Michael R. Erdos

The paper identifies dysregulated pathways and candidate driver genes in skeletal muscle from individuals with type 2 diabetes using co-expression networks and single-nucleus RNA-seq data.

The paper uses donor-matched iPSC-derived FAPs to show that T2D GWAS signals are enriched in adipogenic FAP subtypes under high-insulin exposure.

This paper describes a scalable method to differentiate human pluripotent stem cells into hypothalamic arcuate neurons, which can model metabolic and reproductive disorders.

The paper identifies genetic factors influencing iPSC growth rates, finding rare variants in WDR54 and a polygenic background explaining most variance.

The paper examines how physical activity and insulin resistance relate to skeletal muscle cell type abundance and gene expression in humans, finding inverse associations with specific cell types and gene expression patterns.

The paper introduces a method to detect protein-DNA binding in single molecules using antibody-guided methylation, generating identifiable methylation patterns near binding sites.

The paper uses direct RNA nanopore sequencing to identify RNA modification changes in human pancreatic beta-cells after glucose stimulation, finding 1,697 differentially modified sites linked to type 2 diabetes genes.

This study uses single-cell RNA sequencing to analyze gene expression changes in human pancreatic islets under different glucose conditions over 24 hours, identifying genes and modules associated with time, glucose exposure, and their interaction.

The paper performs population-scale single-nucleus multi-omic profiling of skeletal muscle to identify genetic regulation across cell types and traits.

The paper identifies additional eQTL signals in adipose tissue and shows they contribute to cardiometabolic traits through gene dosage effects.

This study functionally interrogates 20 T2D-associated genes using isogenic hESC-derived β-like cells, identifying variants and genes linked to insulin production and lipotoxicity sensitivity.

This paper identifies miRNAs associated with type 2 diabetes and glycemic traits through genetic analysis of miRNA expression in human pancreatic islets.

This study integrates single-cell gene expression and chromatin accessibility data from human pancreatic islets to identify T1D risk mechanisms involving multiple cell types and genetic variants.

The paper examines the association between adipose tissue ACE2 expression and cardio-metabolic risk factors, finding lower ACE2 levels linked to T2D, obesity, and inflammation-related cell type changes.

This study identifies 303 novel genetic signals associated with plasma metabolites in Finnish men, revealing biological insights into disease mechanisms.

The paper maps transcription start sites in human pancreatic islets and identifies functional regulatory elements linked to type 2 diabetes GWAS signals.

The paper investigates how genetic variants affect gene expression in human pancreatic islets and identifies candidate genes linked to type 2 diabetes.

The paper uses single-cell ATAC-seq and deep learning to analyze pancreatic islet cells, revealing cell-specific regulatory signatures linked to type 2 diabetes.

The paper provides multi-genomic maps of the EndoC-βH1 cell model to define gene regulatory programs in human pancreatic β cells and identifies allelic effects on cis-regulatory elements.

BoostMe improves DNA methylation imputation in WGBS data, enhancing concordance with the MethylationEPIC array and identifying tissue-specific regulatory features.

The paper identifies genetic-environment interactions in skeletal muscle gene expression using allele-specific expression and clinical traits as environmental proxies.