Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes

scQA: A dual-perspective cell type identification model for single cell transcriptome data

Single-cell RNA sequencing technologies have been pivotal in advancing the development of algorithms for clustering heterogeneous cell populations. Existing methods for utilizing scRNA-seq data to identify cell types tend to neglect the beneficial impact of dropout events and perform clustering focusing solely on quantitative perspective. Here, we introduce a novel method named scQA, notable for its ability to concurrently identify cell types and cell type-specific key genes from both qualitative and quantitative perspectives. In contrast to other methods, scQA not only identifies cell types but also extracts key genes associated with these cell types, enabling bidirectional clustering for scRNA-seq data. Through an iterative process, our approach aims to minimize the number of landmarks to approximately a dozen while maximizing the inclusion of quasi-trend-preserved genes with dropouts both qualitatively and quantitatively. It then clusters cells by employing an ingenious label propagation strategy, obviating the requirement for a predetermined number of cell types. Validated on 20 publicly available scRNA-seq datasets, scQA consistently outperforms other salient tools. Furthermore, we confirm the effectiveness and potential biological significance of the identified key genes through both external and internal validation. In conclusion, scQA emerges as a valuable tool for investigating cell heterogeneity due to its distinctive fusion of qualitative and quantitative facets, along with bidirectional clustering capabilities. Furthermore, it can be seamlessly integrated into border scRNA-seq analyses. The source codes are publicly available at https://github.com/LD-Lyndee/scQA.

Assessing parameter efficient methods for pre-trained language model in annotating scRNA-seq data

Annotating cell types of single-cell RNA sequencing (scRNA-seq) data is crucial for studying cellular heterogeneity in the tumor microenvironment. Recently, large-scale pre-trained language models (PLMs) have achieved significant progress in cell-type annotation of scRNA-seq data. This approach effectively addresses previous methods' shortcomings in performance and generalization. However, fine-tuning PLMs for different downstream tasks demands considerable computational resources, rendering it impractical. Hence, a new research branch introduces parameter-efficient fine-tuning (PEFT). This involves optimizing a few parameters while leaving the majority unchanged, leading to substantial reductions in computational expenses. Here, we utilize scBERT, a large-scale pre-trained model, to explore the capabilities of three PEFT methods in scRNA-seq cell type annotation. Extensive benchmark studies across several datasets demonstrate the superior applicability of PEFT methods. Furthermore, downstream analysis using models obtained through PEFT showcases their utility in novel cell type discovery and model interpretability for potential marker genes. Our findings underscore the considerable potential of PEFT in PLM-based cell type annotation, presenting novel perspectives for the analysis of scRNA-seq data.

Differential contributions of G protein- or arrestin subtype-mediated signalling underlie urocortin 3-induced somatostatin secretion in pancreatic δ cells

BACKGROUND AND PURPOSE

Pancreatic islets are modulated by cross-talk among different cell types and paracrine signalling plays important roles in maintaining glucose homeostasis. Urocortin 3 (UCN3) secreted by pancreatic β cells activates the CRF2 receptor (CRF2R) and downstream pathways mediated by different G protein or arrestin subtypes in δ cells to cause somatostatin (SST) secretion, and constitutes an important feedback circuit for glucose homeostasis.

EXPERIMENTAL APPROACH

Here, we used Arrb1-/-, Arrb2-/-, Gsfl/fl and Gqfl/fl knockout mice, the G11-shRNA-GFPfl/fl lentivirus, as well as functional assays and pharmacological characterization to study how the coupling of Gs, G11 and β-arrestin1 to CRF2R contributed to UCN3-induced SST secretion in pancreatic δ cells.

KEY RESULTS

Our study showed that CRF2R coupled to a panel of G protein and arrestin subtypes in response to UCN3 engagement. While RyR3 phosphorylation by PKA at the S156, S2706 and S4697 sites may underlie the Gs-mediated UCN3- CRF2R axis for SST secretion, the interaction of SYT1 with β-arrestin1 is also essential for efficient SST secretion downstream of CRF2R. The specific expression of the transcription factor Stat6 may contribute to G11 expression in pancreatic δ cells. Furthermore, we found that different UCN3 concentrations may have distinct effects on glucose homeostasis, and these effects may depend on different CRF2R downstream effectors.

CONCLUSIONS AND IMPLICATIONS

Collectively, our results provide a landscape view of signalling mediated by different G protein or arrestin subtypes downstream of paracrine UCN3- CRF2R signalling in pancreatic β-δ-cell circuits, which may facilitate the understanding of fine-tuned glucose homeostasis networks.

Axon guidance cue SEMA3A promotes the aggressive phenotype of basal-like PDAC

OBJECTIVE

The dysregulation of the axon guidance pathway is common in pancreatic ductal adenocarcinoma (PDAC), yet our understanding of its biological relevance is limited. Here, we investigated the functional role of the axon guidance cue SEMA3A in supporting PDAC progression.

DESIGN

We integrated bulk and single-cell transcriptomic datasets of human PDAC with in situ hybridisation analyses of patients' tissues to evaluate SEMA3A expression in molecular subtypes of PDAC. Gain and loss of function experiments in PDAC cell lines and organoids were performed to dissect how SEMA3A contributes to define a biologically aggressive phenotype.

RESULTS

In PDAC tissues, SEMA3A is expressed by stromal elements and selectively enriched in basal-like/squamous epithelial cells. Accordingly, expression of SEMA3A in PDAC cells is induced by both cell-intrinsic and cell-extrinsic determinants of the basal-like phenotype. In vitro, SEMA3A promotes cell migration as well as anoikis resistance. At the molecular level, these phenotypes are associated with increased focal adhesion kinase signalling through canonical SEMA3A-NRP1 axis. SEMA3A provides mouse PDAC cells with greater metastatic competence and favours intratumoural infiltration of tumour-associated macrophages and reduced density of T cells. Mechanistically, SEMA3A functions as chemoattractant for macrophages and skews their polarisation towards an M2-like phenotype. In SEMA3Ahigh tumours, depletion of macrophages results in greater intratumour infiltration by CD8+T cells and better control of the disease from antitumour treatment.

CONCLUSIONS

Here, we show that SEMA3A is a stress-sensitive locus that promotes the malignant phenotype of basal-like PDAC through both cell-intrinsic and cell-extrinsic mechanisms.

Unraveling pancreatic ductal adenocarcinoma immune prognostic signature through a naive B cell gene set

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC), a leading cause of cancer mortality, has a complex pathogenesis involving various immune cells, including B cells and their subpopulations. Despite emerging research on the role of these cells within the tumor microenvironment (TME), the detailed molecular interactions with tumor-infiltrating immune cells (TIICs) are not fully understood.

METHODS

We applied CIBERSORT to quantify TIICs and naive B cells, which are prognostic for PDAC. Marker genes from scRNA-seq and modular genes from weighted gene co-expression network analysis (WGCNA) were integrated to identify naive B cell-related genes. A prognostic signature was constructed utilizing ten machine-learning algorithms, with validation in external cohorts. We further assessed the immune cell diversity, ESTIMATE scores, and immune checkpoint genes (ICGs) between patient groups stratified by risk to clarify the immune landscape in PDAC.

RESULTS

Our analysis identified 994 naive B cell-related genes across single-cell and bulk transcriptomes, with 247 linked to overall survival. We developed a 12-gene prognostic signature using Lasso and plsRcox algorithms, which was confirmed by 10-fold cross-validation and showed robust predictive power in training and real-world cohorts. Notably, we observed substantial differences in immune infiltration between patients with high and low risk.

CONCLUSION

Our study presents a robust prognostic signature that effectively maps the complex immune interactions in PDAC, emphasizing the critical function of naive B cells and suggesting new avenues for immunotherapeutic interventions. This signature has potential clinical applications in personalizing PDAC treatment, enhancing the understanding of immune dynamics, and guiding immunotherapy strategies.

PARE: A framework for removal of confounding effects from any distance-based dimension reduction method

Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. For lower-dimensional visualization, our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.

Abdominal aortic aneurysm and cardiometabolic traits share strong genetic susceptibility to lipid metabolism and inflammation

Abdominal aortic aneurysm has a high heritability and often co-occurs with other cardiometabolic disorders, suggesting shared genetic susceptibility. We investigate this commonality leveraging recent GWAS studies of abdominal aortic aneurysm and 32 cardiometabolic traits. We find significant genetic correlations between abdominal aortic aneurysm and 21 of the cardiometabolic traits investigated, including causal relationships with coronary artery disease, hypertension, lipid traits, and blood pressure. For each trait pair, we identify shared causal variants, genes, and pathways, revealing that cholesterol metabolism and inflammation are shared most prominently. Additionally, we show the tissue and cell type specificity in the shared signals, with strong enrichment across traits in the liver, arteries, adipose tissues, macrophages, adipocytes, and fibroblasts. Finally, we leverage drug-gene databases to identify several lipid-lowering drugs and antioxidants with high potential to treat abdominal aortic aneurysm with comorbidities. Our study provides insight into the shared genetic mechanism between abdominal aortic aneurysm and cardiometabolic traits, and identifies potential targets for pharmacological intervention.

Proteomic predictors of individualized nutrient-specific insulin secretion in health and disease

Population-level variation and mechanisms behind insulin secretion in response to carbohydrate, protein, and fat remain uncharacterized. We defined prototypical insulin secretion responses to three macronutrients in islets from 140 cadaveric donors, including those with type 2 diabetes. The majority of donors' islets exhibited the highest insulin response to glucose, moderate response to amino acid, and minimal response to fatty acid. However, 9% of donors' islets had amino acid responses, and 8% had fatty acid responses that were larger than their glucose-stimulated insulin responses. We leveraged this heterogeneity and used multi-omics to identify molecular correlates of nutrient responsiveness, as well as proteins and mRNAs altered in type 2 diabetes. We also examined nutrient-stimulated insulin release from stem cell-derived islets and observed responsiveness to fat but not carbohydrate or protein-potentially a hallmark of immaturity. Understanding the diversity of insulin responses to carbohydrate, protein, and fat lays the groundwork for personalized nutrition.

Identification of unique cell type responses in pancreatic islets to stress

Diabetes involves the death or dysfunction of pancreatic β-cells. Analysis of bulk sequencing from human samples and studies using in vitro and in vivo models suggest that endoplasmic reticulum and inflammatory signaling play an important role in diabetes progression. To better characterize cell type-specific stress response, we perform multiplexed single-cell RNA sequencing to define the transcriptional signature of primary human islet cells exposed to endoplasmic reticulum and inflammatory stress. Through comprehensive pair-wise analysis of stress responses across pancreatic endocrine and exocrine cell types, we define changes in gene expression for each cell type under different diabetes-associated stressors. We find that β-, α-, and ductal cells have the greatest transcriptional response. We utilize stem cell-derived islets to study islet health through the candidate gene CIB1, which was upregulated under stress in primary human islets. Our findings provide insights into cell type-specific responses to diabetes-associated stress and establish a resource to identify targets for diabetes therapeutics.

Hyperglycemia impairs EAAT2 glutamate transporter trafficking and glutamate clearance in islets of Langerhans: implications for type 2 diabetes pathogenesis and treatment

Pancreatic endocrine cells employ a sophisticated system of paracrine and autocrine signals to synchronize their activities, including glutamate, which controls hormone release and β-cell viability by acting on glutamate receptors expressed by endocrine cells. We here investigate whether alteration of the excitatory amino acid transporter 2 (EAAT2), the major glutamate clearance system in the islet, may occur in type 2 diabetes mellitus and contribute to β-cell dysfunction. Increased EAAT2 intracellular localization was evident in islets of Langerhans from T2DM subjects as compared with healthy control subjects, despite similar expression levels. Chronic treatment of islets from healthy donors with high-glucose concentrations led to the transporter internalization in vesicular compartments and reduced [H3]-d-glutamate uptake (65 ± 5% inhibition), phenocopying the findings in T2DM pancreatic sections. The transporter relocalization was associated with decreased Akt phosphorylation protein levels, suggesting an involvement of the phosphoinositide 3-kinase (PI3K)/Akt pathway in the process. In line with this, PI3K inhibition by a 100-µM LY294002 treatment in human and clonal β-cells caused the transporter relocalization in intracellular compartments and significantly reduced the glutamate uptake compared to control conditions, suggesting that hyperglycemia changes the trafficking of the transporter to the plasma membrane. Upregulation of the glutamate transporter upon treatment with the antibiotic ceftriaxone rescued hyperglycemia-induced β-cells dysfunction and death. Our data underscore the significance of EAAT2 in regulating islet physiology and provide a rationale for potential therapeutic targeting of this transporter to preserve β-cell survival and function in diabetes.NEW & NOTEWORTHY The glutamate transporter SLC1A2/excitatory amino acid transporter 2 (EAAT2) is expressed on the plasma membrane of pancreatic β-cells and controls islet glutamate clearance and β-cells survival. We found that the EAAT2 membrane expression is lost in the islets of Langerhans from type 2 diabetes mellitus (T2DM) patients due to hyperglycemia-induced downregulation of the phosphoinositide 3-kinase/Akt pathway and modification of its intracellular trafficking. Pharmacological rescue of EAAT2 expression prevents β-cell dysfunction and death, suggesting EAAT2 as a new potential target of intervention in T2DM.

Evaluating glucose-dependent insulinotropic polypeptide and glucagon as key regulators of insulin secretion in the pancreatic islet

The incretin axis is an essential component of postprandial insulin secretion and glucose homeostasis. There are two incretin hormones, glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), which exert multiple actions throughout the body. A key cellular target for the incretins are pancreatic β-cells, where they potentiate nutrient-stimulated insulin secretion. This feature of incretins has made this system an attractive target for therapeutic interventions aimed at controlling glycemia. Here, we discuss the role of GIP in both β-cells and α-cells within the islet, to stimulate insulin and glucagon secretion, respectively. Moreover, we discuss how glucagon secretion from α-cells has important insulinotropic actions in β-cells through an axis termed α- to β-cell communication. These recent advances have elevated the potential of GIP and glucagon as a therapeutic targets, coinciding with emerging compounds that pharmacologically leverage the actions of these two peptides in the context of diabetes and obesity.

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

B-Glycine as a marker for β cell imaging and β cell mass evaluation

PURPOSE

β cell mass (BCM) and function are essential to the diagnosis and therapy of diabetes. Diabetic patients serve β cell loss is, and damage of β cells leads to severe insulin deficiency. Our understanding of the role of BCM in diabetes progression is extremely limited by lacking efficient methods to evaluate BCM in vivo. In vitro methods of labeling islets, including loading of contrast reagent or integration of exogenous biomarker, require artificial manipulation on islets, of which the clinical application is limited. Imaging methods targeting endogenous biomarkers may solve the above problems. However, traditional reagents targeting GLP-1R and VAMT2 result in a high background of adjacent tissues, complicating the identification of pancreatic signals. Here, we report a non-invasive and quantitative imaging technique by using radiolabeled glycine mimics ([18F]FBG, a boron-trifluoride derivative of glycine) to assay islet function and monitor BCM changes in living animals.

METHODS

Glycine derivatives, FBG, FBSa, 2Me-FBG, 3Me-FBG, were successfully synthesized and labeled with 18F. Specificity of glycine derivatives were characterized by in vitro experiment. PET imaging and biodistribution studies were performed in animal models carring GLYT over-expressed cells. In vivo evaluation of BCM with [18F]FBG were performed in STZ (streptozocin) induced T1D (type 1 diabetes) models.

RESULTS

GLYT responds to excess blood glycine levels and transports glycine into islet cells to maintain the activity of the glycine receptor (GLYR). Best PET imaging condition was 80 min after given a total of 240 ~ 250 nmol imaging reagent (a mixture of [18F]FBG and natural glycine) intravenously. [18F]FBG can detect both endogenous and exogenous islets clearly in vivo. When applied to STZ induced T1D mouse models, total uptake of [18F]FBG in the pancreas exhibited a linear correlation with survival BCM.

CONCLUSION

[18F]FBG targeting the endogenous glycine transporter (GLYT), which is highly expressed on islet cells, avoiding extra modification on islet cells. Meanwhile the highly restricted expression pattern of GLYT excluded the background in adjacent tissues. This [18F]FBG-based imaging technique provides a non-invasive method to quantify BCM in vivo, implying a new evaluation index for diabetic assessment.

The Potential Mechanism of Remission in Type 2 Diabetes Mellitus After Vertical Sleeve Gastrectomy

In recent years, there has been a gradual increase in the prevalence of obesity and type 2 diabetes mellitus (T2DM), with bariatric surgery remaining the most effective treatment strategy for these conditions. Vertical sleeve gastrectomy (VSG) has emerged as the most popular surgical procedure for bariatric/metabolic surgeries, effectively promoting weight loss and improving or curing T2DM. The alterations in the gastrointestinal tract following VSG may improve insulin secretion and resistance by increasing incretin secretion (especially GLP-1), modifying the gut microbiota composition, and through mechanisms dependent on weight loss. This review focuses on the potential mechanisms through which the enhanced action of incretin and metabolic changes in the digestive system after VSG may contribute to the remission of T2DM.

PredGCN: a Pruning-enabled Gene-Cell Net for automatic cell annotation of single cell transcriptome data

MOTIVATION

The annotation of cell types from single-cell transcriptomics is essential for understanding the biological identity and functionality of cellular populations. Although manual annotation remains the gold standard, the advent of automatic pipelines has become crucial for scalable, unbiased, and cost-effective annotations. Nonetheless, the effectiveness of these automatic methods, particularly those employing deep learning, significantly depends on the architecture of the classifier and the quality and diversity of the training datasets.

RESULTS

To address these limitations, we present a Pruning-enabled Gene-Cell Net (PredGCN) incorporating a Coupled Gene-Cell Net (CGCN) to enable representation learning and information storage. PredGCN integrates a Gene Splicing Net (GSN) and a Cell Stratification Net (CSN), employing a pruning operation (PrO) to dynamically tackle the complexity of heterogeneous cell identification. Among them, GSN leverages multiple statistical and hypothesis-driven feature extraction methods to selectively assemble genes with specificity for scRNA-seq data while CSN unifies elements based on diverse region demarcation principles, exploiting the representations from GSN and precise identification from different regional homogeneity perspectives. Furthermore, we develop a multi-objective Pareto pruning operation (Pareto PrO) to expand the dynamic capabilities of CGCN, optimizing the sub-network structure for accurate cell type annotation. Multiple comparison experiments on real scRNA-seq datasets from various species have demonstrated that PredGCN surpasses existing state-of-the-art methods, including its scalability to cross-species datasets. Moreover, PredGCN can uncover unknown cell types and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into cell type identification and characterizing scRNA-seq data from different perspectives.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/IrisQi7/PredGCN and test data is available at https://figshare.com/articles/dataset/PredGCN/25251163.

Optogenetic β cell interrogation in vivo reveals a functional hierarchy directing the Ca2+ response to glucose supported by vitamin B6

Coordination of cellular activity through Ca2+ enables β cells to secrete precise quantities of insulin. To explore how the Ca2+ response is orchestrated in space and time, we implement optogenetic systems to probe the role of individual β cells in the glucose response. By targeted β cell activation/inactivation in zebrafish, we reveal a hierarchy of cells, each with a different level of influence over islet-wide Ca2+ dynamics. First-responder β cells lie at the top of the hierarchy, essential for initiating the first-phase Ca2+ response. Silencing first responders impairs the Ca2+ response to glucose. Conversely, selective activation of first responders demonstrates their increased capability to raise pan-islet Ca2+ levels compared to followers. By photolabeling and transcriptionally profiling β cells that differ in their thresholds to a glucose-stimulated Ca2+ response, we highlight vitamin B6 production as a signature pathway of first responders. We further define an evolutionarily conserved requirement for vitamin B6 in enabling the Ca2+ response to glucose in mammalian systems.

Regulation of endocrine cell alternative splicing revealed by single-cell RNA sequencing in type 2 diabetes pathogenesis

The prevalent RNA alternative splicing (AS) contributes to molecular diversity, which has been demonstrated in cellular function regulation and disease pathogenesis. However, the contribution of AS in pancreatic islets during diabetes progression remains unclear. Here, we reanalyze the full-length single-cell RNA sequencing data from the deposited database to investigate AS regulation across human pancreatic endocrine cell types in non-diabetic (ND) and type 2 diabetic (T2D) individuals. Our analysis demonstrates the significant association between transcriptomic AS profiles and cell-type-specificity, which could be applied to distinguish the clustering of major endocrine cell types. Moreover, AS profiles are enabled to clearly define the mature subset of β-cells in healthy controls, which is completely lost in T2D. Further analysis reveals that RNA-binding proteins (RBPs), heterogeneous nuclear ribonucleoproteins (hnRNPs) and FXR1 family proteins are predicted to induce the functional impairment of β-cells through regulating AS profiles. Finally, trajectory analysis of endocrine cells suggests the β-cell identity shift through dedifferentiation and transdifferentiation of β-cells during the progression of T2D. Together, our study provides a mechanism for regulating β-cell functions and suggests the significant contribution of AS program during diabetes pathogenesis.

Predicting type 2 diabetes via machine learning integration of multiple omics from human pancreatic islets

Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this defect are insufficiently characterized. Integrating multiple layers of biomedical information, such as different Omics, may allow more accurate understanding of complex diseases such as T2D. Our aim was to explore and use Machine Learning to integrate multiple sources of biological/molecular information (multiOmics), in our case RNA-sequening, DNA methylation, SNP and phenotypic data from islet donors with T2D and non-diabetic controls. We exploited Machine Learning to perform multiOmics integration of DNA methylation, expression, SNPs, and phenotypes from pancreatic islets of 110 individuals, with ~ 30% being T2D cases. DNA methylation was analyzed using Infinium MethylationEPIC array, expression was analyzed using RNA-sequencing, and SNPs were analyzed using HumanOmniExpress arrays. Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the curve of 0.96 ± 0.08 on the test dataset after cross-validation. Biomarkers identified by this multiOmics integration, including SACS and TXNIP DNA methylation, OPRD1 and RHOT1 expression and a SNP annotated to ANO1, provide novel insights into the interplay between different biological mechanisms contributing to T2D. This Machine Learning approach of multiOmics cross-sectional data from human pancreatic islets achieved a promising accuracy of T2D prediction, which may potentially find broad applications in clinical diagnostics. In addition, it delivered novel candidate biomarkers for T2D and links between them across the different Omics.

Senescence of human pancreatic beta cells enhances functional maturation through chromatin reorganization and promotes interferon responsiveness

Senescent cells can influence the function of tissues in which they reside, and their propensity for disease. A portion of adult human pancreatic beta cells express the senescence marker p16, yet it is unclear whether they are in a senescent state, and how this affects insulin secretion. We analyzed single-cell transcriptome datasets of adult human beta cells, and found that p16-positive cells express senescence gene signatures, as well as elevated levels of beta-cell maturation genes, consistent with enhanced functionality. Senescent human beta-like cells in culture undergo chromatin reorganization that leads to activation of enhancers regulating functional maturation genes and acquisition of glucose-stimulated insulin secretion capacity. Strikingly, Interferon-stimulated genes are elevated in senescent human beta cells, but genes encoding senescence-associated secretory phenotype (SASP) cytokines are not. Senescent beta cells in culture and in human tissue show elevated levels of cytoplasmic DNA, contributing to their increased interferon responsiveness. Human beta-cell senescence thus involves chromatin-driven upregulation of a functional-maturation program, and increased responsiveness of interferon-stimulated genes, changes that could increase both insulin secretion and immune reactivity.

HumanIslets: An integrated platform for human islet data access and analysis

Comprehensive molecular and cellular phenotyping of human islets can enable deep mechanistic insights for diabetes research. We established the Human Islet Data Analysis and Sharing (HI-DAS) consortium to advance goals in accessibility, usability, and integration of data from human islets isolated from donors with and without diabetes at the Alberta Diabetes Institute (ADI) IsletCore. Here we introduce HumanIslets.com, an open resource for the research community. This platform, which presently includes data on 547 human islet donors, allows users to access linked datasets describing molecular profiles, islet function and donor phenotypes, and to perform various statistical and functional analyses at the donor, islet and single-cell levels. As an example of the analytic capacity of this resource we show a dissociation between cell culture effects on transcript and protein expression, and an approach to correct for exocrine contamination found in hand-picked islets. Finally, we provide an example workflow and visualization that highlights links between type 2 diabetes status, SERCA3b Ca2+-ATPase levels at the transcript and protein level, insulin secretion and islet cell phenotypes. HumanIslets.com provides a growing and adaptable set of resources and tools to support the metabolism and diabetes research community.

Mesenchymal stromal cells ameliorate mitochondrial dysfunction in α cells and hyperglucagonemia in type 2 diabetes via SIRT1/FoxO3a signaling

Dysregulation of α cells results in hyperglycemia and hyperglucagonemia in type 2 diabetes mellitus (T2DM). Mesenchymal stromal cell (MSC)-based therapy increases oxygen consumption of islets and enhances insulin secretion. However, the underlying mechanism for the protective role of MSCs in α-cell mitochondrial dysfunction remains unclear. Here, human umbilical cord MSCs (hucMSCs) were used to treat 2 kinds of T2DM mice and αTC1-6 cells to explore the role of hucMSCs in improving α-cell mitochondrial dysfunction and hyperglucagonemia. Plasma and supernatant glucagon were detected by enzyme-linked immunosorbent assay (ELISA). Mitochondrial function of α cells was assessed by the Seahorse Analyzer. To investigate the underlying mechanisms, Sirtuin 1 (SIRT1), Forkhead box O3a (FoxO3a), glucose transporter type1 (GLUT1), and glucokinase (GCK) were assessed by Western blotting analysis. In vivo, hucMSC infusion improved glucose and insulin tolerance, as well as hyperglycemia and hyperglucagonemia in T2DM mice. Meanwhile, hucMSC intervention rescued the islet structure and decreased α- to β-cell ratio. Glucagon secretion from αTC1-6 cells was consistently inhibited by hucMSCs in vitro. Meanwhile, hucMSC treatment activated intracellular SIRT1/FoxO3a signaling, promoted glucose uptake and activation, alleviated mitochondrial dysfunction, and enhanced ATP production. However, transfection of SIRT1 small interfering RNA (siRNA) or the application of SIRT1 inhibitor EX-527 weakened the therapeutic effects of hucMSCs on mitochondrial function and glucagon secretion. Our observations indicate that hucMSCs mitigate mitochondrial dysfunction and glucagon hypersecretion of α cells in T2DM via SIRT1/FoxO3a signaling, which provides novel evidence demonstrating the potential for hucMSCs in treating T2DM.

Single cell regulatory architecture of human pancreatic islets suggests sex differences in β cell function and the pathogenesis of type 2 diabetes

Biological sex affects the pathogenesis of type 2 and type 1 diabetes (T2D, T1D) including the development of β cell failure observed more often in males. The mechanisms that drive sex differences in β cell failure is unknown. Studying sex differences in islet regulation and function represent a unique avenue to understand the sex-specific heterogeneity in β cell failure in diabetes. Here, we examined sex and race differences in human pancreatic islets from up to 52 donors with and without T2D (including 37 donors from the Human Pancreas Analysis Program [HPAP] dataset) using an orthogonal series of experiments including single cell RNA-seq (scRNA-seq), single nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq), dynamic hormone secretion, and bioenergetics. In cultured islets from nondiabetic (ND) donors, in the absence of the in vivo hormonal environment, sex differences in islet cell type gene accessibility and expression predominantly involved sex chromosomes. Of particular interest were sex differences in the X-linked KDM6A and Y-linked KDM5D chromatin remodelers in female and male islet cells respectively. Islets from T2D donors exhibited similar sex differences in differentially expressed genes (DEGs) from sex chromosomes. However, in contrast to islets from ND donors, islets from T2D donors exhibited major sex differences in DEGs from autosomes. Comparing β cells from T2D and ND donors revealed that females had more DEGs from autosomes compared to male β cells. Gene set enrichment analysis of female β cell DEGs showed a suppression of oxidative phosphorylation and electron transport chain pathways, while male β cell had suppressed insulin secretion pathways. Thus, although sex-specific differences in gene accessibility and expression of cultured ND human islets predominantly affect sex chromosome genes, major differences in autosomal gene expression between sexes appear during the transition to T2D and which highlight mitochondrial failure in female β cells.

SLIVER: Unveiling large scale gene regulatory networks of single-cell transcriptomic data through causal structure learning and modules aggregation

Prevalent Gene Regulatory Network (GRN) construction methods rely on generalized correlation analysis. However, in biological systems, regulation is essentially a causal relationship that cannot be adequately captured solely through correlation. Therefore, it is more reasonable to infer GRNs from a causal perspective. Existing causal discovery algorithms typically rely on Directed Acyclic Graphs (DAGs) to model causal relationships, but it often requires traversing the entire network, which result in computational demands skyrocketing as the number of nodes grows and make causal discovery algorithms only suitable for small networks with one or two hundred nodes or fewer. In this study, we propose the SLIVER (cauSaL dIscovery Via dimEnsionality Reduction) algorithm which integrates causal structural equation model and graph decomposition. SLIVER introduces a set of factor nodes, serving as abstractions of different functional modules to integrate the regulatory relationships between genes based on their respective functions or pathways, thus reducing the GRN to the product of two low-dimensional matrices. Subsequently, we employ the structural causal model (SCM) to learn the GRN within the gene node space, enforce the DAG constraint in the low-dimensional space, and guide each factor to aggregate various functions through cosine similarity. We evaluate the performance of the SLIVER algorithm on 12 real single cell transcriptomic datasets, and demonstrate it outperforms other 12 widely used methods both in GRN inference performance and computational resource usage. The analysis of the gene information integrated by factor nodes also demonstrate the biological explanation of factor nodes in GRNs. We apply it to scRNA-seq of Type 2 diabetes mellitus to capture the transcriptional regulatory structural changes of β cells under high insulin demand.

Insulin reduces endoplasmic reticulum stress-induced apoptosis by decreasing mitochondrial hyperpolarization and caspase-12 in INS-1 pancreatic β-cells

Pancreatic β-cell mass is a critical determinant of insulin secretion. Severe endoplasmic reticulum (ER) stress causes β-cell apoptosis; however, the mechanisms of progression and suppression are not yet fully understood. Here, we report that the autocrine/paracrine function of insulin reduces ER stress-induced β-cell apoptosis. Insulin reduced the ER-stress inducer tunicamycin- and thapsigargin-induced cell viability loss due to apoptosis in INS-1 β-cells. Moreover, the effect of insulin was greater than that of insulin-like growth factor-1 at physiologically relevant concentrations. Insulin did not attenuate the ER stress-induced increase in unfolded protein response genes. ER stress did not induce cytochrome c release from mitochondria. Mitochondrial hyperpolarization was induced by ER stress and prevented by insulin. The protonophore/mitochondrial oxidative phosphorylation uncoupler, but not the antioxidants N-acetylcysteine and α-tocopherol, exhibited potential cytoprotection during ER stress. Both procaspase-12 and cleaved caspase-12 levels increased under ER stress. The caspase-12 inhibitor Z-ATAD-FMK decreased ER stress-induced apoptosis. Caspase-12 overexpression reduced cell viability, which was diminished in the presence of insulin. Insulin decreased caspase-12 levels at the post-translational stages. These results demonstrate that insulin protects against ER stress-induced β-cell apoptosis in this cell line. Furthermore, mitochondrial hyperpolarization and increased caspase-12 levels are involved in ER stress-induced and insulin-suppressed β-cell apoptosis.

Differential CpG methylation at Nnat in the early establishment of beta cell heterogeneity

AIMS/HYPOTHESIS

Beta cells within the pancreatic islet represent a heterogenous population wherein individual sub-groups of cells make distinct contributions to the overall control of insulin secretion. These include a subpopulation of highly connected 'hub' cells, important for the propagation of intercellular Ca2+ waves. Functional subpopulations have also been demonstrated in human beta cells, with an altered subtype distribution apparent in type 2 diabetes. At present, the molecular mechanisms through which beta cell hierarchy is established are poorly understood. Changes at the level of the epigenome provide one such possibility, which we explore here by focusing on the imprinted gene Nnat (encoding neuronatin [NNAT]), which is required for normal insulin synthesis and secretion.

METHODS

Single-cell RNA-seq datasets were examined using Seurat 4.0 and ClusterProfiler running under R. Transgenic mice expressing enhanced GFP under the control of the Nnat enhancer/promoter regions were generated for FACS of beta cells and downstream analysis of CpG methylation by bisulphite sequencing and RNA-seq, respectively. Animals deleted for the de novo methyltransferase DNA methyltransferase 3 alpha (DNMT3A) from the pancreatic progenitor stage were used to explore control of promoter methylation. Proteomics was performed using affinity purification mass spectrometry and Ca2+ dynamics explored by rapid confocal imaging of Cal-520 AM and Cal-590 AM. Insulin secretion was measured using homogeneous time-resolved fluorescence imaging.

RESULTS

Nnat mRNA was differentially expressed in a discrete beta cell population in a developmental stage- and DNA methylation (DNMT3A)-dependent manner. Thus, pseudo-time analysis of embryonic datasets demonstrated the early establishment of Nnat-positive and -negative subpopulations during embryogenesis. NNAT expression is also restricted to a subset of beta cells across the human islet that is maintained throughout adult life. NNAT+ beta cells also displayed a discrete transcriptome at adult stages, representing a subpopulation specialised for insulin production, and were diminished in db/db mice. 'Hub' cells were less abundant in the NNAT+ population, consistent with epigenetic control of this functional specialisation.

CONCLUSIONS/INTERPRETATION

These findings demonstrate that differential DNA methylation at Nnat represents a novel means through which beta cell heterogeneity is established during development. We therefore hypothesise that changes in methylation at this locus may contribute to a loss of beta cell hierarchy and connectivity, potentially contributing to defective insulin secretion in some forms of diabetes.

DATA AVAILABILITY

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD048465.

Enhancing β-cell function and identity in type 2 diabetes: The protective role of Coptis deltoidea C. Y. Cheng et Hsiao via glucose metabolism modulation and AMPK signaling activation

BACKGROUND

Abnormalities in glucose metabolism may be the underlying cause of β-cell dysfunction and identity impairment resulting from high glucose exposure. In China, Coptis deltoidea C. Y. Cheng et Hsiao (YL) has demonstrated remarkable hypoglycemic effects.

HYPOTHESIS/PURPOSE

To investigate the hypoglycemic effect of YL and determine the mechanism of YL in treating diabetes.

METHODS

A type 2 diabetes mouse model was used to investigate the pharmacodynamics of YL. YL was administrated once daily for 8 weeks. The hypoglycemic effect of YL was assessed by fasting blood glucose, an oral glucose tolerance test, insulin levels, and other indexes. The underlying mechanism of YL was examined by targeting glucose metabolomics, western blotting, and qRT-PCR. Subsequently, the binding capacity between predicted AMP-activated protein kinase (AMPK) and important components of YL (Cop, Ber, and Epi) were validated by molecular docking and surface plasmon resonance. Then, in AMPK knockdown MIN6 cells, the mechanisms of Cop, Ber, and Epi were inversely confirmed through evaluations encompassing glucose-stimulated insulin secretion, markers indicative of β-cell identity, and the examination of glycolytic genes and products.

RESULTS

YL (0.9 g/kg) treatment exerted notable hypoglycemic effects and protected the structural integrity and identity of pancreatic β-cells. Metabolomic analysis revealed that YL inhibited the hyperactivated glycolysis pathway in diabetic mice, thereby regulating the products of the tricarboxylic acid cycle. KEGG enrichment revealed the intimate relationship of this process with the AMPK signaling pathway. Cop, Ber, and Epi in YL displayed high binding affinities for AMPK protein. These compounds played a pivotal role in preserving the identity of pancreatic β-cells and amplifying insulin secretion. The mechanism underlying this process involved inhibition of glucose uptake, lowering intracellular lactate levels, and elevating acetyl coenzyme A and ATP levels through AMPK signaling. The use of a glycolytic inhibitor corroborated that attenuation of glycolysis restored β-cell identity and function.

CONCLUSION

YL demonstrates significant hypoglycemic efficacy. We elucidated the potential mechanisms underlying the protective effects of YL and its active constituents on β-cell function and identity by observing glucose metabolism processes in pancreatic tissue and cells. In this intricate process, AMPK plays a pivotal regulatory role.

SIMBA: single-cell embedding along with features

Most current single-cell analysis pipelines are limited to cell embeddings and rely heavily on clustering, while lacking the ability to explicitly model interactions between different feature types. Furthermore, these methods are tailored to specific tasks, as distinct single-cell problems are formulated differently. To address these shortcomings, here we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin-accessible regions and DNA sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal and omics data integration. We show that SIMBA provides a single framework that allows diverse single-cell problems to be formulated in a unified way and thus simplifies the development of new analyses and extension to new single-cell modalities. SIMBA is implemented as a comprehensive Python library ( https://simba-bio.readthedocs.io ).

A single-cell strategy for the identification of intronic variants related to mis-splicing in pancreatic cancer

Most clinical diagnostic and genomic research setups focus almost exclusively on coding regions and essential splice sites, thereby overlooking other non-coding variants. As a result, intronic variants that can promote mis-splicing events across a range of diseases, including cancer, are yet to be systematically investigated. Such investigations would require both genomic and transcriptomic data, but there currently exist very few datasets that satisfy these requirements. We address this by developing a single-nucleus full-length RNA-sequencing approach that allows for the detection of potentially pathogenic intronic variants. We exemplify the potency of our approach by applying pancreatic cancer tumor and tumor-derived specimens and linking intronic variants to splicing dysregulation. We specifically find that prominent intron retention and pseudo-exon activation events are shared by the tumors and affect genes encoding key transcriptional regulators. Our work paves the way for the assessment and exploitation of intronic mutations as powerful prognostic markers and potential therapeutic targets in cancer.

Advancing Diabetes Research: A Novel Islet Isolation Method from Living Donors

Pancreatic islet isolation is critical for type 2 diabetes research. Although -omics approaches have shed light on islet molecular profiles, inconsistencies persist; on the other hand, functional studies are essential, but they require reliable and standardized isolation methods. Here, we propose a simplified protocol applied to very small-sized samples collected from partially pancreatectomized living donors. Islet isolation was performed by digesting tissue specimens collected during surgery within a collagenase P solution, followed by a Lympholyte density gradient separation; finally, functional assays and staining with dithizone were carried out. Isolated pancreatic islets exhibited functional responses to glucose and arginine stimulation mirroring donors' metabolic profiles, with insulin secretion significantly decreasing in diabetic islets compared to non-diabetic islets; conversely, proinsulin secretion showed an increasing trend from non-diabetic to diabetic islets. This novel islet isolation method from living patients undergoing partial pancreatectomy offers a valuable opportunity for targeted study of islet physiology, with the primary advantage of being time-effective and successfully preserving islet viability and functionality. It enables the generation of islet preparations that closely reflect donors' clinical profiles, simplifying the isolation process and eliminating the need for a Ricordi chamber. Thus, this method holds promises for advancing our understanding of diabetes and for new personalized pharmacological approaches.

Improving replicability in single-cell RNA-Seq cell type discovery with Dune

BACKGROUND

Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable.

RESULTS

Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html .

CONCLUSIONS

Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

scPLAN: a hierarchical computational framework for single transcriptomics data annotation, integration and cell-type label refinement

MOTIVATION

In the past decade, single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal method for transcriptomic profiling in biomedical research. Precise cell-type identification is crucial for subsequent analysis of single-cell data. And the integration and refinement of annotated data are essential for building comprehensive databases. However, prevailing annotation techniques often overlook the hierarchical organization of cell types, resulting in inconsistent annotations. Meanwhile, most existing integration approaches fail to integrate datasets with different annotation depths and none of them can enhance the labels of outdated data with lower annotation resolutions using more intricately annotated datasets or novel biological findings.

RESULTS

Here, we introduce scPLAN, a hierarchical computational framework designed for scRNA-seq data analysis. scPLAN excels in annotating unlabeled scRNA-seq data using a reference dataset structured along a hierarchical cell-type tree. It identifies potential novel cell types in a systematic, layer-by-layer manner. Additionally, scPLAN effectively integrates annotated scRNA-seq datasets with varying levels of annotation depth, ensuring consistent refinement of cell-type labels across datasets with lower resolutions. Through extensive annotation and novel cell detection experiments, scPLAN has demonstrated its efficacy. Two case studies have been conducted to showcase how scPLAN integrates datasets with diverse cell-type label resolutions and refine their cell-type labels.

AVAILABILITY

https://github.com/michaelGuo1204/scPLAN.

Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data

Numerous single-cell transcriptomic datasets from identical tissues or cell lines are generated from different laboratories or single-cell RNA sequencing (scRNA-seq) protocols. The denoising of these datasets to eliminate batch effects is crucial for data integration, ensuring accurate interpretation and comprehensive analysis of biological questions. Although many scRNA-seq data integration methods exist, most are inefficient and/or not conducive to downstream analysis. Here, DeepBID, a novel deep learning-based method for batch effect correction, non-linear dimensionality reduction, embedding, and cell clustering concurrently, is introduced. DeepBID utilizes a negative binomial-based autoencoder with dual Kullback-Leibler divergence loss functions, aligning cell points from different batches within a consistent low-dimensional latent space and progressively mitigating batch effects through iterative clustering. Extensive validation on multiple-batch scRNA-seq datasets demonstrates that DeepBID surpasses existing tools in removing batch effects and achieving superior clustering accuracy. When integrating multiple scRNA-seq datasets from patients with Alzheimer's disease, DeepBID significantly improves cell clustering, effectively annotating unidentified cells, and detecting cell-specific differentially expressed genes.

Unraveling the significance of PPP1R1A gene in pancreatic β-cell function: A study in INS-1 cells and human pancreatic islets

BACKGROUND AND AIMS

The protein phosphatase 1 regulatory inhibitor subunit 1A (PPP1R1A) has been linked with insulin secretion and diabetes mellitus. Yet, its full significance in pancreatic β-cell function remains unclear. This study aims to elucidate the role of the PPP1R1A gene in β-cell biology using human pancreatic islets and rat INS-1 (832/13) cells.

RESULTS

Disruption of Ppp1r1a in INS-1 cells was associated with reduced insulin secretion and impaired glucose uptake; however, cell viability, ROS, apoptosis or proliferation were intact. A significant downregulation of crucial β-cell function genes such as Ins1, Ins2, Pcsk1, Cpe, Pdx1, Mafa, Isl1, Glut2, Snap25, Vamp2, Syt5, Cacna1a, Cacna1d and Cacnb3, was observed upon Ppp1r1a disruption. Furthermore, silencing Pdx1 in INS-1 cells altered PPP1R1A expression, indicating that PPP1R1A is a target gene for PDX1. Treatment with rosiglitazone increased Ppp1r1a expression, while metformin and insulin showed no effect. RNA-seq analysis of human islets revealed high PPP1R1A expression, with α-cells showing the highest levels compared to other endocrine cells. Muscle tissues exhibited greater PPP1R1A expression than pancreatic islets, liver, or adipose tissues. Co-expression analysis revealed significant correlations between PPP1R1A and genes associated with insulin biosynthesis, exocytosis machinery, and intracellular calcium transport. Overexpression of PPP1R1A in human islets augmented insulin secretion and upregulated protein expression of Insulin, MAFA, PDX1, and GLUT1, while silencing of PPP1R1A reduced Insulin, MAFA, and GLUT1 protein levels.

CONCLUSION

This study provides valuable insights into the role of PPP1R1A in regulating β-cell function and glucose homeostasis. PPP1R1A presents a promising opportunity for future therapeutic interventions.

Acinar to β-like cell conversion through inhibition of focal adhesion kinase

Insufficient functional β-cell mass causes diabetes; however, an effective cell replacement therapy for curing diabetes is currently not available. Reprogramming of acinar cells toward functional insulin-producing cells would offer an abundant and autologous source of insulin-producing cells. Our lineage tracing studies along with transcriptomic characterization demonstrate that treatment of adult mice with a small molecule that specifically inhibits kinase activity of focal adhesion kinase results in trans-differentiation of a subset of peri-islet acinar cells into insulin producing β-like cells. The acinar-derived insulin-producing cells infiltrate the pre-existing endocrine islets, partially restore β-cell mass, and significantly improve glucose homeostasis in diabetic mice. These findings provide evidence that inhibition of the kinase activity of focal adhesion kinase can convert acinar cells into insulin-producing cells and could offer a promising strategy for treating diabetes.

GLP1R and GIPR expression and signaling in pancreatic alpha cells, beta cells and delta cells

Glucagon-like peptide-1 receptor (GLP1R) and glucose-dependent insulinotropic polypeptide receptor (GIPR) are transmembrane receptors involved in insulin, glucagon and somatostatin secretion from the pancreatic islet. Therapeutic targeting of GLP1R and GIPR restores blood glucose levels in part by influencing beta cell, alpha cell and delta cell function. Despite the importance of the incretin-mimetics for diabetes therapy, our understanding of GLP1R and GIPR expression patterns and signaling within the islet remain incomplete. Here, we present the evidence for GLP1R and GIPR expression in the major islet cell types, before addressing signaling pathway(s) engaged, as well as their influence on cell survival and function. While GLP1R is largely a beta cell-specific marker within the islet, GIPR is expressed in alpha cells, beta cells, and (possibly) delta cells. GLP1R and GIPR engage Gs-coupled pathways in most settings, although the exact outcome on hormone release depends on paracrine communication and promiscuous signaling. Biased agonism away from beta-arrestin is an emerging concept for improving therapeutic efficacy, and is also relevant for GLP1R/GIPR dual agonism. Lastly, dual agonists exert multiple effects on islet function through GIPR > GLP1R imbalance, increased GLP1R surface expression and cAMP signaling, as well as beneficial alpha cell-beta cell-delta cell crosstalk.

Cell-Surface ZnT8 Antibody Prevents and Reverses Autoimmune Diabetes in Mice

Type 1 diabetes (T1D) is an autoimmune disease in which pathogenic lymphocytes target autoantigens expressed in pancreatic islets, leading to the destruction of insulin-producing β-cells. Zinc transporter 8 (ZnT8) is a major autoantigen abundantly present on the β-cell surface. This unique molecular target offers the potential to shield β-cells against autoimmune attacks in T1D. Our previous work showed that a monoclonal antibody (mAb43) against cell-surface ZnT8 could home in on pancreatic islets and prevent autoantibodies from recognizing β-cells. This study demonstrates that mAb43 binds to exocytotic sites on the β-cell surface, masking the antigenic exposure of ZnT8 and insulin after glucose-stimulated insulin secretion. In vivo administration of mAb43 to NOD mice selectively increased the proportion of regulatory T cells in the islet, resulting in complete and sustained protection against T1D onset as well as reversal of new-onset diabetes. The mAb43-induced self-tolerance was reversible after treatment cessation, and no adverse effects were exhibited during long-term monitoring. Our findings suggest that mAb43 masking of the antigenic exposure of β-cells suppresses the immunological cascade from B-cell antigen presentation to T cell-mediated β-cell destruction, providing a novel islet-targeted and antigen-specific immunotherapy to prevent and reverse clinical T1D.

ARTICLE HIGHLIGHTS

NKX2-2 based nuclei sorting on frozen human archival pancreas enables the enrichment of islet endocrine populations for single-nucleus RNA sequencing

BACKGROUND

Current approaches to profile the single-cell transcriptomics of human pancreatic endocrine cells almost exclusively rely on freshly isolated islets. However, human islets are limited in availability. Furthermore, the extensive processing steps during islet isolation and subsequent single cell dissolution might alter gene expressions. In this work, we report the development of a single-nucleus RNA sequencing (snRNA-seq) approach with targeted islet cell enrichment for endocrine-population focused transcriptomic profiling using frozen archival pancreatic tissues without islet isolation.

RESULTS

We cross-compared five nuclei isolation protocols and selected the citric acid method as the best strategy to isolate nuclei with high RNA integrity and low cytoplasmic contamination from frozen archival human pancreata. We innovated fluorescence-activated nuclei sorting based on the positive signal of NKX2-2 antibody to enrich nuclei of the endocrine population from the entire nuclei pool of the pancreas. Our sample preparation procedure generated high-quality single-nucleus gene-expression libraries while preserving the endocrine population diversity. In comparison with single-cell RNA sequencing (scRNA-seq) library generated with live cells from freshly isolated human islets, the snRNA-seq library displayed comparable endocrine cellular composition and cell type signature gene expression. However, between these two types of libraries, differential enrichments of transcripts belonging to different functional classes could be observed.

CONCLUSIONS

Our work fills a technological gap and helps to unleash frozen archival pancreatic tissues for molecular profiling targeting the endocrine population. This study opens doors to retrospective mappings of endocrine cell dynamics in pancreatic tissues of complex histopathology. We expect that our protocol is applicable to enrich nuclei for transcriptomics studies from various populations in different types of frozen archival tissues.

DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation

Cell deconvolution is the estimation of cell type fractions and cell type-specific gene expression from mixed data. An unmet challenge in cell deconvolution is the scarcity of realistic training data and the domain shift often observed in synthetic training data. Here, we show that two novel deep neural networks with simultaneous consistency regularization of the target and training domains significantly improve deconvolution performance. Our algorithm, DISSECT, outperforms competing algorithms in cell fraction and gene expression estimation by up to 14 percentage points. DISSECT can be easily adapted to other biomedical data types, as exemplified by our proteomic deconvolution experiments.

Pancreatic β-Cell Identity Change through the Lens of Single-Cell Omics Research

The main hallmark in the development of both type 1 and type 2 diabetes is a decline in functional β-cell mass. This decline is predominantly attributed to β-cell death, although recent findings suggest that the loss of β-cell identity may also contribute to β-cell dysfunction. This phenomenon is characterized by a reduced expression of key markers associated with β-cell identity. This review delves into the insights gained from single-cell omics research specifically focused on β-cell identity. It highlights how single-cell omics based studies have uncovered an unexpected level of heterogeneity among β-cells and have facilitated the identification of distinct β-cell subpopulations through the discovery of cell surface markers, transcriptional regulators, the upregulation of stress-related genes, and alterations in chromatin activity. Furthermore, specific subsets of β-cells have been identified in diabetes, such as displaying an immature, dedifferentiated gene signature, expressing significantly lower insulin mRNA levels, and expressing increased β-cell precursor markers. Additionally, single-cell omics has increased insight into the detrimental effects of diabetes-associated conditions, including endoplasmic reticulum stress, oxidative stress, and inflammation, on β-cell identity. Lastly, this review outlines the factors that may influence the identification of β-cell subpopulations when designing and performing a single-cell omics experiment.

LDHB contributes to the regulation of lactate levels and basal insulin secretion in human pancreatic β cells

Using 13C6 glucose labeling coupled to gas chromatography-mass spectrometry and 2D 1H-13C heteronuclear single quantum coherence NMR spectroscopy, we have obtained a comparative high-resolution map of glucose fate underpinning β cell function. In both mouse and human islets, the contribution of glucose to the tricarboxylic acid (TCA) cycle is similar. Pyruvate fueling of the TCA cycle is primarily mediated by the activity of pyruvate dehydrogenase, with lower flux through pyruvate carboxylase. While the conversion of pyruvate to lactate by lactate dehydrogenase (LDH) can be detected in islets of both species, lactate accumulation is 6-fold higher in human islets. Human islets express LDH, with low-moderate LDHA expression and β cell-specific LDHB expression. LDHB inhibition amplifies LDHA-dependent lactate generation in mouse and human β cells and increases basal insulin release. Lastly, cis-instrument Mendelian randomization shows that low LDHB expression levels correlate with elevated fasting insulin in humans. Thus, LDHB limits lactate generation in β cells to maintain appropriate insulin release.

Leveraging cross-source heterogeneity to improve the performance of bulk gene expression deconvolution

A main limitation of bulk transcriptomic technologies is that individual measurements normally contain contributions from multiple cell populations, impeding the identification of cellular heterogeneity within diseased tissues. To extract cellular insights from existing large cohorts of bulk transcriptomic data, we present CSsingle, a novel method designed to accurately deconvolve bulk data into a predefined set of cell types using a scRNA-seq reference. Through comprehensive benchmark evaluations and analyses using diverse real data sets, we reveal the systematic bias inherent in existing methods, stemming from differences in cell size or library size. Our extensive experiments demonstrate that CSsingle exhibits superior accuracy and robustness compared to leading methods, particularly when dealing with bulk mixtures originating from cell types of markedly different cell sizes, as well as when handling bulk and single-cell reference data obtained from diverse sources. Our work provides an efficient and robust methodology for the integrated analysis of bulk and scRNA-seq data, facilitating various biological and clinical studies.

The Importance of Intra-Islet Communication in the Function and Plasticity of the Islets of Langerhans during Health and Diabetes

Islets of Langerhans are anatomically dispersed within the pancreas and exhibit regulatory coordination between islets in response to nutritional and inflammatory stimuli. However, within individual islets, there is also multi-faceted coordination of function between individual beta-cells, and between beta-cells and other endocrine and vascular cell types. This is mediated partly through circulatory feedback of the major secreted hormones, insulin and glucagon, but also by autocrine and paracrine actions within the islet by a range of other secreted products, including somatostatin, urocortin 3, serotonin, glucagon-like peptide-1, acetylcholine, and ghrelin. Their availability can be modulated within the islet by pericyte-mediated regulation of microvascular blood flow. Within the islet, both endocrine progenitor cells and the ability of endocrine cells to trans-differentiate between phenotypes can alter endocrine cell mass to adapt to changed metabolic circumstances, regulated by the within-islet trophic environment. Optimal islet function is precariously balanced due to the high metabolic rate required by beta-cells to synthesize and secrete insulin, and they are susceptible to oxidative and endoplasmic reticular stress in the face of high metabolic demand. Resulting changes in paracrine dynamics within the islets can contribute to the emergence of Types 1, 2 and gestational diabetes.

Single-Cell RNA Sequencing Profiling Cellular Heterogeneity and Specific Responses of Fish Gills to Microplastics and Nanoplastics

Fish gills are highly sensitive organs for microplastic (MP) and nanoplastic (NP) invasions, but the cellular heterogeneity of fish gills to MPs and NPs remains largely unknown. We employed single-cell RNA sequencing to investigate the responses of individual cell populations in tilapia Oreochromis niloticus gills to MP and NP exposure at an environmentally relevant concentration. Based on the detected differentially expressed gene (DEG) numbers, the most affected immune cells by MP exposure were macrophages, while the stimulus of NPs primarily targeted T cells. In response to MPs and NPs, H+-ATPase-rich cells exhibited distinct changes as compared with Na+/K+-ATPase-rich cells and pavement cells. Fibroblasts were identified as a potential sensitive cell-type biomarker for MP interaction with O. niloticus gills, as evidenced by the largely reduced cell counts and the mostly detected DEGs among the 12 identified cell populations. The most MP-sensitive fibroblast subpopulation in O. niloticus gills was lipofibroblasts. Cell-cell communications between fibroblasts and H+-ATPase-rich cells, neurons, macrophages, neuroepithelial cells, and Na+/K+-ATPase-rich cells in O. niloticus gills were significantly inhibited by MP exposure. Collectively, our study demonstrated the cellular heterogeneity of O. niloticus gills to MPs and NPs and provided sensitive markers for their toxicological mechanisms at single-cell resolution.

Distribution-Agnostic Deep Learning Enables Accurate Single-Cell Data Recovery and Transcriptional Regulation Interpretation

Single-cell RNA sequencing (scRNA-seq) is a robust method for studying gene expression at the single-cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expression values. Existing imputation methods rely on strict data assumptions, limiting their broader application, and lack reliable supervision, leading to biased signal recovery. To address these challenges, authors developed Bis, a distribution-agnostic deep learning model for accurately recovering missing sing-cell gene expression from multiple platforms. Bis is an optimal transport-based autoencoder model that can capture the intricate distribution of scRNA-seq data while addressing the characteristic sparsity by regularizing the cellular embedding space. Additionally, they propose a module using bulk RNA-seq data to guide reconstruction and ensure expression consistency. Experimental results show Bis outperforms other models across simulated and real datasets, showcasing superiority in various downstream analyses including batch effect removal, clustering, differential expression analysis, and trajectory inference. Moreover, Bis successfully restores gene expression levels in rare cell subsets in a tumor-matched peripheral blood dataset, revealing developmental characteristics of cytokine-induced natural killer cells within a head and neck squamous cell carcinoma microenvironment.

Single-cell sequencing: A promising approach for uncovering the characteristic of pancreatic islet cells in type 2 diabetes

Single-cell sequencing is a novel and rapidly advancing high-throughput technique that can be used to investigating genomics, transcriptomics, and epigenetics at a single-cell level. Currently, single-cell sequencing can not only be used to draw the pancreatic islet cells map and uncover the characteristics of cellular heterogeneity in type 2 diabetes, but can also be used to label and purify functional beta cells in pancreatic stem cells, improving stem cells and islet organoids therapies. In addition, this technology helps to analyze islet cell dedifferentiation and can be applied to the treatment of type 2 diabetes. In this review, we summarize the development and process of single-cell sequencing, describe the potential applications of single-cell sequencing in the field of type 2 diabetes, and discuss the prospects and limitations of single-cell sequencing to provide a new direction for exploring the pathogenesis of type 2 diabetes and finding therapeutic targets.

High-Throughput Methods for the Discovery of Small Molecule Modulators of Pancreatic Beta-Cell Function and Regeneration

The progression of type II diabetes (T2D) is characterized by a complex and highly variable loss of beta-cell mass, resulting in impaired insulin secretion. Many T2D drug discovery efforts aimed at discovering molecules that can protect or restore beta-cell mass and function have been developed using limited beta-cell lines and primary rodent/human pancreatic islets. Various high-throughput screening methods have been used in the context of drug discovery, including luciferase-based reporter assays, glucose-stimulated insulin secretion, and high-content screening. In this context, a cornerstone of small molecule discovery has been the use of immortalized rodent beta-cell lines. Although insightful, this usage has led to a more comprehensive understanding of rodent beta-cell proliferation pathways rather than their human counterparts. Advantages gained in enhanced physiological relevance are offered by three-dimensional (3D) primary islets and pseudoislets in contrast to monolayer cultures, but these approaches have been limited to use in low-throughput experiments. Emerging methods, such as high-throughput 3D islet imaging coupled with machine learning, aim to increase the feasibility of integrating 3D microtissue structures into high-throughput screening. This review explores the current methods used in high-throughput screening for small molecule modulators of beta-cell mass and function, a potentially pivotal strategy for diabetes drug discovery.

Cytidine Deaminase Resolves Replicative Stress and Protects Pancreatic Cancer from DNA-Targeting Drugs

Cytidine deaminase (CDA) functions in the pyrimidine salvage pathway for DNA and RNA syntheses and has been shown to protect cancer cells from deoxycytidine-based chemotherapies. In this study, we observed that CDA was overexpressed in pancreatic adenocarcinoma from patients at baseline and was essential for experimental tumor growth. Mechanistic investigations revealed that CDA localized to replication forks where it increased replication speed, improved replication fork restart efficiency, reduced endogenous replication stress, minimized DNA breaks, and regulated genetic stability during DNA replication. In cellular pancreatic cancer models, high CDA expression correlated with resistance to DNA-damaging agents. Silencing CDA in patient-derived primary cultures in vitro and in orthotopic xenografts in vivo increased replication stress and sensitized pancreatic adenocarcinoma cells to oxaliplatin. This study sheds light on the role of CDA in pancreatic adenocarcinoma, offering insights into how this tumor type modulates replication stress. These findings suggest that CDA expression could potentially predict therapeutic efficacy and that targeting CDA induces intolerable levels of replication stress in cancer cells, particularly when combined with DNA-targeted therapies.

SIGNIFICANCE

Cytidine deaminase reduces replication stress and regulates DNA replication to confer resistance to DNA-damaging drugs in pancreatic cancer, unveiling a molecular vulnerability that could enhance treatment response.

scDAC: deep adaptive clustering of single-cell transcriptomic data with coupled autoencoder and Dirichlet process mixture model

MOTIVATION

Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is an important step in revealing cellular heterogeneity. Many clustering methods have been proposed to discover heterogenous cell types from scRNA-seq data. However, adaptive clustering with accurate cluster number reflecting intrinsic biology nature from large-scale scRNA-seq data remains quite challenging.

RESULTS

Here, we propose a single-cell Deep Adaptive Clustering (scDAC) model by coupling the Autoencoder (AE) and the Dirichlet Process Mixture Model (DPMM). By jointly optimizing the model parameters of AE and DPMM, scDAC achieves adaptive clustering with accurate cluster numbers on scRNA-seq data. We verify the performance of scDAC on five subsampled datasets with different numbers of cell types and compare it with 15 widely used clustering methods across nine scRNA-seq datasets. Our results demonstrate that scDAC can adaptively find accurate numbers of cell types or subtypes and outperforms other methods. Moreover, the performance of scDAC is robust to hyperparameter changes.

AVAILABILITY AND IMPLEMENTATION

The scDAC is implemented in Python. The source code is available at https://github.com/labomics/scDAC.

CATD: a reproducible pipeline for selecting cell-type deconvolution methods across tissues

Motivation

Cell-type deconvolution methods aim to infer cell composition from bulk transcriptomic data. The proliferation of developed methods coupled with inconsistent results obtained in many cases, highlights the pressing need for guidance in the selection of appropriate methods. Additionally, the growing accessibility of single-cell RNA sequencing datasets, often accompanied by bulk expression from related samples enable the benchmark of existing methods.

Results

In this study, we conduct a comprehensive assessment of 31 methods, utilizing single-cell RNA-sequencing data from diverse human and mouse tissues. Employing various simulation scenarios, we reveal the efficacy of regression-based deconvolution methods, highlighting their sensitivity to reference choices. We investigate the impact of bulk-reference differences, incorporating variables such as sample, study and technology. We provide validation using a gold standard dataset from mononuclear cells and suggest a consensus prediction of proportions when ground truth is not available. We validated the consensus method on data from the stomach and studied its spillover effect. Importantly, we propose the use of the critical assessment of transcriptomic deconvolution (CATD) pipeline which encompasses functionalities for generating references and pseudo-bulks and running implemented deconvolution methods. CATD streamlines simultaneous deconvolution of numerous bulk samples, providing a practical solution for speeding up the evaluation of newly developed methods.

Availability and implementation

https://github.com/Papatheodorou-Group/CATD_snakemake.

Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation

BACKGROUND

The integration of single-cell RNA sequencing data from multiple experimental batches and diverse biological conditions holds significant importance in the study of cellular heterogeneity.

RESULTS

To expedite the exploration of systematic disparities under various biological contexts, we propose a scRNA-seq integration method called scDisco, which involves a domain-adaptive decoupling representation learning strategy for the integration of dissimilar single-cell RNA data. It constructs a condition-specific domain-adaptive network founded on variational autoencoders. scDisco not only effectively reduces batch effects but also successfully disentangles biological effects and condition-specific effects, and further augmenting condition-specific representations through the utilization of condition-specific Domain-Specific Batch Normalization layers. This enhancement enables the identification of genes specific to particular conditions. The effectiveness and robustness of scDisco as an integration method were analyzed using both simulated and real datasets, and the results demonstrate that scDisco can yield high-quality visualizations and quantitative outcomes. Furthermore, scDisco has been validated using real datasets, affirming its proficiency in cell clustering quality, retaining batch-specific cell types and identifying condition-specific genes.

CONCLUSION

scDisco is an effective integration method based on variational autoencoders, which improves analytical tasks of reducing batch effects, cell clustering, retaining batch-specific cell types and identifying condition-specific genes.