Michael Miller

The paper identifies gene regulators of ER stress in beta cells and links them to type 2 diabetes risk through functional genomics and CRISPR screening.

The paper uses single-cell epigenomics to study brain aging in mice, finding heterochromatin instability and transcription factor dysfunction across multiple brain regions and cell types.

This study uses single-cell multiomics and spatial transcriptomics to identify gene regulatory programs in pancreatic cell types that drive type 1 diabetes progression.

This study uses single-cell multi-omics to identify cell type-specific regulatory networks and novel therapeutic targets in MASLD.

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.

The paper constructs a single-cell chromatin accessibility atlas of the human brain, identifies cell-type-specific regulatory elements, and develops models to predict regulatory function of disease risk variants.

The paper analyzes gene regulatory programs in the neocortex of rodents and primates using single-cell multiomics, finding rapid evolution of cell type-specific gene expression and TE-driven cCREs.

The paper integrates single-cell multiomic data with genetics to identify gene regulatory changes in beta cells associated with type 2 diabetes.

This study identifies cMet/HGFR as a modulator of early neuroinflammation in TBI, showing that its inhibition reduces microglial reactivity and improves recovery.

The paper identifies T1D risk genes and regulatory elements that influence beta cell survival under proinflammatory cytokine exposure using functional genomics and genetics.

The paper creates a chromatin accessibility atlas across 25 adult human tissues, identifying over 750,000 cis-regulatory elements and linking them to gene regulatory programs and disease-associated variants.

The paper identifies 491,818 candidate cis regulatory elements in adult mouse brain sub-types and links them to target genes and transcriptional regulators involved in various pathways.

This study combines large-scale GWAS and single-cell epigenomics to identify cell type-specific mechanisms of type 1 diabetes risk.

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

The paper analyzes transcriptomics data availability and reusability between microarray and RNA-seq experiments published between 2009 and 2013, and provides recommendations to improve RNA-seq data sharing.