Guglielmo Roma

The paper introduces FLASH-Seq, a new single-cell RNA-seq method that improves sensitivity and reduces processing time compared to existing protocols.

This study shows that reduced TREM2 proteolytic cleavage in mice increases microglial heterogeneity and accelerates Abeta pathology in Alzheimer's disease.

The paper develops scAR, a machine learning model to remove ambient noise from single-cell omics data across multiple technologies, showing high denoising accuracy.

APSiC identifies novel cancer genes and non-genetic drivers from perturbation screens, revealing context-dependent oncogenic mechanisms and functional roles in specific cancers.

This study compares cell-type development and transcriptomes in human retinal organoids and adult retinas, finding convergence in transcriptomes and cell-type-specific disease gene expression.

The paper identifies age-related gene expression patterns in multiple rat tissues, revealing early, mid, late, and linear transcriptional changes and their potential pharmacological targets.

The paper benchmarks scRNA-seq methods and introduces CellSIUS, a tool that improves detection of rare cell populations and identifies their functional signatures in human stem cell differentiation.