Biology subjects

Gubbels, L.

Publications and source records attributed to Gubbels, L..

4 recordsLinked to original sources

bioRxiv2026

Single-cell CRISPR activation screens in primary B cells discover gene regulatory mechanisms for hundreds of autoimmune risk loci.

Genome-wide association studies (GWAS) have discovered thousands of genetic variants linked to autoimmune disease, and yet the molecular pathways underlying autoimmunity have remained elusive. A key challenge is that >90% of identified GWAS risk loci are in non-coding genomic regions making it difficult to predict their relevance to disease. Here, we have curated fine-mapped non-coding risk variants from over 30 different autoimmune traits including common conditions such as systemic lupus erythematosus (SLE), Crohns disease, and multiple sclerosis, and reveal shared genetic signatures between diverse autoimmune diseases. We subsequently performed a high-throughput single-cell multi-omic CRISPR activation screen targeting 763 autoimmune risk loci in primary human B cells (a highly relevant cell type to autoimmune diseases) and discover 524 cis-regulatory target gene effects for 378 risk loci, with many risk loci regulating multiple gene targets. This Single Cell Analysis of Non-coding Distal Autoimmune Loci (SCANDAL) provides a powerful experimental resource linking non-coding risk loci to many disease-relevant genes, including lowly-expressed cytokines and transcription factors for which perturbation effects can be difficult to quantify with other CRISPR-based strategies. We reveal how increased transcriptional activity at one non-coding risk locus can drive transcription at other risk loci within the same regulatory landscape that may be relevant to understand genetic pleiotropy of autoimmune diseases. Finally, we quantified allele-specific effects on target gene expression with massive parallel reporter assays and prime editing to discover a gain-of-function variant associated with SLE that controls expression of the transcription factor REL/cREL which subsequently binds dozens of risk loci and target genes associated with different autoimmune diseases. Our study provides a valuable resource linking non-coding risk loci with their cis-regulatory target genes and advances our understanding of the shared genetic networks and mechanisms involved in autoimmunity.

genomics↗

bioRxiv2025

Macrophages Mediate Antiviral Immunity and Repair of Type 2 Alveolar Epithelial Cells in a Human Stem Cell Model

The lung alveoli are constantly exposed to inhaled pathogens and inorganic hazards, relying on robust defence mechanisms to maintain homeostasis. Alveolar macrophages and type 2 alveolar epithelial cells (AT2s) collaborate to orchestrate protection. Compromised defence can dysregulate immunity and repair, leading to acute and chronic respiratory diseases. To better understand these processes and drive therapeutic discovery, human model systems that capture key cell interactions are essential. Here, we develop the first induced pluripotent stem cell (iPSC)-derived platform that integrates AT2 cells and macrophages in an air-liquid interface culture. Coculture enhanced AT2-specific gene expression and lipid synthesis, while macrophages actively phagocytosed AT2-derived surfactant. iPSC-derived AT2s supported macrophage survival by producing M-CSF and coculture promoted an alveolar macrophage-like phenotype. Additionally, during respiratory infection macrophages played a crucial role in modulating proinflammatory signalling, enhancing antiviral immunity, and restricting viral replication. Furthermore, we identify a role for iPSC-derived macrophages in epithelial repair, with VEGF signalling to macrophages increasing epithelial permeability. We present an iPSC-derived air-interface platform to study AT2-macrophage interactions in homeostasis, infection, and repair, providing insights into their potential roles in the initiation and progression of respiratory diseases.

cell biology↗

bioRxiv2025

Antibody responses against bacterial glycans affinity mature and diversify in germinal centers.

Anti-carbohydrate antibodies (Abs) play crucial roles in pathogen control, but their generation remains poorly understood. By studying responses to Streptococcus pyogenes in humans, we reveal that the glycan-targeted response shifts from IgM towards IgG and IgA memory with age and antigen exposure across blood, spleen, and tonsils. Both natural colonization and controlled human infection with S. pyogenes increased class-switched B cells, with evidence of within-clone switching. Glycan-specific B cells readily participated in germinal center (GC) responses and showed robust somatic hypermutation despite a molecular signature consistent with receiving reduced T cell help. We conclude that mucosal pathogen encounters elicit glycan responses that class-switch, evolve and diversify through the GC. These findings reveal how age and infection history can influence the quality, quantity, and isotype use of glycan-specific B cells, with implications for the design and schedule of glycan-containing vaccines.

immunology↗

bioRxiv2024

More cells, more doublets in highly multiplexed single-cell data

Withdrawal statementThe authors have withdrawn this manuscript. In our initial submission of this paper, we proposed a combinatoric model for the probability of finding doublets in sample-barcoded single-cell RNA-sequencing data. This model predicts a doublet rate higher than the rate included in the documentation for the 10X Flex protocol. This was motivated by our use of doublet-finding software, scDblFinder, on experimental data using the Flex protocol, which identified several times more doublets than we expected based on the documentation. Our original model produced better agreement with the results of scDblFinder in 9 of our own data sets as well as one public dataset made available by 10X. During revisions, however, we performed Monte Carlo simulations of doublet formation that were inconsistent with the results of our model, and much closer to the predictions from the documentation. This prompted us to reanalyse the assumptions of our model. A longer version of this withdrawal statement that explains in detail the errors in the model and includes updated figures is available at https://github.com/Oshlack/flex-doublets, but in brief, our initial assumptions over-predicted the doublet fraction, and our revised model is now consistent with both the Monte Carlo simulations and the predictions from the Flex documentation. We therefore speculate that the unexpectedly high doublet fractions observed in the experimental data might be due to some combination of experimental conditions and software performance. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please ontact the corresponding author.

bioinformatics↗