E. de Rinaldis

DiSiR: a software framework to identify ligand-receptor interactions at subunit level from single-cell RNA-sequencing data

DiSiR identifies ligand-receptor interactions at the subunit level from scRNA-seq data, outperforming existing methods in inferring disease-related pathways.

Cell type classification and discovery across diseases, technologies and tissues reveals conserved gene signatures and enables standardized single-cell readouts

The paper introduces Signac, a machine learning method for classifying single-cell RNA-sequencing data across diseases and tissues, and identifies drug targets in rheumatoid arthritis.

The Th1 cell regulatory circuitry is largely conserved between human and mouse

The study compares T-bet target gene regulation between human and mouse, finding most genes are conserved despite species-specific pathogen exposure.

An optimized workflow for single-cell transcriptomics and repertoire profiling of purified lymphocytes from clinical samples.

The paper optimizes workflows for single-cell transcriptomics and repertoire profiling of purified lymphocytes from clinical samples, identifying best practices for viability, yield, and data quality.

Interplay between the human gut microbiome and host metabolism

This study examines the gut microbiome's metabolic interactions with the host, finding that functional pathways, not just species, significantly influence metabolites in blood and feces.