Nicolas Dias

The paper uses single-cell RNA sequencing to analyze mutant phenotypes of epigenetic regulators in mouse embryos, revealing how these regulators partition the genome based on developmental and evolutionary constraints.

The paper introduces MeRN, a graph-guided variational autoencoder for learning single-cell metabolic states from transcriptomes, and identifies metabolic drivers in various biological contexts.

This study uses a deep-learning model to analyze scRNA-seq data and identifies how folate deficiency disrupts metabolic transitions in developing mouse neural ectoderm, leading to morphological defects.

The paper identifies a rare secretory cell type in the intestine, CHEs, and shows that a second wave of Notch signaling is required for their specification.