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 PDGFRα as well as planar cell polarity genes in epithelial-adjacent telocyte progenitors, both directly and in a feed-forward loop with Foxo3.

Separation of telomere protection from length regulation by two different point mutations at amino acid 492 of RTEL1.

RTEL1 is an essential DNA helicase that plays multiple roles in genome stability and telomere length regulation. A variant of RTEL1 with a lysine at position 492 is associated with short telomeres in Mus spretus , while a conserved methionine at this position is found in M. musculus, which has ultra-long telomeres. In humans, a missense mutation at this position ( RTEL1 M492I ) causes a fatal telomere biology disease termed Hoyeraal-Hreidarsson syndrome (HHS). We previously described a M. musculus mouse model termed 'Telomouse', in which changing methionine 492 to a lysine (M492K) shortened the telomeres to their length in humans. Here, we report on the derivation of a mouse strain carrying the M492I mutation, termed 'HHS mouse'. The HHS mouse telomeres are not as short as those of Telomice but nevertheless they display higher levels of telomeric DNA damage, fragility and recombination, associated with anaphase bridges and micronuclei. These observations indicate that the two mutations separate critical functions of RTEL1: M492K mainly reduces the telomere length setpoint, while M492I predominantly disrupts telomere protection. The two mouse models enable dissecting the mechanistic roles of RTEL1 and the different contributions of short telomeres and DNA damage to telomere biology diseases, genomic instability, cancer, and aging.

DNA Methylation-Based Assessment of Cell Composition in Human Pancreas and Islets.

Assessment of pancreas cell type composition is crucial to the understanding of the genesis of diabetes. Current approaches use immunodetection of protein markers, for example, insulin as a marker of ß-cells. A major limitation of these methods is that protein content varies in physiological and pathological conditions, complicating the extrapolation to actual cell number. Here, we demonstrate the use of cell type-specific DNA methylation markers for determining the fraction of specific cell types in human islet and pancreas specimens. We identified genomic loci that are uniquely demethylated in specific pancreatic cell types and applied targeted PCR to assess the methylation status of these loci in tissue samples, enabling inference of cell type composition. In islet preparations, normalization of insulin secretion to ß-cell DNA revealed similar ß-cell function in pre-type 1 diabetes (T1D), T1D, and type 2 diabetes (T2D), which was significantly lower than in donors without diabetes. In histological pancreas specimens from recent-onset T1D, this assay showed ß-cell fraction within the normal range, suggesting a significant contribution of ß-cell dysfunction. In T2D pancreata, we observed increased α-cell fraction and normal ß-cell fraction. Methylation-based analysis provides an accurate molecular alternative to immune detection of cell types in the human pancreas, with utility in the interpretation of insulin secretion assays and the assessment of pancreas cell composition in health and disease.

PRKAG2.2 is essential for FoxA1+ regulatory T cell differentiation and metabolic rewiring distinct from FoxP3+ regulatory T cells.

Forkhead box A1 (FoxA1)+ regulatory T cells (Tregs) exhibit distinct characteristics from FoxP3+ Tregs while equally effective in exerting anti-inflammatory properties. The role of FoxP3+ Tregs in vivo has been challenged, motivating a better understanding of other Tregs in modulating hyperactive immune responses. FoxA1+ Tregs are generated on activation of the transcription factor FoxA1 by interferon-ß (IFNß), an anti-inflammatory cytokine. T cell activation, expansion, and function hinge on metabolic adaptability. We demonstrated that IFNß promotes a metabolic rearrangement of FoxA1+ Tregs by enhancing oxidative phosphorylation and mitochondria clearance by mitophagy. In response to IFNß, FoxA1 induces a specific transcription variant of adenosine 5'-monophosphate-activated protein kinase (AMPK) γ2 subunit, PRKAG2.2. This leads to the activation of AMPK signaling, thereby enhancing mitochondrial respiration and mitophagy by ULK1-BNIP3. This IFNß-FoxA1-PRKAG2.2-BNIP3 axis is pivotal for their suppressive function. The involvement of PRKAG2.2 in FoxA1+ Treg, not FoxP3+ Treg differentiation, underscores the metabolic differences between Treg populations and suggests potential therapeutic targets for autoimmune diseases.

Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data.

BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) measures gene expression in single cells, while single-nucleus ATAC-sequencing (snATAC-seq) quantifies chromatin accessibility in single nuclei. These two data types provide complementary information for deciphering cell types and states. However, when analyzed individually, they sometimes produce conflicting results regarding cell type/state assignment. The power is compromised since the two modalities reflect the same underlying biology. Recently, it has become possible to measure both gene expression and chromatin accessibility from the same nucleus. Such paired data enable the direct modeling of the relationships between the two modalities. Given the availability of the vast amount of single-modality data, it is desirable to integrate the paired and unpaired single-modality datasets to gain a comprehensive view of the cellular complexity. RESULTS: We benchmark nine existing single-cell multi-omic data integration methods. Specifically, we evaluate to what extent the multiome data provide additional guidance for analyzing the existing single-modality data, and whether these methods uncover peak-gene associations from single-modality data. Our results indicate that multiome data are helpful for annotating single-modality data. However, we emphasize that the availability of an adequate number of nuclei in the multiome dataset is crucial for achieving accurate cell type annotation. Insufficient representation of nuclei may compromise the reliability of the annotations. Additionally, when generating a multiome dataset, the number of cells is more important than sequencing depth for cell type annotation. CONCLUSIONS: Seurat v4 is the best currently available platform for integrating scRNA-seq, snATAC-seq, and multiome data even in the presence of complex batch effects.

Telomouse-a mouse model with human-length telomeres generated by a single amino acid change in RTEL1.

Telomeres, the ends of eukaryotic chromosomes, protect genome integrity and enable cell proliferation. Maintaining optimal telomere length in the germline and throughout life limits the risk of cancer and enables healthy aging. Telomeres in the house mouse, Mus musculus, are about five times longer than human telomeres, limiting the use of this common laboratory animal for studying the contribution of telomere biology to aging and cancer. We identified a key amino acid variation in the helicase RTEL1, naturally occurring in the short-telomere mouse species M. spretus. Introducing this variation into M. musculus is sufficient to reduce the telomere length set point in the germline and generate mice with human-length telomeres. While these mice are fertile and appear healthy, the regenerative capacity of their colonic epithelium is compromised. The engineered Telomouse reported here demonstrates a dominant role of RTEL1 in telomere length regulation and provides a unique model for aging and cancer.

In vivo tracing of the Cytokeratin 14 lineages using self-cleaving guide RNAs and CRISPR/Cas9.

The current gold-standard for genetic lineage tracing in transgenic mice is based on cell-type specific expression of Cre recombinase. As an alternative, we developed a cell-type specific CRISPR/spCas9 system for lineage tracing. This method relies on RNA polymerase II promoter driven self-cleaving guide RNAs (scgRNA) to achieve tissue-specificity. To demonstrate proof-of-principle for this approach a transgenic mouse was generated harbouring a knock-in of a scgRNA into the Cytokeratin 14 (Krt14) locus. Krt14 expression marks the stem cells of squamous epithelium in the skin and oral mucosa. The scgRNA targets a Stop cassette preceding a fluorescent reporter in the Ai9-tdtomato mouse. Ai9-tdtomato reporter mice harbouring this allele along with a spCas9 transgene demonstrated precise marking of the Krt14 lineage. We conclude that RNA polymerase II promoter driven scgRNAs enable the use of CRISPR/spCas9 for genetic lineage tracing.

Mammalian Intestinal Development and Differentiation-The State of the Art.

The development of the mammalian intestine, from its earliest origins as a morphologically uniform sheet of endoderm cells during gastrulation into the complex organ system that is essential for the life of the organism, is a truly fascinating process. During midgestation development, reciprocal interactions between endoderm-derived epithelium and mesoderm-derived mesenchyme enable villification, or the conversion of a radially symmetric pseudostratified epithelium into the functional subdivision of crypts and villi. Once a mature crypt-villus axis is established, proliferation and differentiation of new epithelial cells continue throughout life. Spatially localized signals including the wingless and Int-1, fibroblast growth factor, and Hippo systems, among others, ensure that new cells are being born continuously in the crypt. As cells exit the crypt compartment, a gradient of bone morphogenetic protein signaling limits proliferation to allow for the specification of multiple mature cell types. The first major differentiation decision is dependent on Notch signaling, which specifies epithelial cells into absorptive and secretory lineages. The secretory lineage is subdivided further into Paneth, goblet, tuft, and enteroendocrine cells via a complex network of transcription factors. Although some of the signaling molecules are produced by epithelial cells, critical components are derived from specialized crypt-adjacent mesenchymal cells termed telocytes, which are marked by Forkhead box l1, GLI Family Zinc Finger 1, and platelet-derived growth factor receptor α. The crucial nature of these processes is evidenced by the multitude of intestinal disorders such as colorectal cancer, short-bowel syndrome, and inflammatory bowel disease, which all reflect perturbations of the development and/or differentiation of the intestine.

Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data.

Single-cell RNA-sequencing (scRNA-seq) measures gene expression in single cells, while single-nucleus ATAC-sequencing (snATAC-seq) enables the quantification of chromatin accessibility in single nuclei. These two data types provide complementary information for deciphering cell types/states. However, when analyzed individually, scRNA-seq and snATAC-seq data often produce conflicting results regarding cell type/state assignment. In addition, there is a loss of power as the two modalities reflect the same underlying cell types/states. Recently, it has become possible to measure both gene expression and chromatin accessibility from the same nucleus. Such paired data make it possible to directly model the relationships between the two modalities. However, given the availability of the vast amount of single-modality data, it is desirable to integrate the paired and unpaired single-modality data to gain a comprehensive view of the cellular complexity. Here, we benchmarked the performance of seven existing single-cell multi-omic data integration methods. Specifically, we evaluated whether these methods are able to uncover peak-gene associations from single-modality data, and to what extent the multiome data can provide additional guidance for the analysis of the existing single-modality data. Our results indicate that multiome data are helpful for annotating single-modality data, but the number of cells in the multiome data is critical to ensure a good cell type annotation. Additionally, when generating a multiome dataset, the number of cells is more important than sequencing depth for cell type annotation. Lastly, Seurat v4 is the best at integrating scRNA-seq, snATAC-seq, and multiome data even in the presence of complex batch effects.

The Role of Non-Coding RNAs in Liver Disease, Injury, and Regeneration.

Non-coding RNAs (ncRNAs) have diverse functions in health and pathology in many tissues, including the liver. This review highlights important microRNAs (miRs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) in liver disease and regeneration. Greater attention is given to more prevalent and well characterized RNAs, including: miR-122, miR-21, the let-7 family of miRs, miR-451a, miR-144, and MALAT1.

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.

AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics.

Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs, we developed AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX show the superior performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulated known islet pathobiology and showed differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+ T cells infiltration in islets during type 1 diabetes progression.

H3K27me3 Demethylases Maintain the Transcriptional and Epigenomic Landscape of the Intestinal Epithelium.

BACKGROUND & AIMS: Although trimethylation of histone H3 lysine 27 (H3K27me3) by polycomb repressive complex 2 is required for intestinal function, the role of the antagonistic process-H3K27me3 demethylation-in the intestine remains unknown. The aim of this study was to determine the contribution of H3K27me3 demethylases to intestinal homeostasis. METHODS: An inducible mouse model was used to simultaneously ablate the 2 known H3K27me3 demethylases, lysine (K)-specific demethylase 6A (Kdm6a) and lysine (K)-specific demethylase 6B (Kdm6b), from the intestinal epithelium. Mice were analyzed at acute and prolonged time points after Kdm6a/b ablation. Cellular proliferation and differentiation were measured using immunohistochemistry, while RNA sequencing and chromatin immunoprecipitation followed by sequencing for H3K27me3 were used to identify gene expression and chromatin changes after Kdm6a/b loss. Intestinal epithelial renewal was evaluated using a radiation-induced injury model, while Paneth cell homeostasis was measured via immunohistochemistry, immunoblot, and transmission electron microscopy. RESULTS: We did not detect any effect of Kdm6a/b ablation on intestinal cell proliferation or differentiation toward the secretory cell lineages. Acute and prolonged Kdm6a/b loss perturbed expression of gene signatures belonging to multiple cell lineages (adjusted P value < .05), and a set of 72 genes was identified as being down-regulated with an associated increase in H3K27me3 levels after Kdm6a/b ablation (false discovery rate, <0.05). After prolonged Kdm6a/b loss, dysregulation of the Paneth cell gene signature was associated with perturbed matrix metallopeptidase 7 localization (P < .0001) and expression. CONCLUSIONS: Although KDM6A/B does not regulate intestinal cell differentiation, both enzymes are required to support the full transcriptomic and epigenomic landscape of the intestinal epithelium and the expression of key Paneth cell genes.

Understanding islet dysfunction in type 2 diabetes through multidimensional pancreatic phenotyping: The Human Pancreas Analysis Program.

In this perspective, we provide an overview of a recently established National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) initiative, the Human Pancreas Analysis Program for Type 2 Diabetes (HPAP-T2D). This program is designed to define the molecular pathogenesis of islet dysfunction by studying human pancreatic tissue samples from organ donors with T2D. HPAP-T2D generates detailed datasets of physiological, histological, transcriptomic, epigenomic, and genomic information. Importantly, all data collected, generated, and analyzed by HPAP-T2D are made immediately and freely available through a centralized database, PANC-DB, thus providing a comprehensive data resource for the diabetes research community.

3D chromatin maps of the human pancreas reveal lineage-specific regulatory architecture of T2D risk.

Three-dimensional (3D) chromatin organization maps help dissect cell-type-specific gene regulatory programs. Furthermore, 3D chromatin maps contribute to elucidating the pathogenesis of complex genetic diseases by connecting distal regulatory regions and genetic risk variants to their respective target genes. To understand the cell-type-specific regulatory architecture of diabetes risk, we generated transcriptomic and 3D epigenomic profiles of human pancreatic acinar, alpha, and beta cells using single-cell RNA-seq, single-cell ATAC-seq, and high-resolution Hi-C of sorted cells. Comparisons of these profiles revealed differential A/B (open/closed) chromatin compartmentalization, chromatin looping, and transcriptional factor-mediated control of cell-type-specific gene regulatory programs. We identified a total of 4,750 putative causal-variant-to-target-gene pairs at 194 type 2 diabetes GWAS signals using pancreatic 3D chromatin maps. We found that the connections between candidate causal variants and their putative target effector genes are cell-type stratified and emphasize previously underappreciated roles for alpha and acinar cells in diabetes pathogenesis.

Genetic activation of glucokinase in a minority of pancreatic beta cells causes hypoglycemia in mice.

AIMS: Glucokinase (GK) is expressed in the glucose-sensing cells of the islets of Langerhans and plays a critical role in glucose homeostasis. Here, we tested the hypothesis that genetic activation of GK in a small subset of ß-cells is sufficient to change the glucose set-point of the whole islet. MATERIAL AND METHODS: Mouse models of cell-type specific GK deficiency (GKKO) and genetic enzyme activation (GKKI) in a subset of ß-cells were obtained by crossing the αGSU (gonadotropin alpha subunit)-Cre transgene with the appropriate GK mutant alleles. Metabolic analyses consisted of glucose tolerance tests, perifusion of isolated islets and intracellular calcium measurements. KEY FINDINGS: The αGSU-Cre transgene produced genetically mosaic islets, as Cre was active in 15 ± 1.2 % of ß-cells. While mice deficient for GK in a subset of islet cells were normal, unexpectedly, GKKI mice were chronically hypoglycemic, glucose intolerant, and had a lower threshold for glucose stimulated insulin secretion. GKKI mice exhibited an average fasting blood glucose level of 3.5 mM. GKKI islets responded with intracellular calcium signals that spread through the whole islets at 1 mM and secreted insulin at 3 mM glucose. SIGNIFICANCE: Genetic activation of GK in a minority of ß-cells is sufficient to change the glucose threshold for insulin secretion in the entire islet and thereby glucose homeostasis in the whole animal. These data support the model in which ß-cells with higher GK activity function as 'hub' or 'trigger' cells and thus control insulin secretion by the ß-cell collective within the islet.