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.

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).

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.

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.

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

Unlabelled: 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.

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.

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

Unlabelled: 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).

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.

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.

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.

Interplay between SMAD2 and STAT5A is a critical determinant of IL-17A/IL-17F differential expression.

Interleukins (IL)-17A and F are critical cytokines in anti-microbial immunity but also contribute to auto-immune pathologies. Recent evidence suggests that they may be differentially produced by T-helper (Th) cells, but the underlying mechanisms remain unknown. To address this question, we built a regulatory graph integrating all reported upstream regulators of IL-17A and F, completed by ChIP-seq data analyses.

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.

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.

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.

Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images.

Multiple sclerosis (MS) is one of the most common autoimmune diseases which is commonly diagnosed and monitored using magnetic resonance imaging (MRI) with a combination of clinical manifestations. The purpose of this review is to highlight the main applications of Machine Learning (ML) models and their performance in the MS field using MRI. We reviewed the articles of the last decade and grouped them based on the applications of ML in MS using MRI data into four categories: 1) Automated diagnosis of MS, 2) Prediction of MS disease progression, 3) Differentiation of MS stages, 4) Differentiation of MS from similar disorders.

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.

A computational workflow for the detection of candidate diagnostic biomarkers of Kawasaki disease using time-series gene expression data.

Unlike autoimmune diseases, there is no known constitutive and disease-defining biomarker for systemic autoinflammatory diseases (SAIDs). Kawasaki disease (KD) is one of the "undiagnosed" types of SAIDs whose pathogenic mechanism and gene mutation still remain unknown. To address this issue, we have developed a sequential computational workflow which clusters KD patients with similar gene expression profiles across the three different KD phases (Acute, Subacute and Convalescent) and utilizes the resulting clustermap to detect prominent genes that can be used as diagnostic biomarkers for KD.

Systems analysis of human T helper17 cell differentiation uncovers distinct time-regulated transcriptional modules.

T helper (Th) 17 cells protect from infections and are pathogenic in autoimmunity. While human Th17 cell differentiation has been defined, the global and stepwise transcriptional changes accompanying this process remain uncharacterized. Herein, by performing transcriptome analysis of human Th17 cells, we uncovered three time-regulated modules: early, involving exclusively "signaling pathways" genes; late, characterized by response to infections; and persistent, involving effector immune functions.