Single-Cell Transcriptomics of the Human Endocrine Pancreas

ScAGCN: Graph Convolutional Network with Adaptive Aggregation Mechanism for scRNA-seq Data Dimensionality Reduction

With the development of single-cell RNA-sequencing (scRNA-seq) technology, scRNA-seq data analysis suffers huge challenges due to large scale, high dimensionality, high noise, and high sparsity. To achieve accurately embedded representation in the large-scale scRNA-seq data, we try to design a novel graph convolutional network with an adaptive aggregation mechanism. Based on the assumption that the aggregation order of different cells would be different, a graph convolutional network with an adaptive aggregation-based dimensionality reduction algorithm for scRNA-seq data is developed, named scAGCN. In scAGCN, a preprocessing consisting of quality control and feature selection is implemented. Then, an approximate nearest neighbor graph is rapidly constructed. Finally, a graph convolutional network with an adaptive aggregation mechanism is constructed, in which the neighborhood selection strategy based on node distribution and similarity boxplots is designed, and the aggregation function is optimized by defining a similarity measurement between neighborhood nodes and the central node. The results show that scAGCN outperforms existing dimensionality reduction methods on 15 real scRNA-seq datasets, especially in 10 large-scale scRNA-seq datasets.

12-Lipoxygenase Inhibition Improves Glycemia and Obesity-associated Inflammation in Male Human Gene Replacement Mice

Obesity-associated inflammation is characterized by macrophage infiltration into peripheral tissues, contributing to the progression of prediabetes and type 2 diabetes. 12-lipoxygenase (12-LOX) catalyzes the formation of pro-inflammatory eicosanoids and promotes the migration of macrophages, yet its role in obesity-associated inflammation remains incompletely understood. Furthermore, differences between mouse and human orthologs of 12-LOX have limited efforts to study existing pharmacologic inhibitors of 12-LOX. In this study, we used a human gene replacement mouse model in which the gene encoding mouse 12-LOX (Alox15) is replaced by the human ALOX12 gene. As a model of obesity and dysglycemia, we administered male mice a high-fat diet. We subsequently investigated the effects of VLX-1005, a potent and selective small molecule inhibitor of human 12-LOX. Oral administration of VLX-1005 resulted in improved glucose homeostasis, decreased β-cell dedifferentiation, and reduced macrophage infiltration in islets and adipose tissue. Analysis of the stromal vascular fraction from adipose tissue showed a reduction in myeloid cells and cytokine expression with VLX-1005 treatment, indicating decreased adipose tissue inflammation. In a distinct mouse model in which Alox15 was selectively deleted in myeloid cells, we observed decreased β-cell dedifferentiation and reduced macrophage infiltration in both islets and adipose tissue, suggesting that the effects of VLX-1005 may relate to the inhibition of 12-LOX in macrophages. These findings highlight 12-LOX as a key factor in obesity-associated inflammation and suggest that 12-LOX inhibition could serve as a therapeutic strategy to improve glucose homeostasis and peripheral inflammation in the setting of obesity and type 2 diabetes.

Exploration of individual beta cell function over time in vivo: effects of hyperglycemia and glucagon-like peptide-1 receptor (GLP1R) agonism

The coordinated function of beta cells within the pancreatic islet is required for the normal regulation of insulin secretion and is partly controlled by specialized "leader" and highly connected "hub" beta-cell subpopulations. Whether cells within these subpopulations are functionally stable in vivo remains unclear. Here, we establish an approach to monitor Ca 2+ dynamics within individual beta cells over time, after engraftment into the anterior eye chamber, where continuous blood perfusion and near normal innervation pertain. Under normoglycemic conditions, islet network dynamics, and the behavior of individual leaders and hubs, remain stable for at least seven days. Hyperglycemia, resulting from high-fat diet feeding or the loss of a host Gck allele, caused engrafted islets to display incomplete and abortive Ca 2+ waves and overall connectivity was diminished. Whereas hub cell numbers were lowered profoundly in both disease models, leaders largely persisted. Treatment with the GLP1R agonist Exendin-4 led to a recovery of islet-wide Ca 2+ dynamics and the re-emergence of hub cells within minutes, with the effects of the incretin mimetic being more marked than those observed after analogous treatments in vitro . Similar observations were made using 3-dimensional imaging across the whole islet. Our findings thus suggest that incretins may act both directly and indirectly on beta cells in vivo. The approach described may provide broad applicability to the exploration of individual cell function over time in the living animal.

transCAE: Enhancing Cell Type Annotation in Single-cell RNA-seq Data with Transfer Learning and Convolutional Autoencoder

Single-cell RNA sequencing (scRNA-seq) analysis offers tremendous potential for addressing various biological questions, with one key application being the annotation of query datasets with unknown cell types using well-annotated external reference datasets. However, the performance of existing supervised or semi-supervised methods largely depends on the quality of source data. Furthermore, these methods often struggle with the batch effects arising from different platforms when handling multiple reference or query datasets, making precise annotation challenging. We developed transCAE, a robust transfer learning-based algorithm for single-cell annotation that integrates unsupervised dimensionality reduction with supervised cell type classification. This approach fully leverages information from both reference and query datasets to achieve precise cell classification within the query data. Extensive evaluations show that transCAE significantly enhances classification accuracy and efficiently mitigates batch effects. Compared to other state-of-the-art methods, transCAE demonstrates superior performance in experiments involving multiple reference or query datasets. These strengths position transCAE as an optimal annotation method for scRNA-seq datasets.

scCobra allows contrastive cell embedding learning with domain adaptation for single cell data integration and harmonization

The rapid advancement of single-cell technologies has created an urgent need for effective methods to integrate and harmonize single-cell data. Technical and biological variations across studies complicate data integration, while conventional tools often struggle with reliance on gene expression distribution assumptions and over-correction. Here, we present scCobra, a deep generative neural network designed to overcome these challenges through contrastive learning with domain adaptation. scCobra effectively mitigates batch effects, minimizes over-correction, and ensures biologically meaningful data integration without assuming specific gene expression distributions. It enables online label transfer across datasets with batch effects, allowing continuous integration of new data without retraining. Additionally, scCobra supports batch effect simulation, advanced multi-omic integration, and scalable processing of large datasets. By integrating and harmonizing datasets from similar studies, scCobra expands the available data for investigating specific biological problems, improving cross-study comparability, and revealing insights that may be obscured in isolated datasets.

Endogenous cell membrane interactome mapping for the GLP-1 receptor in different cell types

The GLP-1 receptor, one of the most successful drug targets for the treatment of type 2 diabetes and obesity, is known to engage multiple intracellular signaling proteins. However, it remains less explored how the receptor interacts with proteins on the cell membrane. Here, we present a ligand-based proximity labeling approach to interrogate the native cell membrane interactome for the GLP-1 receptor after agonist simulation. Our study identified several unreported putative cell membrane interactors for the endogenous receptor in either a pancreatic β cell line or a neuronal cell line. We further uncovered new regulators of GLP-1 receptor-mediated signaling and insulinotropic responses in β cells. Additionally, we obtained a time-resolved cell membrane interactome map for the receptor in β cells. Therefore, our study provides a new approach that is generalizable to map endogenous cell membrane interactomes for G-protein-coupled receptors to decipher the molecular basis of their cell-type-specific functional regulation.

MetaQ: fast, scalable and accurate metacell inference via single-cell quantization

To overcome the computational barriers of analyzing large-scale single-cell sequencing data, we introduce MetaQ, a metacell algorithm that scales to arbitrarily large datasets with linear runtime and constant memory usage. Inspired by cellular development, MetaQ conceptualizes each metacell as a collective ancestor of biologically similar cells. By quantizing cells into a discrete codebook, where each entry represents a metacell capable of reconstructing the original cells it quantizes, MetaQ identifies homogeneous cell subsets for efficient and accurate metacell inference. This approach reduces computational complexity from exponential to linear while maintaining or surpassing the performance of existing metacell algorithms. Extensive experiments demonstrate that MetaQ excels in downstream tasks such as cell type annotation, developmental trajectory inference, batch integration, and differential expression analysis. Thanks to its superior efficiency and effectiveness, MetaQ makes analyzing datasets with millions of cells practical, offering a powerful solution for single-cell studies in the era of high-throughput profiling.

Single-cell decoding of human islet cell type-specific alterations in type 2 diabetes reveals converging genetic- and state-driven β -cell gene expression defects

Pancreatic islets maintain glucose homeostasis through coordinated action of their constituent endocrine and affiliate cell types and are central to type 2 diabetes (T2D) genetics and pathophysiology. Our understanding of robust human islet cell type-specific alterations in T2D remains limited. Here, we report comprehensive single cell transcriptome profiling of 245,878 human islet cells from a 48-donor cohort spanning non-diabetic (ND), pre-diabetic (PD), and T2D states, identifying 14 distinct cell types detected in every donor from each glycemic state. Cohort analysis reveals ~25-30% loss of functional beta cell mass in T2D vs. ND or PD donors resulting from (1) reduced total beta cell numbers/proportions and (2) reciprocal loss of 'high function' and gain of senescent β -cell subpopulations. We identify in T2D β -cells 511 differentially expressed genes (DEGs), including new (66.5%) and validated genes (e.g., FXYD2, SLC2A2, SYT1), and significant neuronal transmission and vitamin A metabolism pathway alterations. Importantly, we demonstrate newly identified DEG roles in human β -cell viability and/or insulin secretion and link 47 DEGs to diabetes-relevant phenotypes in knockout mice, implicating them as potential causal islet dysfunction genes. Additionally, we nominate as candidate T2D causal genes and therapeutic targets 27 DEGs for which T2D genetic risk variants (GWAS SNPs) and pathophysiology (T2D vs. ND) exert concordant expression effects. We provide this freely accessible atlas for data exploration, analysis, and hypothesis testing. Together, this study provides new genomic resources for and insights into T2D pathophysiology and human islet dysfunction.

12-Lipoxygenase inhibition improves glucose homeostasis and obesity-associated inflammation in human gene replacement mice

Obesity-associated inflammation is characterized by macrophage infiltration into peripheral tissues, contributing to the progression of prediabetes and type 2 diabetes (T2D). The enzyme 12-lipoxygenase (12-LOX) catalyzes the formation of pro-inflammatory eicosanoids and is known to promote the migration of macrophages, yet its role in obesity-associated inflammation remains incompletely understood. Furthermore, differences between mouse and human orthologs of 12-LOX have limited efforts to study existing pharmacologic inhibitors of 12-LOX. In this study, we utilized a human gene replacement mouse model in which the gene encoding mouse 12-LOX (Alox15) is replaced by the human ALOX12 gene. As a model of obesity and dysglycemia, we administered these mice a high-fat diet. We subsequently investigated the effects of VLX-1005, a potent and selective small molecule inhibitor of human 12-LOX. Oral administration of VLX-1005 resulted in improved glucose homeostasis, decreased β cell dedifferentiation, and reduced macrophage infiltration in islets and adipose tissue. Analysis of the stromal vascular fraction from adipose tissue showed a reduction in myeloid cells and cytokine expression with VLX-1005 treatment, indicating decreased adipose tissue inflammation. In a distinct mouse model in which Alox15 was selectively deleted in myeloid cells, we observed decreased β cell dedifferentiation and reduced macrophage infiltration in both islets and adipose tissue, suggesting that the effects of VLX-1005 may relate to the inhibition of 12-LOX in macrophages. These findings highlight 12-LOX as a key factor in obesity-associated inflammation and suggest that 12-LOX inhibition could serve as a therapeutic strategy to improve glucose homeostasis and peripheral inflammation in the setting of obesity and T2D.

pscAdapt: Pre-Trained Domain Adaptation Network Based on Structural Similarity for Cell Type Annotation in Single Cell RNA-seq Data

Cell type annotation refers to the process of categorizing and labeling cells to identify their specific cell types, which is crucial for understanding cell functions and biological processes. Although many methods have been developed for automated cell type annotation, they often encounter challenges such as batch effects due to variations in data distribution across platforms and species, thereby compromising their performance. To address batch effects, in this study, a pre-trained domain adaptation model based on structural similarity, named pscAdapt, is proposed for cell type annotation. Specifically, a pre-trained strategy is employed to initialize model parameters to learn the data distribution of source domain. This strategy is also combined with an adversarial learning strategy to train the domain adaptation network for achieving domain level alignment and reducing domain discrepancy. Furthermore, to better distinguish different types of cells, a structural similarity loss is designed, aiming to shorten distances between cells of the same type and increase distances between cells of different types in feature space, thus achieving cell level alignment and enhancing the discriminability of cell types. Comprehensive experiments were conducted on simulated datasets, cross-platforms datasets and cross-species datasets to validate the effectiveness of pscAdapt, results of which demonstrate that pscAdapt outperforms several popular cell type annotation methods.

G6PC2 controls glucagon secretion by defining the set point for glucose in pancreatic α cells

Elevated glucagon concentrations have been reported in patients with type 2 diabetes (T2D). A critical role for α cell-intrinsic mechanisms in regulating glucagon secretion was previously established through genetic manipulation of the glycolytic enzyme glucokinase (GCK) in mice. Genetic variation at the glucose-6-phosphatase catalytic subunit 2 (G6PC2) locus, encoding an enzyme that opposes GCK, has been reproducibly associated with fasting blood glucose and hemoglobin A1c. Here, we found that trait-associated variants in the G6PC2 promoter are located in open chromatin not just in β but also in α cells and documented allele-specific G6PC2 expression of linked variants in human α cells. Using α cell-specific gene ablation of G6pc2 in mice, we showed that this gene plays a critical role in controlling glucose suppression of amino acid-stimulated glucagon secretion independent of alterations in insulin output, islet hormone content, or islet morphology, findings that we confirmed in primary human α cells. Collectively, our data demonstrate that G6PC2 affects glycemic control via its action in α cells and possibly suggest that G6PC2 inhibitors might help control blood glucose through a bihormonal mechanism.

Cycling alpha cells in regenerative drug-treated human pancreatic islets may serve as key beta cell progenitors

Diabetes results from an inadequate number of insulin-producing human beta cells. There is currently no clinically available effective means to restore beta cell mass in millions of people with diabetes. Although the DYRK1A inhibitors, either alone or in combination with GLP-1 receptor agonists (GLP-1) or transforming growth factor β (TGF-β) superfamily inhibitors (LY), induce beta cell replication and increase beta cell mass, the precise mechanisms of action remain elusive. Here we perform single-cell RNA sequencing on human pancreatic islets treated with a DYRK1A inhibitor, either alone or with GLP-1 or LY. We identify cycling alpha cells as the most responsive cells to DYRK1A inhibition. Lineage trajectory analyses suggest that cycling alpha cells may serve as precursor cells that transdifferentiate into beta cells. Collectively, in addition to enhancing expression of beta cell phenotypic genes in beta cells, our findings suggest that regenerative drugs may be targeting cycling alpha cells in human islets.

Beta cells are essential drivers of pancreatic ductal adenocarcinoma development

Pancreatic endocrine-exocrine crosstalk plays a key role in normal physiology and disease. For instance, endocrine islet beta (β) cell secretion of insulin or cholecystokinin (CCK) promotes progression of pancreatic adenocarcinoma (PDAC), an exocrine cell-derived tumor. However, the cellular and molecular mechanisms that govern endocrine-exocrine signaling in tumorigenesis remain incompletely understood. We find that β cell ablation impedes PDAC development in mice, arguing that the endocrine pancreas is critical for exocrine tumorigenesis. Conversely, obesity induces β cell hormone dysregulation, alters CCK-dependent peri-islet exocrine cell transcriptional states, and enhances islet proximal tumor formation. Single-cell RNA-sequencing, in silico latent-space archetypal and trajectory analysis, and genetic lineage tracing in vivo reveal that obesity stimulates postnatal immature β cell expansion and adaptation towards a pro-tumorigenic CCK+ state via JNK/cJun stress-responsive signaling. These results define endocrine-exocrine signaling as a driver of PDAC development and uncover new avenues to target the endocrine pancreas to subvert exocrine tumorigenesis.

Using Multi-Encoder Semi-Implicit Graph Variational Autoencoder to Analyze Single-Cell RNA Sequencing Data

Rapid advances in single-cell RNA sequencing (scRNA-seq) have made it possible to characterize cell states at a high resolution view for large scale library. scRNA-seq data contains a great deal of biological information, which can be mainly used to discover cell subtypes and track cell development. However, traditional methods face many challenges in addressing scRNA-seq data with high dimensions and high sparsity. For better analysis of scRNA-seq data, we propose a new framework called MSVGAE based on variational graph auto-encoder and graph attention networks. Specifically, we introduce multiple encoders to learn features at different scales and control for uninformative features. Moreover, different noises are added to encoders to promote the propagation of graph structural information and distribution uncertainty. Therefore, some complex posterior distributions can be captured by our model. MSVGAE maps scRNA-seq data with high dimensions and high noise into the low-dimensional latent space, which is beneficial for downstream tasks. In particular, MSVGAE can handle extremely sparse data. Before the experiment, we create 24 simulated datasets to simulate various biological scenarios and collect 8 real-world datasets. The experimental results of clustering, visualization and marker genes analysis indicate that MSVGAE model has excellent accuracy and robustness in analyzing scRNA-seq data.

Characterization of Human Pancreatic Islet Cells Using a Single-Cell Western Blot Platform

OBJECTIVE

Islet transplantation is an effective treatment for type 1 diabetes. However, transplant success depends on quick islet assessment because islets deteriorate 2-3 days after isolation. A new tool, single-cell western blot (scWestern), offers results within 1 day. In this study, we aimed to test the suitability of scWestern to detect protein markers for beta (insulin), alpha (glucagon), and delta (somatostatin) cells, the 3 major endocrine cell types in islets.

MATERIALS AND METHODS

We characterized the antibody specificity, signal intensity, and cell identification on the scWestern platform and then compared the islet cell composition analysis between scWestern and immunohistochemistry performed by the Integrated Islet Distribution Program.

RESULTS

Islet cell composition is comparable for alpha and beta cells, but not delta cells. Protein expression levels of insulin, glucagon, and somatostatin in individual islet cells varied greatly, highlighting cell type heterogeneity. Surprisingly, scWestern revealed double-hormonal cells (~1%), co-expressing insulin and somatostatin or insulin and glucagon, in nondiabetic and nonobese adult human islets, which was confirmed by confocal immunofluorescence microscopy.

CONCLUSIONS

These results demonstrate that each alpha, beta, and delta cells express varying levels of peptide hormones, and a small subpopulation co-expresses double hormones in normal human islets. The scWestern platform will enable timely assessment of beta cell mass in isolated islets before clinical transplantation.

Tankyrase inhibition promotes endocrine commitment of hPSC-derived pancreatic progenitors

Human pluripotent stem cells (hPSCs) have the potential to differentiate into various cell types, including pancreatic insulin-producing β cells, which are crucial for developing therapies for diabetes. However, current methods for directing hPSC differentiation towards pancreatic β-like cells are often inefficient and produce cells that do not fully resemble the native counterparts. Here, we report that highly selective tankyrase inhibitors, such as WIKI4, significantly enhances pancreatic differentiation from hPSCs. Our results show that WIKI4 promotes the formation of pancreatic progenitors that give rise to islet-like cells with improved β-like cell frequencies and glucose responsiveness compared to our standard cultures. These findings not only advance our understanding of pancreatic development, but also provide a promising new tool for generating pancreatic cells for research and potential therapeutic applications.

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

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

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.

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.

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.

Remodeling ceramide homeostasis promotes functional maturation of human pluripotent stem cell-derived β cells

Human pluripotent stem cell-derived β cells (hPSC-β cells) show the potential to restore euglycemia. However, the immature functionality of hPSC-β cells has limited their efficacy in application. Here, by deciphering the continuous maturation process of hPSC-β cells post transplantation via single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), we show that functional maturation of hPSC-β cells is an orderly multistep process during which cells sequentially undergo metabolic adaption, removal of negative regulators of cell function, and establishment of a more specialized transcriptome and epigenome. Importantly, remodeling lipid metabolism, especially downregulating the metabolic activity of ceramides, the central hub of sphingolipid metabolism, is critical for β cell maturation. Limiting intracellular accumulation of ceramides in hPSC-β cells remarkably enhanced their function, as indicated by improvements in insulin processing and glucose-stimulated insulin secretion. In summary, our findings provide insights into the maturation of human pancreatic β cells and highlight the importance of ceramide homeostasis in function acquisition.

scEM: A New Ensemble Framework for Predicting Cell Type Composition Based on scRNA-Seq Data

With the advent of single-cell RNA sequencing (scRNA-seq) technology, many scRNA-seq data have become available, providing an unprecedented opportunity to explore cellular composition and heterogeneity. Recently, many computational algorithms for predicting cell type composition have been developed, and these methods are typically evaluated on different datasets and performance metrics using diverse techniques. Consequently, the lack of comprehensive and standardized comparative analysis makes it difficult to gain a clear understanding of the strengths and weaknesses of these methods. To address this gap, we reviewed 20 cutting-edge unsupervised cell type identification methods and evaluated these methods comprehensively using 24 real scRNA-seq datasets of varying scales. In addition, we proposed a new ensemble cell-type identification method, named scEM, which learns the consensus similarity matrix by applying the entropy weight method to the four representative methods are selected. The Louvain algorithm is adopted to obtain the final classification of individual cells based on the consensus matrix. Extensive evaluation and comparison with 11 other similarity-based methods under real scRNA-seq datasets demonstrate that the newly developed ensemble algorithm scEM is effective in predicting cellular type composition.

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.

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 ).

Autoimmune CD8+ T cells in type 1 diabetes: from single-cell RNA sequencing to T-cell receptor redirection

Type 1 diabetes (T1D) is an organ-specific autoimmune disease caused by pancreatic β cell destruction and mediated primarily by autoreactive CD8+ T cells. It has been shown that only a small number of stem cell-like β cell-specific CD8+ T cells are needed to convert normal mice into T1D mice; thus, it is likely that T1D can be cured or significantly improved by modulating or altering self-reactive CD8+ T cells. However, stem cell-type, effector and exhausted CD8+ T cells play intricate and important roles in T1D. The highly diverse T-cell receptors (TCRs) also make precise and stable targeted therapy more difficult. Therefore, this review will investigate the mechanisms of autoimmune CD8+ T cells and TCRs in T1D, as well as the related single-cell RNA sequencing (ScRNA-Seq), CRISPR/Cas9, chimeric antigen receptor T-cell (CAR-T) and T-cell receptor-gene engineered T cells (TCR-T), for a detailed and clear overview. This review highlights that targeting CD8+ T cells and their TCRs may be a potential strategy for predicting or treating T1D.

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.

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.

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.

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 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.

The role of Na+-coupled bicarbonate transporters (NCBT) in health and disease

Cellular and organism survival depends upon the regulation of pH, which is regulated by highly specialized cell membrane transporters, the solute carriers (SLC) (For a comprehensive list of the solute carrier family members, see: https://www.bioparadigms.org/slc/ ). The SLC4 family of bicarbonate (HCO3-) transporters consists of ten members, sorted by their coupling to either sodium (NBCe1, NBCe2, NBCn1, NBCn2, NDCBE), chloride (AE1, AE2, AE3), or borate (BTR1). The ionic coupling of SLC4A9 (AE4) remains controversial. These SLC4 bicarbonate transporters may be controlled by cellular ionic gradients, cellular membrane voltage, and signaling molecules to maintain critical cellular and systemic pH (acid-base) balance. There are profound consequences when blood pH deviates even a small amount outside the normal range (7.35-7.45). Chiefly, Na+-coupled bicarbonate transporters (NCBT) control intracellular pH in nearly every living cell, maintaining the biological pH required for life. Additionally, NCBTs have important roles to regulate cell volume and maintain salt balance as well as absorption and secretion of acid-base equivalents. Due to their varied tissue expression, NCBTs have roles in pathophysiology, which become apparent in physiologic responses when their expression is reduced or genetically deleted. Variations in physiological pH are seen in a wide variety of conditions, from canonically acid-base related conditions to pathologies not necessarily associated with acid-base dysfunction such as cancer, glaucoma, or various neurological diseases. The membranous location of the SLC4 transporters as well as recent advances in discovering their structural biology makes them accessible and attractive as a druggable target in a disease context. The role of sodium-coupled bicarbonate transporters in such a large array of conditions illustrates the potential of treating a wide range of disease states by modifying function of these transporters, whether that be through inhibition or enhancement.

A deep learning adversarial autoencoder with dynamic batching displays high performance in denoising and ordering scRNA-seq data

By providing high-resolution of cell-to-cell variation in gene expression, single-cell RNA sequencing (scRNA-seq) offers insights into cell heterogeneity, differentiating dynamics, and disease mechanisms. However, challenges such as low capture rates and dropout events can introduce noise in data analysis. Here, we propose a deep neural generative framework, the dynamic batching adversarial autoencoder (DB-AAE), which excels at denoising scRNA-seq datasets. DB-AAE directly captures optimal features from input data and enhances feature preservation, including cell type-specific gene expression patterns. Comprehensive evaluation on simulated and real datasets demonstrates that DB-AAE outperforms other methods in denoising accuracy and biological signal preservation. It also improves the accuracy of other algorithms in establishing pseudo-time inference. This study highlights DB-AAE's effectiveness and potential as a valuable tool for enhancing the quality and reliability of downstream analyses in scRNA-seq research.

Glucagon-producing α-cell transcriptional identity and reprogramming towards insulin production

β-Cell replacement by in situ reprogramming of non-β-cells is a promising diabetes therapy. Following the observation that near-total β-cell ablation in adult mice triggers the reprogramming of pancreatic α-, δ-, and γ-cells into insulin (INS)-producing cells, recent studies are delving deep into the mechanisms controlling adult α-cell identity. Systematic analyses of the α-cell transcriptome and epigenome have started to pinpoint features that could be crucial for maintaining α-cell identity. Using different transgenic and chemical approaches, significant advances have been made in reprogramming α-cells in vivo into INS-secreting cells in mice. The recent reprogramming of human α-cells in vitro is an important step forward that must now be complemented with a comprehensive molecular dissection of the mechanisms controlling α-cell identity.

Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data

BACKGROUND

Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healthcare to achieve better accuracy and generalizability. Sharing data across different healthcare institutions or jurisdictions is challenging because of complex and varying privacy and regulatory requirements. Hence, it is hard but crucial to allow multiple parties to collaboratively train an ML model leveraging the private datasets available at each party without the need for direct sharing of those datasets or compromising the privacy of the datasets through collaboration.

METHODS

In this paper, we address this challenge by proposing Decentralized, Collaborative, and Privacy-preserving ML for Multi-Hospital Data (DeCaPH). This framework offers the following key benefits: (1) it allows different parties to collaboratively train an ML model without transferring their private datasets (i.e., no data centralization); (2) it safeguards patients' privacy by limiting the potential privacy leakage arising from any contents shared across the parties during the training process; and (3) it facilitates the ML model training without relying on a centralized party/server.

FINDINGS

We demonstrate the generalizability and power of DeCaPH on three distinct tasks using real-world distributed medical datasets: patient mortality prediction using electronic health records, cell-type classification using single-cell human genomes, and pathology identification using chest radiology images. The ML models trained with DeCaPH framework have less than 3.2% drop in model performance comparing to those trained by the non-privacy-preserving collaborative framework. Meanwhile, the average vulnerability to privacy attacks of the models trained with DeCaPH decreased by up to 16%. In addition, models trained with our DeCaPH framework achieve better performance than those models trained solely with the private datasets from individual parties without collaboration and those trained with the previous privacy-preserving collaborative training framework under the same privacy guarantee by up to 70% and 18.2% respectively.

INTERPRETATION

We demonstrate that the ML models trained with DeCaPH framework have an improved utility-privacy trade-off, showing DeCaPH enables the models to have good performance while preserving the privacy of the training data points. In addition, the ML models trained with DeCaPH framework in general outperform those trained solely with the private datasets from individual parties, showing that DeCaPH enhances the model generalizability.

FUNDING

This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC, RGPIN-2020-06189 and DGECR-2020-00294), Canadian Institute for Advanced Research (CIFAR) AI Catalyst Grants, CIFAR AI Chair programs, Temerty Professor of AI Research and Education in Medicine, University of Toronto, Amazon, Apple, DARPA through the GARD project, Intel, Meta, the Ontario Early Researcher Award, and the Sloan Foundation. Resources used in preparing this research were provided, in part, by the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute.

Combining Global-Constrained Concept Factorization and a Regularized Gaussian Graphical Model for Clustering Single-Cell RNA-seq Data

Single-cell RNA sequencing technology is one of the most cost-effective ways to uncover transcriptomic heterogeneity. With the rapid rise of this technology, enormous amounts of scRNA-seq data have been produced. Due to the high dimensionality, noise, sparsity and missing features of the available scRNA-seq data, accurately clustering the scRNA-seq data for downstream analysis is a significant challenge. Many computational methods have been designed to address this issue; nevertheless, the efficacy of the available methods is still inadequate. In addition, most similarity-based methods require a number of clusters as input, which is difficult to achieve in real applications. In this study, we developed a novel computational method for clustering scRNA-seq data by considering both global and local information, named GCFG. This method characterizes the global properties of data by applying concept factorization, and the regularized Gaussian graphical model is utilized to evaluate the local embedding relationship of data. To learn the cell-cell similarity matrix, we integrated the two components, and an iterative optimization algorithm was developed. The categorization of single cells is obtained by applying Louvain, a modularity-based community discovery algorithm, to the similarity matrix. The behavior of the GCFG approach is assessed on 14 real scRNA-seq datasets in terms of ACC and ARI, and comparison results with 17 other competitive methods suggest that GCFG is effective and robust.

A novel batch-effect correction method for scRNA-seq data based on Adversarial Information Factorization

Single-cell RNA sequencing (scRNA-seq) technology produces an unprecedented resolution at the level of a unique cell, raising great hopes in medicine. Nevertheless, scRNA-seq data suffer from high variations due to the experimental conditions, called batch effects, preventing any aggregated downstream analysis. Adversarial Information Factorization provides a robust batch-effect correction method that does not rely on prior knowledge of the cell types nor a specific normalization strategy while being adapted to any downstream analysis task. It compares to and even outperforms state-of-the-art methods in several scenarios: low signal-to-noise ratio, batch-specific cell types with few cells, and a multi-batches dataset with imbalanced batches and batch-specific cell types. Moreover, it best preserves the relative gene expression between cell types, yielding superior differential expression analysis results. Finally, in a more complex setting of a Leukemia cohort, our method preserved most of the underlying biological information for each patient while aligning the batches, improving the clustering metrics in the aggregated dataset.

Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq

While sub-clustering cell-populations has become popular in single cell-omics, negative controls for this process are lacking. Popular feature-selection/clustering algorithms fail the null-dataset problem, allowing erroneous subdivisions of homogenous clusters until nearly each cell is called its own cluster. Using real and synthetic datasets, we find that anti-correlated gene selection reduces or eliminates erroneous subdivisions, increases marker-gene selection efficacy, and efficiently scales to millions of cells.

Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning

Diabetes affects >10% of adults worldwide and is caused by impaired production or response to insulin, resulting in chronic hyperglycemia. Pancreatic islet β-cells are the sole source of endogenous insulin and our understanding of β-cell dysfunction and death in type 2 diabetes (T2D) is incomplete. Single-cell RNA-seq data supports heterogeneity as an important factor in β-cell function and survival. However, it is difficult to identify which β-cell phenotypes are critical for T2D etiology and progression. Our goal was to prioritize specific disease-related β-cell subpopulations to better understand T2D pathogenesis and identify relevant genes for targeted therapeutics. To address this, we applied a deep transfer learning tool, DEGAS, which maps disease associations onto single-cell RNA-seq data from bulk expression data. Independent runs of DEGAS using T2D or obesity status identified distinct β-cell subpopulations. A singular cluster of T2D-associated β-cells was identified; however, β-cells with high obese-DEGAS scores contained two subpopulations derived largely from either non-diabetic or T2D donors. The obesity-associated non-diabetic cells were enriched for translation and unfolded protein response genes compared to T2D cells. We selected DLK1 for validation by immunostaining in human pancreas sections from healthy and T2D donors. DLK1 was heterogeneously expressed among β-cells and appeared depleted from T2D islets. In conclusion, DEGAS has the potential to advance our holistic understanding of the β-cell transcriptomic phenotypes, including features that distinguish β-cells in obese non-diabetic or lean T2D states. Future work will expand this approach to additional human islet omics datasets to reveal the complex multicellular interactions driving T2D.

PancrESS - a meta-analysis resource for understanding cell-type specific expression in the human pancreas

BACKGROUND

The human pancreas is composed of specialized cell types producing hormones and enzymes critical to human health. These specialized functions are the result of cell type-specific transcriptional programs which manifest in cell-specific gene expression. Understanding these programs is essential to developing therapies for pancreatic disorders. Transcription in the human pancreas has been widely studied by single-cell RNA technologies, however the diversity of protocols and analysis methods hinders their interpretability in the aggregate.

RESULTS

In this work, we perform a meta-analysis of pancreatic single-cell RNA sequencing data. We present a database for reference transcriptome abundances and cell-type specificity metrics. This database facilitates the identification and definition of marker genes within the pancreas. Additionally, we introduce a versatile tool which is freely available as an R package, and should permit integration into existing workflows. Our tool accepts count data files generated by widely-used single-cell gene expression platforms in their original format, eliminating an additional pre-formatting step. Although we designed it to calculate expression specificity of pancreas cell types, our tool is agnostic to the biological source of count data, extending its applicability to other biological systems.

CONCLUSIONS

Our findings enhance the current understanding of expression specificity within the pancreas, surpassing previous work in terms of scope and detail. Furthermore, our database and tool enable researchers to perform similar calculations in diverse biological systems, expanding the applicability of marker gene identification and facilitating comparative analyses.

Pathway analysis through mutual information

MOTIVATION

In pathway analysis, we aim to establish a connection between the activity of a particular biological pathway and a difference in phenotype. There are many available methods to perform pathway analysis, many of them rely on an upstream differential expression analysis, and many model the relations between the abundances of the analytes in a pathway as linear relationships.

RESULTS

Here, we propose a new method for pathway analysis, MIPath, that relies on information theoretical principles and, therefore, does not model the association between pathway activity and phenotype, resulting in relatively few assumptions. For this, we construct a graph of the data points for each pathway using a nearest-neighbor approach and score the association between the structure of this graph and the phenotype of these same samples using Mutual Information while adjusting for the effects of random chance in each score. The initial nearest neighbor approach evades individual gene-level comparisons, hence making the method scalable and less vulnerable to missing values. These properties make our method particularly useful for single-cell data. We benchmarked our method on several single-cell datasets, comparing it to established and new methods, and found that it produces robust, reproducible, and meaningful scores.

AVAILABILITY AND IMPLEMENTATION

Source code is available at https://github.com/statisticalbiotechnology/mipath, or through Python Package Index as "mipathway."

Bridging the Gap: Pancreas Tissue Slices From Organ and Tissue Donors for the Study of Diabetes Pathogenesis

Over the last two decades, increased availability of human pancreatic tissues has allowed for major expansions in our understanding of islet biology in health and disease. Indeed, studies of fixed and frozen pancreatic tissues, as well as efforts using viable isolated islets obtained from organ donors, have provided significant insights toward our understanding of diabetes. However, the procedures associated with islet isolation result in distressed cells that have been removed from any surrounding influence. The pancreas tissue slice technology was developed as an in situ approach to overcome certain limitations associated with studies on isolated islets or fixed tissue. In this Perspective, we discuss the value of this novel platform and review how pancreas tissue slices, within a short time, have been integrated in numerous studies of rodent and human islet research. We show that pancreas tissue slices allow for investigations in a less perturbed organ tissue environment, ranging from cellular processes, over peri-islet modulations, to tissue interactions. Finally, we discuss the considerations and limitations of this technology in its future applications. We believe the pancreas tissue slices will help bridge the gap between studies on isolated islets and cells to the systemic conditions by providing new insight into physiological and pathophysiological processes at the organ level.

ARTICLE HIGHLIGHTS

Human pancreas tissue slices represent a novel platform to study human islet biology in close to physiological conditions. Complementary to established technologies, such as isolated islets, single cells, and histological sections, pancreas tissue slices help bridge our understanding of islet physiology and pathophysiology from single cell to intact organ. Diverse sources of viable human pancreas tissue, each with distinct characteristics to be considered, are available to use in tissue slices for the study of diabetes pathogenesis.

Apolipoprotein L genes are novel mediators of inflammation in beta cells

AIMS/HYPOTHESIS

Inflammation induces beta cell dysfunction and demise but underlying molecular mechanisms remain unclear. The apolipoprotein L (APOL) family of genes has been associated with innate immunity and apoptosis in non-pancreatic cell types, but also with metabolic syndrome and type 2 diabetes mellitus. Here, we hypothesised that APOL genes play a role in inflammation-induced beta cell damage.

METHODS

We used single-cell transcriptomics datasets of primary human pancreatic islet cells to study the expression of APOL genes upon specific stress conditions. Validation of the findings was carried out in EndoC-βH1 cells and primary human islets. Finally, we performed loss- and gain-of-function experiments to investigate the role of APOL genes in beta cells.

RESULTS

APOL genes are expressed in primary human beta cells and APOL1, 2 and 6 are strongly upregulated upon inflammation via the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway. APOL1 overexpression increases endoplasmic reticulum stress while APOL1 knockdown prevents cytokine-induced beta cell death and interferon-associated response. Furthermore, we found that APOL genes are upregulated in beta cells from donors with type 2 diabetes compared with donors without diabetes mellitus.

CONCLUSIONS/INTERPRETATION

APOLs are novel regulators of islet inflammation and may contribute to beta cell damage during the development of diabetes.

DATA AVAILABILITY

scRNAseq data generated by our laboratory and used in this study are available in the Gene Expression Omnibus (GEO; www.ncbi.nlm.nih.gov/geo/ ), accession number GSE218316.

scGCC: Graph Contrastive Clustering With Neighborhood Augmentations for scRNA-Seq Data Analysis

Single-cell RNA sequencing (scRNA-seq) has rapidly emerged as a powerful technique for analyzing cellular heterogeneity at the individual cell level. In the analysis of scRNA-seq data, cell clustering is a critical step in downstream analysis, as it enables the identification of cell types and the discovery of novel cell subtypes. However, the characteristics of scRNA-seq data, such as high dimensionality and sparsity, dropout events and batch effects, present significant computational challenges for clustering analysis. In this study, we propose scGCC, a novel graph self-supervised contrastive learning model, to address the challenges faced in scRNA-seq data analysis. scGCC comprises two main components: a representation learning module and a clustering module. The scRNA-seq data is first fed into a representation learning module for training, which is then used for data classification through a clustering module. scGCC can learn low-dimensional denoised embeddings, which is advantageous for our clustering task. We introduce Graph Attention Networks (GAT) for cell representation learning, which enables better feature extraction and improved clustering accuracy. Additionally, we propose five data augmentation methods to improve clustering performance by increasing data diversity and reducing overfitting. These methods enhance the robustness of clustering results. Our experimental study on 14 real-world datasets has demonstrated that our model achieves extraordinary accuracy and robustness. We also perform downstream tasks, including batch effect removal, trajectory inference, and marker genes analysis, to verify the biological effectiveness of our model.

The emerging role of the hedgehog signaling pathway in immunity response and autoimmune diseases

The Hedgehog (Hh) family is a prototypical morphogen involved in embryonic patterning, multi-lineage differentiation, self-renewal, morphogenesis, and regeneration. There are studies that have demonstrated that the Hh signaling pathway differentiates developing T cells into MHC-restricted self-antigen tolerant T cells in a concentration-dependent manner in the thymus. Whereas Hh signaling pathway is not required in the differentiation of B cells but is indispensable in maintaining the regeneration of hematopoietic stem cells (HSCs) and the viability of germinal centers (GCs) B cells. The Hh signaling pathway exerts both positive and negative effects on immune responses, which involves activating human peripheral CD4+ T cells, regulating the accumulation of natural killer T (NKT) cells, recruiting and activating macrophages, increasing CD4+Foxp3+ regulatory T cells in the inflammation sites to sustain homeostasis. Hedgehog signaling is involved in the patterning of the embryo, as well as homeostasis of adult tissues. Therefore, this review aims to highlight evidence for Hh signaling in the differentiation, function of immune cells and autoimmune disease. Targeting Hh signaling promises to be a novel, alternative or adjunct approach to treating tumors and autoimmune diseases.

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 focussing on the imprinted gene 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 eGFP under the control of the Nnat enhancer/promoter regions were generated for fluorescence-activated cell (FAC) sorting of beta cells and downstream analysis of CpG methylation by bisulphite and RNA sequencing, respectively. Animals deleted for the de novo methyltransferase, 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 and Cal-590. 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 data sets 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 sub-population specialised for insulin production, reminiscent of recently-described "βHI" cells and were diminished in db/db mice. 'Hub' cells were less abundant in the NNAT+ population, consistent with epigenetic control of this functional specialization.

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 thus contribute to a loss of beta cell hierarchy and connectivity, potentially contributing to defective insulin secretion in some forms of diabetes.

Dynamic scRNA-seq of live human pancreatic slices reveals functional endocrine cell neogenesis through an intermediate ducto-acinar stage

Human pancreatic plasticity is implied from multiple single-cell RNA sequencing (scRNA-seq) studies. However, these have been invariably based on static datasets from which fate trajectories can only be inferred using pseudotemporal estimations. Furthermore, the analysis of isolated islets has resulted in a drastic underrepresentation of other cell types, hindering our ability to interrogate exocrine-endocrine interactions. The long-term culture of human pancreatic slices (HPSs) has presented the field with an opportunity to dynamically track tissue plasticity at the single-cell level. Combining datasets from same-donor HPSs at different time points, with or without a known regenerative stimulus (BMP signaling), led to integrated single-cell datasets storing true temporal or treatment-dependent information. This integration revealed population shifts consistent with ductal progenitor activation, blurring of ductal/acinar boundaries, formation of ducto-acinar-endocrine differentiation axes, and detection of transitional insulin-producing cells. This study provides the first longitudinal scRNA-seq analysis of whole human pancreatic tissue, confirming its plasticity in a dynamic fashion.

scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching

Single-cell RNA sequencing has been instrumental in uncovering cellular spatiotemporal context. This task is challenging as cells simultaneously encode multiple, potentially cross-interfering, biological signals. Here we propose scPrisma, a spectral computational method that uses topological priors to decouple, enhance and filter different classes of biological processes in single-cell data, such as periodic and linear signals. We apply scPrisma to the analysis of the cell cycle in HeLa cells, circadian rhythm and spatial zonation in liver lobules, diurnal cycle in Chlamydomonas and circadian rhythm in the suprachiasmatic nucleus in the brain. scPrisma can be used to distinguish mixed cellular populations by specific characteristics such as cell type and uncover regulatory networks and cell-cell interactions specific to predefined biological signals, such as the circadian rhythm. We show scPrisma's flexibility in incorporating prior knowledge, inference of topologically informative genes and generalization to additional diverse templates and systems. scPrisma can be used as a stand-alone workflow for signal analysis and as a prior step for downstream single-cell analysis.

scTOP: physics-inspired order parameters for cellular identification and visualization

Advances in single-cell RNA sequencing provide an unprecedented window into cellular identity. The abundance of data requires new theoretical and computational frameworks to analyze the dynamics of differentiation and integrate knowledge from cell atlases. We present 'single-cell Type Order Parameters' (scTOP): a statistical, physics-inspired approach for quantifying cell identity given a reference basis of cell types. scTOP can accurately classify cells, visualize developmental trajectories and assess the fidelity of engineered cells. Importantly, scTOP does this without feature selection, statistical fitting or dimensional reduction (e.g. uniform manifold approximation and projection, principle components analysis, etc.). We illustrate the power of scTOP using human and mouse datasets. By reanalyzing mouse lung data, we characterize a transient hybrid alveolar type 1/alveolar type 2 cell population. Visualizations of lineage tracing hematopoiesis data using scTOP confirm that a single clone can give rise to multiple mature cell types. We assess the transcriptional similarity between endogenous and donor-derived cells in the context of murine pulmonary cell transplantation. Our results suggest that physics-inspired order parameters can be an important tool for understanding differentiation and characterizing engineered cells. scTOP is available as an easy-to-use Python package.

An integrative single-cell multi-omics profiling of human pancreatic islets identifies T1D associated genes and regulatory signals

Genome wide association studies (GWAS) have identified over 100 signals associated with type 1 diabetes (T1D). However, translating any given T1D GWAS signal into mechanistic insights, including putative causal variants and the context (cell type and cell state) in which they function, has been limited. Here, we present a comprehensive multi-omic integrative analysis of single-cell/nucleus resolution profiles of gene expression and chromatin accessibility in healthy and autoantibody+ (AAB+) human islets, as well as islets under multiple T1D stimulatory conditions. We broadly nominate effector cell types for all T1D GWAS signals. We further nominated higher-resolution contexts, including effector cell types, regulatory elements, and genes for three independent T1D risk variants acting through islet cells within the pancreas at the DLK1/MEG3, RASGRP1, and TOX loci. Subsequently, we created isogenic gene knockouts DLK1-/-, RASGRP1-/-, and TOX-/-, and the corresponding regulatory region knockout, RASGRP1Δ, and DLK1Δ hESCs. Loss of RASGRP1 or DLK1, as well as knockout of the regulatory region of RASGRP1 or DLK1, increased β cell apoptosis. Additionally, pancreatic β cells derived from isogenic hESCs carrying the risk allele of rs3783355A/A exhibited increased β cell death. Finally, RNA-seq and ATAC-seq identified five genes upregulated in both RASGRP1-/- and DLK1-/- β-like cells, four of which are associated with T1D. Together, this work reports an integrative approach for combining single cell multi-omics, GWAS, and isogenic hESC-derived β-like cells to prioritize the T1D associated signals and their underlying context-specific cell types, genes, SNPs, and regulatory elements, to illuminate biological functions and molecular mechanisms.