Integrating single-cell imaging and RNA sequencing datasets links differentiation and morphogenetic dynamics of human pancreatic endocrine progenitors.

Basic helix-loop-helix genes, particularly proneural genes, are well-described triggers of cell differentiation, yet information on their dynamics is limited, notably in human development. Here, we focus on Neurogenin 3 (NEUROG3), which is crucial for pancreatic endocrine lineage initiation. By monitoring both NEUROG3 gene expression and protein in single cells using a knockin dual reporter in 2D and 3D models of human pancreas development, we show an approximately 2-fold slower expression of human NEUROG3 than that of the mouse.

DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic features.

Despite the increasing use of genomic sequencing in clinical practice, the interpretation of rare genetic variants remains challenging even in well-studied disease genes, resulting in many patients with Variants of Uncertain Significance (VUSs). Computational Variant Effect Predictors (VEPs) provide valuable evidence in variant assessment, but they are prone to misclassifying benign variants, contributing to false positives. Here, we develop Deciphering Mutations in Actionable Genes (DeMAG), a supervised classifier for missense variants trained using extensive diagnostic data available in 59 actionable disease genes (American College of Medical Genetics and Genomics Secondary Findings v2.