Laurent Gatto

This study identifies mitochondrial dysfunction as a key feature of brain senescence in telomerase-deficient mice, linking telomere attrition to impaired mitochondrial function and region-specific neuronal vulnerability.

This study shows that adipocyte beta3-adrenergic receptor activation protects against high-fat diet-induced cardiac damage by promoting beige fat formation and adipose-cardiac communication.

The paper develops CTexploreR to analyze cancer-germline genes, showing X-linked genes are DNA methylation dependent and exhibit maternal imprinting in early embryos.

scplainer provides a standardized approach using linear models to analyze single-cell proteomics data, enabling variance analysis, differential abundance, and integration across experiments.

The paper introduces demux_bench, a reproducible and portable pipeline for benchmarking genetic demultiplexing methods in scRNA-seq using Nextflow and Apptainer.

MDSvis provides an interactive Shiny app for visualizing MDS projections, enabling users to customize and explore high-dimensional data without programming.

CytoMDS provides a method for visual quality control of cytometry data by representing samples in low dimensions to detect batch effects and outliers.

This study uses spatial transcriptomics to analyze two gallbladder cancer patients, showing distinct progression paths and the role of SEMA4A repression in tumor development.

demuxSNP improves demultiplexing in scRNA-seq by combining cell hashing with SNP data, outperforming existing methods on low-quality data.

This study introduces a method combining plasma prefractionation and DIA to improve proteome coverage and depth, enabling robust biomarker identification in a COVID-19 cohort.

The paper introduces two R packages for building and visualizing automated flow cytometry data pre-processing pipelines and demonstrates their use through benchmarking and visualization.

The paper provides a Bioconductor package with curated landmark datasets for single-cell multimodal data, enabling easy access and analysis of seven different technologies.

The paper identifies tissue-enriched ligands involved in epithelial-endothelial crosstalk during pancreas development using scRNA-seq and computational interactomics.

ProtFinder predicts multiple subcellular locations of proteins using protein interaction networks and biological priors, achieving high accuracy on held-out data.

The paper replicates a landmark single-cell proteomics study to highlight computational challenges in data analysis and proposes a standardized workflow.

The paper demonstrates how the VariantExperiment class addresses the need for efficient storage, handling, and sharing of SARS-CoV-2 genomic data and metadata as highlighted by WHO.

The paper introduces BANDLE, a Bayesian method to infer differential subcellular localisation from spatial proteomics data, showing improved accuracy and novel biological insights.

This paper introduces a semi-supervised Bayesian method for protein sub-cellular localisation and novelty detection, identifying new compartments in proteomic data.

The paper introduces version 2 of the MSnbase R/Bioconductor package, enhancing its ability to handle large mass spectrometry datasets efficiently.

This study evaluates the sub-cellular resolution of 29 spatial proteomics experiments using QSep to provide a quantitative assessment of their ability to resolve sub-cellular niches.

This paper introduces a Bayesian mixture model for spatial proteomics to probabilistically assign proteins to sub-cellular locations and quantify uncertainty in classification.

The paper introduces LOPIT-DC, a simplified spatial proteomics method, and compares it with hyperLOPIT, highlighting suborganellar resolution and isoform-specific niches in human cells.