechanisms interconnecting genotype, transcriptome and microbiome remain largely elusive. OBJECTIVE: In-depth analysis of FLG genotype-associated skin gene expression alterations and host-microbe interactions in AD. METHODS: Multi-omics characterization of a cohort of AD patients carrying heterozygous loss-of-function mutations in the FLG gene (ADMut) (n = 15), along with matched wild-type (ADWt) patients and healthy controls. Detailed clinical characterization, microarray gene expression and 16 S rRNA-based microbial marker gene data were generated and analyzed. RESULTS: In the context of filaggrin dysfunction, the transcriptome was characterized by dysregulation of barrier function and water homeostasis, while the lesional skin of ADWt demonstrated the specific upregulation of pro-inflammatory cytokines and T-cell proliferation. S. aureus dominated the microbiome in both patient groups, however, shifting microbial communities could be observed when comparing healthy with non-lesional ADWt or ADMut skin, offering the opportunity to identify microbe-associated transcriptomic signatures. Moreover, an AD core signature with 28 genes, including CCL13, CCL18, BTC, SCIN, RAB31 and PCLO was identified. CONCLUSIONS: Our integrative approach provides molecular insights for the concept that FLG loss-of-function mutations are a genetic shortcut to atopic inflammation and unravels the complex interplay between genotype, transcriptome and microbiome in the human holobiont.

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.

PD-L1 and ICOSL discriminate human Secretory and Helper dendritic cells in cancer, allergy and autoimmunity.

Dendritic cells (DC) are traditionally classified according to their ontogeny and their ability to induce T cell response to antigens, however, the phenotypic and functional state of these cells in cancer does not necessarily align to the conventional categories. Here we show, by using 16 different stimuli in vitro that activated DCs in human blood are phenotypically and functionally dichotomous, and pure cultures of type 2 conventional dendritic cells acquire these states (termed Secretory and Helper) upon appropriate stimuli. PD-L1highICOSLlow Secretory DCs produce large amounts of inflammatory cytokines and chemokines but induce very low levels of T helper (Th) cytokines following co-culturing with T cells. Conversely, PD-L1lowICOSLhigh Helper DCs produce low levels of secreted factors but induce high levels and a broad range of Th cytokines. Secretory DCs bear a single-cell transcriptomic signature indicative of mature migratory LAMP3+ DCs associated with cancer and inflammation. Secretory DCs are linked to good prognosis in head and neck squamous cell carcinoma, and to response to checkpoint blockade in Melanoma. Hence, the functional dichotomy of DCs we describe has both fundamental and translational implications in inflammation and immunotherapy.

Characterization and functional interrogation of the SARS-CoV-2 RNA interactome.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the COVID-19 pandemic, which has led to a devastating global health crisis. The emergence of variants that escape neutralizing responses emphasizes the urgent need to deepen our understanding of SARS-CoV-2 biology. Using a comprehensive identification of RNA-binding proteins (RBPs) by mass spectrometry (ChIRP-MS) approach, we identify 107 high-confidence cellular factors that interact with the SARS-CoV-2 genome during infection. By systematically knocking down their expression in human lung epithelial cells, we find that the majority of the identified RBPs are SARS-CoV-2 proviral factors. In particular, we show that HNRNPA2B1, ILF3, QKI, and SFPQ interact with the SARS-CoV-2 genome and promote viral RNA amplification. Our study provides valuable resources for future investigations into the mechanisms of SARS-CoV-2 replication and the identification of host-centered antiviral therapies.

Negative Relationship between Post-Treatment Stromal Tumor-Infiltrating Lymphocyte (TIL) and Survival in Triple-Negative Breast Cancer Patients Treated with Dose-Dense Dose-Intense NeoAdjuvant Chemotherapy.

Background: Patients with triple-negative breast cancers (TNBC) have a poor prognosis unless a pathological complete response (pCR) is achieved after neoadjuvant chemotherapy (NAC). Few studies have analyzed changes in TIL levels following dose-dense dose-intense (dd-di) NAC. Patients and methods: From 2009 to 2018, 117 patients with TNBC received dd-di NAC at our institution. We aimed to identify factors associated with pre- and post-NAC TIL levels, and oncological outcomes relapse-free survival (RFS), and overall survival (OS). Results: Median pre-NAC and post-NAC TIL levels were 15% and 3%, respectively. Change in TIL levels with treatment was significantly correlated with metabolic response (SUV) and pCR. High post-NAC TIL levels were associated with a weak metabolic response after two cycles of NAC, with the presence of residual disease and nodal involvement at NAC completion. In multivariate analyses, high post-NAC TIL levels independently predicted poor RFS and poor OS (HR = 1.4 per 10% increment, 95%CI (1.1; 1.9) p = 0.014 and HR = 1.8 per 10% increment 95%CI (1.3−2.3), p < 0.0001, respectively). Conclusion: Our results suggest that TNBC patients with TIL enrichment after NAC are at higher risk of relapse. These patients are potential candidates for adjuvant treatment, such as immunotherapy, in clinical trials.

A multivariate modeling framework to quantify immune checkpoint context-dependent stimulation on T cells.

Cells receive, and adjust to, various stimuli, which function as part of complex microenvironments forming their "context". The possibility that a given context impacts the response to a given stimulus defines "context-dependency" and it explains large parts of the functional variability of physiopathological and pharmacological stimuli. Currently, there is no framework to analyze and quantify context-dependency over multiple contexts and cellular response outputs. We established an experimental system including a stimulus of interest, applied to an immune cell type in several contexts. We studied the function of OX40 ligand (OX40L) on T helper (Th) cell differentiation, in 4 molecular (Th0, Th1, Th2, and Th17) and 11 dendritic cell (DC) contexts (monocyte-derived DC and cDC2 conditions). We measured 17 Th output cytokines in 302 observations, and developed a statistical modeling strategy to quantify OX40L context-dependency. This revealed highly variable context-dependency, depending on the output cytokine and context type itself. Among molecular contexts, Th2 was the most influential on OX40L function. Among DC contexts, the DC type rather than the activating stimuli was dominant in controlling OX40L context-dependency. This work mathematically formalizes the complex determinants of OX40L functionality, and provides a unique framework to decipher and quantify the context-dependent variability of any biomolecule or drug function.

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.

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.

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.

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.