Parul Kudtarkar

The paper integrates multiple scRNA-seq datasets to create a comprehensive atlas of human pancreatic islets, identifying 13 cell populations and providing a resource for diabetes research.

The paper examines sex and race differences in human pancreatic islets using scRNA-seq and snATAC-seq, finding sex-specific gene expression and chromatin accessibility differences in β cells that change with type 2 diabetes.

The paper uses single-cell multiome profiling to identify gene regulatory changes in pancreatic islets linked to diabetes risk factors like age, sex, and genetic variants.

This study identifies 165 T1D risk signals and develops a machine learning model, T1GRS, that improves risk prediction for T1D in Europeans and African Americans.

The paper creates an integrated gene expression map of pancreatic islet cell types from healthy and diabetic donors, identifying marker genes and differential expression patterns.

The paper introduces the T1D Knowledge Portal, a resource for querying genetic and functional data related to type 1 diabetes to aid in hypothesis generation and target discovery.

The paper uses single-cell chromatin accessibility to identify cell type- and state-specific regulatory programs associated with type 2 diabetes risk.

The paper uses single nucleus ATAC-seq and RNA-seq to show age-related changes in chromatin accessibility and gene expression linked to SARS-CoV-2 entry genes in human lung cells.