Kévin Lebrigand

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.

This study identifies AETA peptide as a novel contributor to synaptic dysfunction in Alzheimer's disease, particularly in females, through post-mortem analysis and a mouse model.

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.

The paper uses 3D spheroid models to show filopodia-like protrusions help breast cancer cells invade by widening basement membrane pores.

This study examines the effects of miR-210 locus deletion on cellular homeostasis in human and mouse cells, revealing deregulation of neighboring genes and unexpected lack of miR-210 target enrichment.

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 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 introduces aiFACS, a technique for real-time functional analysis and sorting of cells, and applies it to study interneurons in Fragile X syndrome.

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.

This study identifies a repair-supportive mesenchymal cell population critical for lung epithelial regeneration through single-cell RNA sequencing and lineage tracing.