Biology subjects

BARBRY, P.

Publications and source records attributed to BARBRY, P..

7 recordsLinked to original sources

bioRxiv2026

Imaging-based spatial isoform transcriptomics resolves transcript diversity at cellular and subcellular resolution

Spatial transcriptomics has transformed our understanding of tissue organization, yet current high-throughput approaches remain largely confined to a gene-centric paradigm, precluding systematic characterization of spatially resolved transcript isoform diversity. Here, we introduce imaging-based spatial isoform transcriptomics (iSiT), a framework enabling simultaneous large-scale gene expression and targeted transcript isoform profiling on the commercial Xenium platform. iSiT resolves alternative splicing events that alter internal transcript structure at cellular and subcellular resolution using a single junction-specific probe per target. To facilitate broader adoption, we developed ProbeHarbour, an open-source toolkit for the design, characterization and management of isoform-specific probes. We demonstrate iSiT in the postnatal mouse brain by combining the Prime 5K gene-level panel with isoform-specific detection of 45 synaptogenesis-associated genes. By extending spatial transcriptomics beyond gene-level measurements, iSiT provides a scalable and readily accessible framework for investigating spatial isoform regulation in development and disease.

neuroscience↗

bioRxiv2026

A distinct signature of interferon-stimulated genes linked to cross-protection against secondary viral infections in primary bronchial epithelial cells

Respiratory viral infections, such as those caused by rhinovirus, adenovirus, influenza, respiratory syncytial virus (RSV), and SARS-CoV-2, represent a major global health challenge. Despite extensive research, effective and specific antiviral treatments for these infections are still lacking, with patient care often limited to symptomatic relief. The SARS-CoV-2 pandemic, alongside the emergence of avian flu H1N1 and Nipah virus, has underscored the critical role of the respiratory tract as a critical viral target. Respiratory viral infections exhibit marked variability in infectivity and disease severity across different age groups. Epidemiological and cell-based evidence highlights distinct impacts on pediatric and adult populations. For instance, the COVID-19 disproportionately affected the elderly, while viruses like rhinovirus and adenovirus often cause severe morbidity in children. Additionally, clinical studies indicate that a primary respiratory infection can provide transient protection against subsequent infections by the same or different respiratory viruses. In this study, we utilized a differentiated bronchial epithelial (BE) model derived from pediatric and adult donors to assess age-dependent differences under resting conditions and during viral infections. We investigated how donor age influences infection susceptibility and viral spread within the BE, focusing on the transcriptional response to rhinovirus types A and C, and adenovirus type 5. Importantly, we demonstrate that prior viral infection confers protection against subsequent infections, regardless of donor age or the initial virus type. This cross-protection is driven by interferon signaling, leading to the expression of a narrow and specific set of interferon-stimulated genes (ISGs) in both infected and bystander cells. Notably, IFI44L shows the strongest correlation with the level of cross protection and that its overexpression alone significantly reduces viral infection of BE. These findings suggest a distinctive, interferon-driven innate immune response profile in the BE, offering critical insights for the development of new therapeutic strategies against respiratory viral infections.

microbiology↗

bioRxiv2026

Allos: an integrated Python toolkit for isoform-level single-cell and spatial in-situ transcriptomics

Single-cell RNA sequencing and spatial transcriptomics have transformed our understanding of the transcriptional landscape by enabling high-resolution profiling of gene expression. Yet most experimental pipelines and their associated analysis frameworks collapse transcript diversity into gene-level counts, obscuring alternative splicing and isoform usage. The increasing ability of long-read sequencing to recover full-length transcripts from single cells and spatially barcoded tissues has created a pressing need for computational frameworks to support the storage, analysis, and visualisation of isoform-resolved data. Existing tools for isoform and splicing analysis either specialise in bulk, single-cell, or spatial RNA-seq assays in isolation and remain fragmented across languages and data models, limiting interoperability and hindering widespread adoption. We present Allos, a Python framework for isoform-level single-cell and spatial transcriptomics analysis. Built on the AnnData data model, Allos natively represents transcript-level quantification and integrates directly with GTF/GFF and FASTA annotations. Allos enables differential isoform usage screening, multi-panel visualisation, structural transcript interpretation, and protein-level analysis across bulk, single-cell, and spatial assays from both long- and short-read sequencing. Its modular design and scverse compatibility allow isoform-resolved analyses to run alongside established gene-level workflows, linking transcript-level screening with structure-aware visualisation and downstream interpretation. Allos is open-source and available at https://github.com/cobioda/allos, with comprehensive documentation and tutorials provided online.

bioinformatics↗

bioRxiv2024

scMusketeers: Addressing imbalanced cell type annotation and batch effect reduction with a modular autoencoder

The growing number of single-cell gene expression atlases available offers a conceptual framework for improving our understanding of physio-pathological processes. To take full advantage of this revolution, data integration and cell annotation strategies need to be improved, in particular to better detect rare cell types and by better controlling batch effects in experiments. scMusketeers is a deep learning model that optimises the representation of latent data and solves both challenges. scMusketeers features three modules: (1) an autoencoder for noise and dimensionality reductions; (2) a focal loss classifier to enhance rare cell type predictions; and (3) an adversarial domain adaptation (DANN) module for batch effect correction. Benchmarking against state-of-the-art tools, including the UCE foundation model, showed that scMusketeers performs on par or better, particularly in identifying rare cell types. It also allows to transfer cell labels from single-cell RNA sequencing to spatial transcriptomics. With its modular and adaptable design, scMusketeers offers a versatile framework that can be generalized to other large-scale biological projects requiring deep learning approaches, establishing itself as a valuable tool for single-cell data integration and analysis.

bioinformatics↗

bioRxiv2023

Lung injury shifts pulmonary capillary endothelial cells towards regeneration-associated Lrg1+ subpopulations with delayed dynamics in aged mice

Aging increases the risk of developing fibrotic diseases by hampering tissue regeneration after injury. Using longitudinal single-cell RNA-seq and spatial transcriptomics, here we compare the transcriptome of bleomycin-induced fibrotic lungs of young and aged mice, at 3 time points corresponding to the peak of fibrosis, regeneration and resolution. We find that lung injury shifts the transcriptomic profiles of three pulmonary capillary endothelial cells (PCEC) subpopulations. The associated signatures are linked to pro-angiogenic signaling with strong Lrg1 expression and do not progress similarly throughout the resolution process between young and old animals. Moreover, part of this set of resolution-associated markers is also detected in PCEC from samples of patients with idiopathic pulmonary fibrosis. Finally, we find that aging also alters the transcriptome of PCEC which display typical pro-fibrotic and pro-inflammatory features. We propose that age-associated alterations in specific PCEC subpopulations may interfere with the process of lung progenitor differentiation, thus contributing to the persistent fibrotic process typical of human pathology.

pathology↗

bioRxiv2019

A single-cell atlas of the human healthy airways

RationaleThe respiratory tract constitutes an elaborated line of defense based on a unique cellular ecosystem. Single-cell profiling methods enable the investigation of cell population distributions and transcriptional changes along the airways. MethodsWe have explored cellular heterogeneity of the human airway epithelium in 10 healthy living volunteers by single-cell RNA profiling. 77,969 cells were collected by bronchoscopy at 35 distinct locations, from the nose to the 12th division of the airway tree. ResultsThe resulting atlas is composed of a high percentage of epithelial cells (89.1%), but also immune (6.2%) and stromal (4.7%) cells with peculiar cellular proportions in different sites of the airways. It reveals differential gene expression between identical cell types (suprabasal, secretory, and multiciliated cells) from the nose (MUC4, PI3, SIX3) and tracheobronchial (SCGB1A1, TFF3) airways. By contrast, cell-type specific gene expression was stable across all tracheobronchial samples. Our atlas improves the description of ionocytes, pulmonary neuro-endocrine (PNEC) and brush cells, which are likely derived from a common population of precursor cells. We also report a population of KRT13 positive cells with a high percentage of dividing cells which are reminiscent of "hillock" cells previously described in mouse. ConclusionsRobust characterization of this unprecedented large single-cell cohort establishes an important resource for future investigations. The precise description of the continuum existing from nasal epithelium to successive divisions of lung airways and the stable gene expression profile of these regions better defines conditions under which relevant tracheobronchial proxies of human respiratory diseases can be developed.

genomics↗

bioRxiv2019

High throughput, error corrected Nanopore single cell transcriptome sequencing

Droplet-based high throughput single cell isolation techniques tremendously boosted the throughput of single cell transcriptome profiling experiments. However, those approaches only allow analysis of one extremity of the transcript after short read sequencing. We introduce an approach that combines Oxford Nanopore sequencing with unique molecular identifiers to obtain error corrected full length sequence information with the 10xGenomics single cell isolation system. This allows to examine differential RNA splicing and RNA editing at a single cell level.

genomics↗