Sonya A. MacParland

This study identifies two distinct macrophage populations in chronic lung allograft dysfunction using single-cell RNA sequencing and characterizes their functional roles in inflammation and fibrosis.

The paper integrates multiple datasets to create a comprehensive human liver cell atlas, identifying new cell types and associations with demographic factors.

This paper uses Visium HD spatial transcriptomics to create a high-resolution map of human liver cell types and spatial gene expression in adult and pediatric samples.

This study identifies RUNX1 as a key transcriptional regulator of fibrogenic and inflammatory signaling in cholangiocytes, showing its inhibition reduces biliary fibrosis and inflammation in models of cholangiopathy.

The paper maps the multiscale structure of human liver tissue using electron microscopy and deep learning, revealing vascular, cellular, and organelle organization and dynamics.

This study identifies NINJ1 as a mediator of hepatic ischemia-reperfusion injury, showing its role in cell death and inflammation, and suggests it as a therapeutic target.

The paper constructs a single-cell RNA-seq atlas of the human pediatric liver and identifies age-related gene signatures that differ from adult livers and are relevant to pediatric liver diseases.

This study develops a method to generate functional hepatocytes from human pluripotent stem cells through controlled differentiation and tissue engineering.

This study creates a single-nucleus and spatial transcriptomic atlas of the COVID-19 liver, revealing cellular and functional disruptions associated with organ damage.

This paper creates a single-cell atlas of the woodchuck liver to identify cellular programs in health and disease, showing similarities with human HBV infection.

The paper uses single-cell transcriptomics to compare strain-specific differences in myeloid inflammatory potential in healthy rat livers, revealing strain-specific variations in inflammatory signaling pathways.

This study creates a single-cell atlas of human liver development, identifying pathways and factors that guide hepatic cell fates and improving hepatocyte-like cell functionality.

This study benchmarks four methods for assigning cell type labels to cell clusters in scRNA-seq data, showing high performance but variability in precision-recall across methods and datasets.