Biology subjects

Schug, J.

Publications and source records attributed to Schug, J..

9 recordsLinked to original sources

bioRxiv2025

Epigenetic adaptation of beta cells across lifespan and disease: age-related demethylation is advanced in type 2 diabetes

Although the prevalence of type 2 diabetes (T2D) increases with age, most adults maintain normoglycemia despite rising insulin resistance, largely due to the adaptive capacity of pancreatic beta cells to meet increased metabolic demand. However, persistent insulin resistance can lead to beta cell dysfunction and T2D onset. Here, leveraging cell-type-specific methylome data from the Human Pancreas Analysis Program (HPAP), we investigate the epigenomic basis of beta cell adaptation by mapping genome-wide DNA methylation (DNAm) patterns across the human lifespan. In healthy donors, we identify progressive age-related demethylation enriched in cis- regulatory elements at beta cell identity and function genes, suggesting that epigenetic remodeling supports functional adaptation to metabolic demand over time. In contrast, alpha cells show the opposite trajectory, with subtle, age-related hypermethylation. In T2D beta but not alpha cells we observed further demethylation compared to healthy controls, underscoring a unique capacity of beta cells to respond to changes in metabolic demand. Together, our findings suggest that DNAm remodeling in healthy beta cells reflects a long-term adaptation to metabolic demand, which in T2D is accelerated as part of a compensatory response that ultimately fails under sustained insulin resistance.

genomics↗

bioRxiv2025

Single-cell multiome analysis supports α-to-β transdifferentiation in human pancreas

Spontaneous transdifferentiation of pancreatic glucagon-producing alpha to insulin-secreting beta-cells has been observed in mouse but not in human islets1. Here, we analyzed the largest single-cell dataset of human islets to date, composed of 650,000 cells across 121 deceased organ donors, in search of transitional cell states. By integrating single-cell RNA-seq, single-nucleus ATAC-seq and single-nucleus multiome (joint RNA and ATAC profiling) datasets generated by the Human Pancreas Analysis Program (HPAP)2,3 we identified two previously undescribed cell populations (c11 and c13 cells), which together represent transitional states between alpha- and beta-cells. Some c11 cells are insulin-positive while others are glucagon positive, but none are double-positive. C11 cells repress alpha-cell identity genes and activate beta-cell specific genes. Moreover, the transcriptomic and epigenetic profiles of c11 and c13 cells indicate a transitioning phenotype driven by lineage-specific transcription factors. Genetic lineage tracing in primary human islet cells confirmed alpha-to-beta cell transdifferentiation. C11 and c13 cells exist in all islet samples regardless of disease statuses, with type 2 diabetic samples having significantly more transitioning cells than matched non-diabetic controls. The discovery of these transitional cell types suggests a possibility for future therapy - transdifferentiating alpha-cells to beta-cell through activation of the c11 gene program.

systems biology↗

bioRxiv2024

Villification of the intestinal epithelium is driven by Foxl1

The primitive gut tube of mammals initially forms as a simple cylinder consisting of the endoderm-derived, pseudostratified epithelium and the mesoderm-derived surrounding mesenchyme. During mid-gestation a dramatic transformation occurs in which the epithelium is both restructured into its final cuboidal form and simultaneously folded and refolded to create intestinal villi and intervillus regions, the incipient crypts. Here we show that the mesenchymal winged helix transcription factor Foxl1, itself induced by epithelial hedgehog signaling, controls villification by activating BMP and PDGFR as well as planar cell polarity genes in epithelial-adjacent telocyte progenitors, both directly and in a feed-forward loop with Foxo3.

developmental biology↗

bioRxiv2023

Modeling Type 1 Diabetes progression from single-cell transcriptomic measurements in human islets

Type 1 diabetes (T1D) is a chronic condition in which the insulin-producing beta cells are destroyed by immune cells. Research in the past few decades characterized the immune cells involved in disease pathogenesis and has led to the development of immunotherapies that can delay the onset of T1D by two years. Despite this progress, early detection of autoimmunity in individuals who will develop T1D remains a challenge. Here, we evaluated the potential of combining single-cell genomics and machine learning strategies as a prime approach to tackle this challenge. We used gradient-boosting-based machine learning algorithms and modeled changes in transcriptional profiles of single cells from pancreatic tissues in T1D and nondiabetic organ donors collected by the Human Pancreas Analysis Program. We assessed whether mathematical modelling could predict the likelihood of T1D development in nondiabetic autoantibody-positive organ donors. While the majority of autoantibody-positive organ donors were predicted to be nondiabetic by our model, select donors with unique gene signatures were classified with the T1D group. Remarkably, our strategy also revealed a shared gene signature in distinct T1D associated models based on different cell types including alpha cells, beta cells and acinar cells, suggesting a common effect of the disease on transcriptional outputs of these cells. Together, our strategy presents the first report on the utility of machine learning algorithms in early detection of molecular changes in T1D.

genomics↗

bioRxiv2023

Computational workflow and interactive analysis of single-cell expression profiling of islets generated by the Human Pancreas Analysis Program

Type 1 and Type 2 diabetes are distinct genetic diseases of the pancreas which are defined by the abnormal level of blood glucose. Understanding the initial molecular perturbations that occur during the pathogenesis of diabetes is of critical importance in understanding these disorders. The inability to biopsy the human pancreas of living donors hampers insights into early detection, as the majority of diabetes studies have been performed on peripheral leukocytes from the blood, which is not the site of pathogenesis. Therefore, efforts have been made by various teams including the Human Pancreas Analysis Program (HPAP) to collect pancreatic tissues from deceased organ donors with different clinical phenotypes. HPAP is designed to define the molecular pathogenesis of islet dysfunction by generating detailed datasets of functional, cellular, and molecular information in pancreatic tissues of clinically well-defined organ donors with Type 1 and Type 2 diabetes. Moreover, data generated by HPAP continously become available through a centralized database, PANC-DB, thus enabling the diabetes research community to access these multi-dimensional data prepublication. Here, we present the computational workflow for single-cell RNA-seq data analysis of 258,379 high-quality cells from the pancreatic islets of 67 human donors generated by HPAP, the largest existing scRNA-seq dataset of human pancreatic tissues. We report various computational steps including preprocessing, doublet removal, clustering and cell type annotation across single-cell RNA-seq data from islets of four distintct classes of organ donors, i.e. non-diabetic control, autoantibody positive but normoglycemic, Type 1 diabetic, and Type 2 diabetic individuals. Moreover, we present an interactive tool, called CellxGene developed by the Chan Zuckerberg initiative, to navigate these high-dimensional datasets. Our data and interactive tools provide a reliable reference for singlecell pancreatic islet biology studies, especially diabetes-related conditions.

genomics↗

bioRxiv2021

Single-cell analysis of the human pancreas in type 2 diabetes using multi-spectral imaging mass cytometry

Type 2 diabetes mellitus (T2D) is a chronic age-related disorder characterized by hyperglycemia due to the failure of pancreatic beta cells to compensate for increased insulin demand, typically associated with peripheral insulin resistance. However, despite decades of research, the pathogenic mechanisms underlying T2D remain poorly defined. Imaging mass cytometry (IMC) enables multiplexed assessment of the abundance and subcellular localization of multiple proteins on the same tissue section. Herein, we utilized IMC with a panel of 34 antibodies to quantify key markers of pancreatic exocrine, islet, and immune cells as well as stromal components. We employed this panel to analyze over 2.1 million cells from 16 pancreata obtained from donors with T2D and 13 pancreata from age similar non-diabetic controls. In the T2D pancreata, we observed significant alterations in islet architecture, endocrine cell composition, and surprisingly immune cell constituents. Thus, both HLA-DR positive CD8 T cells and macrophages were enriched intra-islet in the T2D pancreas. These efforts demonstrate the utility of IMC to investigate complex events at the cellular level in order to provide new insights into the pathophysiology of T2D.

molecular biology↗

bioRxiv2021

Multiomics single-cell analysis of human pancreatic islets reveals novel cellular states in health and type 1 diabetes

Type 1 diabetes (T1D) is an autoimmune disease of only partially defined etiology in which immune cells destroy insulin-producing beta cells. Using single-cell transcriptomics and an advanced analytical strategy to assess pancreatic islets of T1D, autoantibody-positive, and non-diabetic organ donors, we identified both canonical cell types and rare insulin-expressing cells with a hybrid mixture of endocrine and exocrine gene signatures within all donors. We further found elevated expression of MHC Class II pathway genes in exocrine ductal cells of T1D donors, which we confirmed through CyTOF, in situ imaging mass cytometry, and immunofluorescence analysis. Taken together, our multimodal analyses identify novel cell types and processes that may contribute to T1D immunopathogenesis and provide new cellular and molecular insights into human pancreas function.

genomics↗

bioRxiv2019

A high-content in vivo screen to identify microRNA epistasis in the repopulating mouse liver

Liver regeneration is impaired in mice with hepatocyte-specific deficiencies in microRNA (miRNA) processing; yet the roles of individual miRNAs or their combinatorial effects in this process are largely unknown. In this study, we sought to identify miRNAs that regulate hepatocyte repopulation following toxic liver injury in a high-throughput manner using the Fah-/- mouse. We constructed plasmid pools encoding over 30,000 tough decoy (TuD) miRNA inhibitors designed to target hepatocyte miRNAs in a pairwise manner. Plasmid libraries were delivered to hepatocytes of Fah-/- mice at the time of liver injury via hydrodynamic tail vein injection and integrated transgene-containing transposons were quantified following repopulation via high-throughput sequencing. Changes in polysome-bound transcripts following miRNA inhibition were determined using translating ribosome affinity purification followed by high-throughput sequencing. Analysis of TuD abundance in hepatocyte genomic DNA and input plasmid pools identified several thousand miRNA inhibitors that were significantly altered following repopulation. We classified a subset of miRNA-binding sites (MBSs) as having strong effect on liver repopulation, thus implicating the targeted hepatocyte miRNAs as regulators of this process. Furthermore, we generated a high-content map of pairwise interactions between 171 MBSs and identified both synergistic and redundant effects. Our study highlights the power of higher-order screens to uncover miRNA functions that would go undetected by individual miRNA perturbations, and provides a new paradigm for the study of epistasis of miRNA activities.

systems biology↗

bioRxiv2019

Comparative analysis of commercially available single-cell RNA sequencing platforms for their performance in complex human tissues

The past five years have witnessed a tremendous growth of single-cell RNA-seq methodologies. Currently, there are three major commercial platforms for single-cell RNA-seq: Fluidigm C1, Clontech iCell8 (formerly Wafergen) and 10x Genomics Chromium. Here, we provide a systematic comparison of the throughput, sensitivity, cost and other performance statistics for these three platforms using single cells from primary human islets. The primary human islets represent a complex biological system where multiple cell types coexist, with varying cellular abundance, diverse transcriptomic profiles and differing total RNA contents. We apply standard pipelines optimized for each system to derive gene expression matrices. We further evaluate the performance of each system by benchmarking single-cell data with bulk RNA-seq data from sorted cell fractions. Our analyses can be generalized to a variety of complex biological systems and serve as a guide to newcomers to the field of single-cell RNA-seq when selecting platforms.

genomics↗