Biology subjects

Browaeys, R.

Publications and source records attributed to Browaeys, R..

5 recordsLinked to original sources

bioRxiv2026

Beyond a binary view of cystic fibrosis: systemic immunity and inflammation across the spectrum of CFTR dysfunction

Cystic fibrosis is traditionally framed as a dichotomy between affected individuals and clinically unaffected carriers, yet the systemic immune consequences across the spectrum of CFTR functionality remain incompletely defined. The advent of highly effective CFTR modulators now provides a unique momentum to examine whether partial restoration of CFTR function can influence systemic immunity. Using multimodal immune profiling, we constructed a single-cell atlas of circulating immune cells in people with cystic fibrosis (pwCF), healthy F508del carriers and non-carriers. In pwCF, systemic immunity was markedly altered following in vivo CFTR modulation with elexacaftor-tezacaftor-ivacaftor, with broad reductions in pro-inflammatory cytokines linked to improved clinical outcomes. Notably, healthy F508del carriers exhibited a CF-like immune signature characterised by low-grade systemic inflammation, including elevated IL-6, reduced mucosal-associated invariant T cells, and inflammatory monocyte features overlapping with pwCF. Together, these findings show that CFTR-related immune dysregulation extends beyond classical cystic fibrosis, challenging a strict dichotomy between health and disease.

immunology↗

bioRxiv2026

IL-1 instructs para-bronchial cuff fibroblasts to organize lung antibody secreting cell niches during continued antigen exposure

Secondary lymphoid organs (SLO) are prototypic sites of antibody production, yet mucosal sites also generate and maintain mucosal antibodies during continued inflammation. Using a murine model of house dust mite-induced airway inflammation, we show that prolonged allergen exposure induces tertiary lymphoid organs (TLO) within para-bronchial adventitial cuffs. Within these regions, IL-1 signaling instructs fibroblasts to form niches that recruit, retain, and sustain lung antibody-secreting cells (ASCs) through chemokine induction. These mucosal ASCs share immunoglobulin repertoires with TLO-derived germinal center B cells. Thus, continued allergen exposure reshapes the lung microenvironment by converting para-bronchial fibroblasts into IL-1-dependent supportive niches for non-IgE antibody production, revealing a fibroblast-mediated mechanism for local immune regulation in chronic inflammation. One sentence summaryContinued allergen exposure drives lung TLO to generate ASCs that home to IL1-instructed para-bronchial cuffs.

immunology↗

bioRxiv2023

MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data

Dysregulated cell-cell communication is a hallmark of many disease phenotypes. Due to recent advances in single-cell transcriptomics and computational approaches, it is now possible to study intercellular communication on a genome- and tissue-wide scale. However, most current cell-cell communication inference tools have limitations when analyzing data from multiple samples and conditions. Their main limitation is that they do not address inter-sample heterogeneity adequately, which could lead to false inference. This issue is crucial for analyzing human cohort scRNA-seq datasets, complicating the comparison between healthy and diseased subjects. Therefore, we developed MultiNicheNet (https://github.com/saeyslab/multinichenetr), a novel framework to better analyze cell-cell communication from multi-sample multi-condition single-cell transcriptomics data. The main goals of MultiNicheNet are inferring the differentially expressed and active ligand-receptor pairs between conditions of interest and predicting the putative downstream target genes of these pairs. To achieve this goal, MultiNicheNet applies the principles of state-of-the-art differential expression algorithms for multi-sample scRNA-seq data. As a result, users can analyze differential cell-cell communication while adequately addressing inter-sample heterogeneity, handling complex multifactorial experimental designs, and correcting for batch effects and covariates. Moreover, MultiNicheNet uses NicheNet-v2, our new and substantially improved version of NicheNets ligand-receptor network and ligand-target prior knowledge model. We applied MultiNicheNet to patient cohort data of several diseases (breast cancer, squamous cell carcinoma, multisystem inflammatory syndrome in children, and lung fibrosis). For these diseases, MultiNicheNet uncovered known and novel aberrant cell-cell signaling processes. We also demonstrated MultiNicheNets potential to perform non-trivial analysis tasks, such as studying between- and within-group differences in cell-cell communication dynamics in response to therapy. As a final example, we used MulitNicheNet to elucidate dysregulated intercellular signaling in idiopathic pulmonary fibrosis while correcting batch effects in integrated atlas data. Given the anticipated increase in multi-sample scRNA-seq datasets due to technological advancements and extensive atlas-building integration efforts, we expect that MultiNicheNet will be a valuable tool to uncover differences in cell-cell communication between healthy and diseased states.

bioinformatics↗

bioRxiv2023

Spotless: a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics

Spatial transcriptomics (ST) is an emerging field that aims to profile the transcriptome of a cell while keeping its spatial context. Although the resolution of non-targeted ST technologies has been rapidly improving in recent years, most commercial methods do not yet operate at single-cell resolution. To tackle this issue, computational methods such as deconvolution can be used to infer cell type proportions in each spot by learning cell type-specific expression profiles from reference single-cell RNA-sequencing (scRNA-seq) data. Here, we benchmarked the performance of 11 deconvolution methods using 63 silver standards, three gold standards, and two case studies on liver and melanoma tissues. The silver standards were generated using our novel simulation engine synthspot, where we used seven scRNA-seq datasets to create synthetic spots that followed one of nine different biological tissue patterns. The gold standards were generated using imaging-based ST technologies at single-cell resolution. We evaluated method performance based on the root-mean-squared error, area under the precision-recall curve, and Jensen-Shannon divergence. Our evaluation revealed that method performance significantly decreases in datasets with highly abundant or rare cell types. Moreover, we evaluated the stability of each method when using different reference datasets and found that having sufficient number of genes for each cell type is crucial for good performance. We conclude that while cell2location and RCTD are the top-performing methods, a simple off-the-shelf deconvolution method surprisingly outperforms almost half of the dedicated spatial deconvolution methods. Our freely available Nextflow pipeline allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).

bioinformatics↗

bioRxiv2021

Spatial proteogenomics reveals distinct and evolutionarily-conserved hepatic macrophage niches

The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here, we present a spatial proteogenomic atlas of the healthy human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and bile-duct macrophages. We also uncover the respective spatially-resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that bile-duct macrophages are induced by local lipid exposure, while Kupffer cells crucially depend on their crosstalk with hepatic stellate cells via the evolutionarily-conserved ALK1-BMP9/10 axis.

immunology↗