Biology subjects

Mummey, H.

Publications and source records attributed to Mummey, H..

4 recordsLinked to original sources

bioRxiv2025

Single cell and spatial characterization of the human pancreas reveals drivers of beta cell dysfunction in cystic fibrosis

Cystic fibrosis (CF) causes severely damaged pancreas morphology and results in high rates of cystic fibrosis-related diabetes (CFRD), but the cellular pathways and regulatory programs driving CFRD pathogenesis in the pancreas are not understood. In this study, we performed single cell multiomic and spatial profiling of 2.04M cells in the pancreas from 23 non-disease and CF donors and defined regulatory programs and tissue niches of specific cell types and sub-types in CF. A high-mucin sub-type of ductal cells had relatively preserved abundance, altered localization and up-regulated stress, secretion and pro-fibrotic activity in CF, and conventional ductal cells showed evidence for transition to the high-mucin state. Increased inflammatory and fibrotic pathway activity in CF was linked to closer proximity and crosstalk between immune and stellate cells in specific niches. Beta cells had extensive genomic changes in CF, including increased stress and insulin secretion-related processes, and these changes were broadly distinct from those in type 1 and type 2 diabetes. Islets in CF preferentially localized near large adipose tissue and collagen structures, and both areas were strongly linked to beta cell loss in CF due to signaling from adipocytes, macrophages, stellate, and high-mucin ductal cells. Overall, our results reveal cellular drivers of pancreatic dysfunction in CF and offer new in-roads to preserving beta cells in CFRD.

genomics↗

bioRxiv2025

Single-cell multiome and spatial profiling reveals pancreas cell type-specific gene regulatory programs driving type 1 diabetes progression

Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here we performed single nucleus multiomics and spatial transcriptomics in up to 32 non-diabetic (ND), autoantibody-positive (AAB+), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine sub- types. Beta, acinar, and other cell types, and related cellular niches, had altered abundance and gene activity in T1D progression, including distinct pathways altered in AAB+ compared to T1D. We identified epigenomic drivers of gene activity in T1D and AAB+ which, combined with genetic association, revealed causal pathways of T1D risk including antigen presentation in beta cells. Finally, single cell and spatial profiles together revealed widespread changes in cell-cell signaling in T1D including signals affecting beta cell regulation. Overall, these results revealed drivers of T1D progression in the pancreas, which form the basis for therapeutic targets for disease prevention.

genomics↗

bioRxiv2024

Single cell multiome profiling of pancreatic islets reveals physiological changes in cell type-specific regulation associated with diabetes risk

Physiological variability in pancreatic cell type gene regulation and the impact on diabetes risk is poorly understood. In this study we mapped gene regulation in pancreatic cell types using single cell multiomic (joint RNA-seq and ATAC-seq) profiling in 28 non-diabetic donors in combination with single cell data from 35 non-diabetic donors in the Human Pancreas Analysis Program. We identified widespread associations with age, sex, BMI, and HbA1c, where gene regulatory responses were highly cell type- and phenotype-specific. In beta cells, donor age associated with hypoxia, apoptosis, unfolded protein response, and external signal-dependent transcriptional regulators, while HbA1c associated with inflammatory responses and gender with chromatin organization. We identified 10.8K loci where genetic variants were QTLs for cis regulatory element (cRE) accessibility, including 20% with lineage- or cell type-specific effects which disrupted distinct transcription factor motifs. Type 2 diabetes and glycemic trait associated variants were enriched in both phenotype- and QTL-associated beta cell cREs, whereas type 1 diabetes showed limited enrichment. Variants at 226 diabetes and glycemic trait loci were QTLs in beta and other cell types, including 40 that were statistically colocalized, and annotating target genes of colocalized QTLs revealed genes with putatively novel roles in disease. Our findings reveal diverse responses of pancreatic cell types to phenotype and genotype in physiology, and identify pathways, networks, and genes through which physiology impacts diabetes risk.

genomics↗

bioRxiv2023

An integrated map of cell type-specific gene expression in pancreatic islets

Pancreatic islets are comprised of multiple endocrine cell types that produce hormones required for glucose homeostasis, and islet dysfunction is a major factor in the development of type 1 and type 2 diabetes (T1D, T2D). Numerous studies have generated gene expression profiles in individual islet cell types using single cell assays. However, there is no canonical reference of gene expression in islet cell types in both health and disease that is also easily accessible for researchers to access, query, and use in bioinformatics pipelines. Here we present an integrated reference map of islet cell type-specific gene expression from 192,203 cells derived from single cell RNA-seq assays of 65 non-diabetic, T1D autoantibody positive (Aab+), T1D, and T2D donors from the Human Pancreas Analysis Program. We identified 10 endocrine and non-endocrine cell types as well as sub-populations of several cell types, and defined sets of marker genes for each cell type and sub-population. We tested for differential expression within each cell type in T1D Aab+, T1D, and T2D states, and identified 1,701 genes with significant changes in expression in any cell type. Most changes were observed in beta cells in T1D, and, by comparison, there were almost no genes with changes in T1D Aab+. To facilitate user interaction with this reference, we provide the data using several single cell visualization and reference mapping tools as well as open-access analytical pipelines used to create this reference. The results will serve as a valuable resource to investigators studying islet biology and diabetes.

genomics↗