Christopher K. Glass

This study identifies cell-type-specific histone acetylation changes in Alzheimer's disease, highlighting MITF's potential role in microglial dysfunction.

This study shows TREM2 drives pro-scarring macrophage accumulation in infarcted myocardium, leading to fibrosis and reduced cardiac repair.

This study shows lysosomal health influences myeloid cell states and immune responses, with Grn deficiency leading to lysosomal dysfunction and specific transcriptional changes in microglia.

This study examines age-related changes in the human hippocampus using multi-omics data, revealing cell-type loss and epigenetic reprogramming linked to cognitive decline.

The paper identifies TREM2-p53 signaling as a determinant of oxygen-induced lung injury in mice, showing that TREM2 loss reduces inflammation and improves lung development.

The paper introduces a machine-learning pipeline, CellCartographer, to design TF pooled-screens for cell type conversion using chromatin accessibility data, achieving efficient differentiation of iPSCs into six cell types within 6 days.

PEAS identifies clustered genomic differences using a statistical approach, finding significant clusters in histone marks between mouse strains.

This study identifies a muscle-sympathetic nerve-intestine network that drives inflammation and atrophy in immobilized muscles, showing that intestinal inflammation contributes to muscle damage.

This study investigates how lysosomal dysfunction in microglia drives epigenetic and transcriptional changes in a mouse model of Sanfilippo syndrome, revealing shared mechanisms with Alzheimer's disease.

The paper identifies sex differences in microglial inflammatory responses in glioblastoma, showing that genes on the inactive X chromosome drive these differences.

The paper identifies transcription factors that regulate β-glucocerebrosidase activity, showing that USF2 and ONECUT2 have opposing effects on GCase levels and function.

This paper develops a platform to model and manipulate human microglial states in vitro using single-cell RNA sequencing and genetic tools, identifying disease-associated transcriptional signatures.

The paper links APOE4/4 genotype to lipid droplet accumulation in Alzheimer's microglia, showing its role in neurotoxicity through triglyceride synthesis and Tau phosphorylation.

The paper analyzes how genetic variation affects gene expression and epigenetics in Kupffer cells, distinguishing between cell intrinsic and environment-dependent mechanisms.

The paper systematically analyzes naturally occurring insertions and deletions (InDels) to assess their impact on transcription factor spacing and identifies tolerant and sensitive TF pairs.

This study identifies IL-33's role in microglial phagocytosis and its impact on synaptic refinement and seizure susceptibility in the developing brain.

The paper revises transcription start site annotations in Chinese hamster genes using experimental data to improve gene engineering and glycosylation in CHO cells.

The paper identifies how non-coding genetic variation affects macrophage responses to IL-4 by analyzing SNP and InDel effects across five inbred mouse strains.

This study defines age-specific brain environmental factors driving microglia phenotypes through transcriptomic and epigenomic analysis of human fetal and postnatal microglia.

The paper identifies DNA repair hotspots in post-mitotic human neurons linked to genes essential for their identity and function.

MAGGIE identifies DNA sequence motifs mediating TF binding and function by linking genetic variation to epigenomic changes, improving sensitivity and specificity over existing methods.

The paper shows that KDM7A and UTX demethylate repressive histone marks to enable NF-κB-dependent inflammatory gene activation in endothelial cells.

The paper identifies cell type-specific enhancer-promoter connectivity in the human brain and links these to disease risk variants in Alzheimer's and psychiatric disorders.

The paper identifies non-redundant DNA binding activities of AP-1 family members in macrophages using ChIP-sequencing and machine learning, showing motif ensembles drive factor-specific binding.

MARGE identifies collaborative transcription factor pairs by integrating genetic variation with epigenetic data to interpret non-coding genetic variation.