Pascal Barbry

The paper introduces iSiT, an imaging-based spatial isoform transcriptomics method, and demonstrates its application in the mouse brain to study transcript isoform diversity at cellular and subcellular resolution.

Allos provides a Python framework for isoform-level analysis of single-cell and spatial transcriptomics data, integrating with existing tools and enabling differential isoform usage screening and visualization.

Cyclin O is necessary for multiciliated cells to enter their differentiation cell cycle variant and allows the massive amplification of centrioles, which serve as basal bodies for cilia nucleation.

scMusketeers improves cell type annotation and reduces batch effects in single-cell data using a modular autoencoder with focal loss and adversarial domain adaptation.

The paper examines transcriptional similarities between two neural lineages during ascidian embryogenesis and finds they cluster together despite limited shared transcriptional control.

This study evaluates how different culture media affect the differentiation and proliferation of human airway epithelial cells in vitro using single-cell RNA sequencing and imaging.

The paper identifies a new cell cycle variant in multiciliated cells that enables centriole amplification without DNA replication or mitosis.

The paper develops a Sparse supervised autoencoder to detect subtle transcriptomic changes from CRISPRi screens of lncRNAs in lung cancer cells under hypoxia.

The paper introduces SiT, a method to characterize spatial isoform variation and sequence heterogeneity in tissue sections, revealing regional isoform switching and RNA editing in mouse brain.

The paper shows that cardiac macrophages in pigs with right ventricular failure drive cardiomyocyte de-differentiation and regeneration through epimorphosis.

This paper introduces a method combining Oxford Nanopore sequencing with unique molecular identifiers to enable error-corrected full-length transcript analysis in single cells.

The paper constructs a single-cell atlas of the human healthy airways using RNA sequencing data from 77,969 cells across 35 locations, revealing cell-type-specific gene expression patterns and cellular heterogeneity.