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 for atlas-scale datasets like Human Pancreas Analysis Program (HPAP), we develop 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 shows the higher 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 recapitulates known islet pathobiology and shows differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+ T cells infiltration in islets during type 1 diabetes progression.

Multiplexed Imaging Mass Cytometry Analysis Characterizes the Vascular Niche in Pancreatic Cancer.

Oncogenesis and progression of pancreatic ductal adenocarcinoma (PDAC) is driven by complex interactions between the neoplastic component and the tumor microenvironment (TME), which includes immune, stromal, and parenchymal cells. In particular, most PDACs are characterized by a hypovascular and hypoxic environment that alters tumor cell behavior and limits the efficacy of chemotherapy and immunotherapy. Characterization of the spatial features of the vascular niche could advance our understanding of inter- and intra-tumoral heterogeneity in PDAC. Here, we investigated the vascular microenvironment of PDAC by applying imaging mass cytometry using a 26-antibody panel on 35 regions of interest (ROIs) across 9 patients, capturing over 140,000 single cells. The approach distinguished major cell types, including multiple populations of lymphoid and myeloid cells, endocrine cells, ductal cells, stromal cells, and endothelial cells. Evaluation of cellular neighborhoods identified 10 distinct spatial domains, including multiple immune and tumor-enriched environments as well as the vascular niche. Focused analysis revealed differential interactions between immune populations and the vasculature and identified distinct spatial domains wherein tumor cell proliferation occurs. Importantly, the vascular niche was closely associated with a population of CD44-expressing macrophages enriched for a pro-angiogenic gene signature. Together, this study provides insights into the spatial heterogeneity of PDAC and suggests a role for CD44-expressing macrophages in shaping the vascular niche.

Telocyte Recruitment During the Emergence of a Metaplastic Niche in the Stomach.

BACKGROUND & AIM: Telocytes, a recently identified type of subepithelial interstitial cell, have garnered attention for their potential roles in tissue homeostasis and repair. However, their contribution to gastric metaplasia remains unexplored. This study elucidates the role of telocytes in the development of metaplasia within the gastric environment. METHODS: To investigate the presence and behavior of telocytes during metaplastic transitions, we used drug-induced acute injury models (using DMP-777 or L635) and a genetically engineered mouse model (Mist1-Kras). Lineage tracing via the Foxl1-CreERT2;R26R-tdTomato mouse model was used to track telocyte migratory dynamics. Immunofluorescence staining was used to identify telocyte markers and evaluate their correlation with metaplasia-related changes. RESULTS: We confirmed the existence of FOXL1+/PDGFRα+ double-positive telocytes in the stomach's isthmus region. As metaplasia developed, we observed a marked increase in the telocyte population. The distribution of telocytes expanded beyond the isthmus to encompass the entire gland and closely reflected the expansion of the proliferative cell zone. Rather than a general response to mucosal damage, the shift in telocyte distribution was associated with the establishment of a metaplastic cell niche at the gland base. Furthermore, lineage-tracing experiments highlighted the active recruitment of telocytes to the emerging metaplastic cell niche, and we observed expression of Wnt5a, Bmp4, and Bmp7 in PDGFRα+ telocytes. CONCLUSIONS: These results suggest that telocytes contribute to the evolution of a gastric metaplasia niche. The dynamic behavior of these stromal cells, their responsiveness to metaplastic changes, and potential association with Wnt5a, Bmp4, and Bmp7 signaling emphasize the significance of telocytes in tissue adaptation and repair.

Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets.

Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of machine learning for early prediction of T1D using single-cell analysis of islets. Using gradient-boosting algorithms, we model changes in gene expression of single cells from pancreatic tissues in T1D and non-diabetic organ donors. We assess if mathematical modeling could predict the likelihood of T1D development in non-diabetic autoantibody-positive donors. While most autoantibody-positive donors are predicted to be non-diabetic, select donors with unique gene signatures are classified as T1D. Our strategy also reveals a shared gene signature in distinct T1D-associated models across cell types, suggesting a common effect of the disease on transcriptional outputs of these cells. Our study establishes a precedent for using machine learning in early detection of T1D.

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.

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.

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.