Biology subjects

Sang-aram, C.

Publications and source records attributed to Sang-aram, C..

3 recordsLinked to original sources

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↗

bioRxiv2025

anndataR improves interoperability between R and Python in single-cell transcriptomics

SummaryMany single-cell transcriptomics datasets are stored in the HDF5-backed AnnData (H5AD) file format, as popularised by the Python scverse ecosystem. However, accessing these datasets from R, allowing users to take advantage of the strengths of each language, can be difficult. anndataR facilitates this access by allowing users to natively read and write H5AD files in R, convert them to and from SingleCellExperiment or Seurat objects, or even work with the resulting R AnnData object directly. We perform rigorous testing to ensure compatibility between Python-written and R-written H5AD files, guaranteeing long-term interoperability between languages. AvailabilityanndataRs source code is available on GitHub at scverse/anndataR under the MIT license. It is compatible with R version 4.5, has been archived at 10.5281/zenodo.18775712 and included within Bioconductor: 10.18129/B9.bioc.anndataR. Installation instructions and tutorials can be found in the online documentation at anndatar.scverse.org. Issues can be reported at the GitHub repository. Code to reproduce the analyses performed can be found on GitHub at LouiseDck/anndataR-paper, archived at 10.5281/zenodo.18792241. Contactrobrecht.cannoodt@gmail.com

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↗