Supervised and unsupervised methods have emerged to address the complexity of single cell data analysis in the context of large pools of independent studies. Here, we present ClusterFoldSimilarity (CFS), a novel statistical method design to quantify the similarity between cell groups across any number of independent datasets, without the need for data correction or integration. By bypassing these processes, CFS avoids the introduction of artifacts and loss of information, offering a simple, efficient, and scalable solution. This method match groups of cells that exhibit conserved phenotypes across datasets, including different tissues and species, and in a multimodal scenario, including single-cell RNA-Seq, ATAC-Seq, single-cell proteomics, or, more broadly, data exhibiting differential abundance effects among groups of cells. Additionally, CFS performs feature selection, obtaining cross-dataset markers of the similar phenotypes observed, providing an inherent interpretability of relationships between cell populations. To showcase the effectiveness of our methodology, we generated single-nuclei RNA-Seq data from the motor cortex and spinal cord of adult mice. By using CFS, we identified three distinct sub-populations of astrocytes conserved on both tissues. CFS includes various visualization methods for the interpretation of the similarity scores and similar cell populations.

Glucose-dependent insulinotropic polypeptide (GIP) was the first incretin identified and plays an essential role in the maintenance of glucose tolerance in healthy humans. Until recently GIP had not been developed as a therapeutic and thus has been overshadowed by the other incretin, glucagon-like peptide 1 (GLP-1), which is the basis for several successful drugs to treat diabetes and obesity. However, there has been a rekindling of interest in GIP biology in recent years, in great part due to pharmacology demonstrating that both GIPR agonism and antagonism may be beneficial in treating obesity and diabetes. This apparent paradox has reinvigorated the field, led to new lines of investigation, and deeper understanding of GIP.

In this review, we provide a detailed overview on the multifaceted nature of GIP biology and discuss the therapeutic implications of GIPR signal modification on various diseases.

Following its classification as an incretin hormone, GIP has emerged as a pleiotropic hormone with a variety of metabolic effects outside the endocrine pancreas. The numerous beneficial effects of GIPR signal modification render the peptide an interesting candidate for the development of pharmacotherapies to treat obesity, diabetes, drug-induced nausea and both bone and neurodegenerative disorders.

Cystic fibrosis related diabetes (CFRD) is the most common comorbidity of cystic fibrosis (CF) still, its pathogenesis is poorly understood. Recent studies have suggested that although pancreatic insufficiency is an important explanation for CFRD development, inherent pancreatic islet cell dysfunction may play a role. This study aimed to systematically compile current data regarding the impact of pancreatic islet cell dysfunction on the development of CFRD.

A systematic search was conducted in PubMed and Embase. The resulting articles were screened for relevant experimental design and outcomes. Articles underwent data extraction and quality assessment before compilation and analysis of the results.

A total of 268 articles were initially screened and 19 studies conducted between 2006-2022 were finally included in this review. Half of the studies in human tissue and most of the studies in animal tissue could detect CFTR in the islets. Similarly, half of the publications in human islets and most studies in animal islets detect decreased insulin secretion with inhibition/mutation of CFTR.

The literature on the role of islet CFTR is contradictory. However, a pattern emerges where CFTR loss-of-function mutations have the potential to negatively affect islet cell function in a way that, together with previously described exocrine damage occurring in CF, could play a part in the development of CFRD.

Foundation models such as scGPT and Geneformer have not been rigorously evaluated in a setting where they are used without any further training (i.e., zero-shot). Understanding the performance of models in zero-shot settings is critical to applications that exclude the ability to fine-tune, such as discovery settings where labels are unknown. Our evaluation of the zero-shot performance of Geneformer and scGPT suggests that, in some cases, these models may face reliability challenges and could be outperformed by simpler methods. Our findings underscore the importance of zero-shot evaluations in development and deployment of foundation models in single-cell research.

The development of long-acting incretin receptor agonists represents a significant advance in the fight against the concurrent epidemics of type 2 diabetes mellitus (T2DM) and obesity. The aim of the present review is to examine the cellular processes underlying the actions of these new, highly significant classes of peptide receptor agonists. We further explore the potential actions of multi-agonist drugs as well as the mechanisms through which gut-brain communication can be used to achieve long-term weight loss without negative side effects.

Several unimolecular dual-receptor agonists have shown promising clinical efficacy studies when used alone or in conjunction with approved glucose-lowering medications. We also describe the development of incretin-based pharmacotherapy, starting with exendin- 4 and ending with the identification of multi-incretin hormone receptor agonists, which appear to be the next major step in the fight against T2DM and obesity. We discuss the multi-agonists currently in clinical trials and how each new generation of these drugs improves their effectiveness. Since most glucose-dependent insulinotropic polypeptide (GIP) receptor: glucagon-like peptide- 1 receptor (GLP- 1) receptor: glucagon receptor triagonists compete in efficacy with bariatric surgery, the success of these agents in preclinical models and clinical trials suggests a bright future for multi-agonists in the treatment of metabolic diseases. To fully understand how these treatments affect body weight, further research is needed.

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.

Accurately identifying cell types in single-cell RNA sequencing data is critical for understanding cellular differentiation and pathological mechanisms in downstream analysis. As traditional biological approaches are laborious and time-intensive, it is imperative to develop computational biology methods for cell classification. However, it remains a challenge for existing methods to adequately utilize the potential gene expression information within the vast amount of unlabeled cell data, which limits their classification and generalization performance. Therefore, we propose a novel self-supervised graph representation learning framework for single-cell classification, named scSSGC. Specifically, in the pre-training stage of self-supervised learning, multiple K-means clustering tasks conducted on unlabeled cell data are jointly employed for model training, thereby mitigating the issue of limited labeled data. To effectively capture the potential interactions among cells, we introduce a locally augmented graph neural network to enhance the information aggregation capability for nodes with fewer neighbors in the cell graph. A range of benchmark experiments demonstrates that scSSGC outperforms existing state-of-the-art cell classification methods. More importantly, scSSGC provides stable performance when faced with cross-datasets, indicating better generalization ability.

In periods of sustained hyper-nutrition, pancreatic β-cells undergo functional compensation through transcriptional upregulation of gene programs driving insulin secretion. This adaptation is essential for maintaining systemic glucose homeostasis and metabolic health. Using single nuclei multiomics, we have mapped the early transcriptional compensation mechanisms in murine islets of Langerhans exposed to high-fat diet (HFD) for one and three weeks. We show that β-cells exhibit the largest transcriptional response to HFD, characterized by early activation of proinflammatory eRegulons and downregulation of β-cell identity genes, particularly in a distinct subset of β-cells. Our observations translate to humans, as we observe an increase in the inflammatory gene signatures in human β-cells in pre-diabetes and diabetes. Collectively, these observations point to cellular cross-talk through proinflammatory signaling as a central and early driver of β-cell dysfunction that limits the compensatory capacity of β-cells, which is closely linked to the development of diabetes.

Pancreatic beta cell mass is dynamically regulated in response to increased physiological and pathological demands. Understanding the mechanisms that control physiological beta cell proliferation could provide valuable insights into novel therapeutic approaches to diabetes. Here, we aimed to analyse the intracellular and extracellular signalling pathways involved in regulating the physiological proliferation of beta cells using single-cell RNA-seq (scRNA-seq) and in vitro functional assays.

Islets isolated from nulliparous mice, mice at different time points of gestation and mice at day 4 after delivery were analysed using scRNA-seq. Bioinformatics analyses of scRNA-seq data were performed to determine the heterogeneous transcriptomic characteristics of beta cells and to identify the proliferating subpopulation. CellChat was used to analyse cell-cell communication and identify the ligand-receptor pairs between beta cell subclusters as well as between non-beta cells and proliferating beta cells. In vitro functional assays were conducted in mouse and rat beta cell lines and isolated mouse primary islets to validate the role of Kmt5a- mono-methylation of histone H4 at lysine 20 (H4K20me) signalling and endothelial-derived heparin-binding EGF-like growth factor (HBEGF) in beta cell proliferation.

Of 43,724 endocrine and non-endocrine cells within islets analysed by scRNA-seq, 15,569 beta cells were clustered into eight distinct populations, each exhibiting unique heterogeneity. A proliferating beta cell subcluster was identified that highly expressed the histone methyltransferase Kmt5a. Activation of Kmt5a-H4K20me signalling upregulated the expression of Cdk1 and promoted beta cell proliferation. The crosstalk between endothelial cells and the proliferating beta cell subcluster, mediated by the HBEGF-EGF receptor (EGFR) ligand-receptor interaction, increased as beta cell mass expanded. HBEGF increased the expression levels of genes involved in the cell cycle and promoted beta cell proliferation by regulating the Kmt5a-H4K20me signalling pathway.

Our study demonstrates that, under physiological conditions, endothelial-derived HBEGF regulates beta cell proliferation through the Kmt5a-H4K20me signalling pathway, which may serve as a potential target to promote beta cell expansion and treat diabetes.

The scRNA-seq and RNA-seq datasets are available from the Gene Expression Omnibus (GEO) using the accession numbers GSE278860 and GSE278861, respectively.

Single-cell RNA sequencing (scRNA-seq) has unveiled extensive cellular heterogeneity, yet precise cell type annotation and the identification of novel cell populations remain significant challenges. scHeteroNet, a novel graph neural network framework specifically designed to leverage heterophily in scRNA-seq data, is presented. Unlike traditional methods that assume homophily, scHeteroNet captures complex cell-cell interactions by integrating information from both immediate and extended cellular neighborhoods, resulting in highly accurate cell representations. Additionally, scHeteroNet incorporates an innovative novelty propagation mechanism that robustly detects previously uncharacterized cell types. Comprehensive evaluations across diverse scRNA-seq datasets demonstrate that scHeteroNet consistently outperforms state-of-the-art approaches in both cell type classification and novel cell detection. This heterophily-aware approach enhances the ability to uncover cellular diversity, providing deeper insights into complex biological systems and advancing the field of single-cell analysis.

Type 2 diabetes (T2D) is characterized by relative insulin deficiency due to pancreatic beta cell dysfunction and insulin resistance in different tissues. Not only beta cells but also other islet cells (alpha, delta, and pancreatic polypeptide [PP]) are critical for maintaining glucose homeostasis in the body. In this overarching context and given that a deeper understanding of T2D pathophysiology and novel molecular targets is much needed, studies that integrate experimental and computational biology approaches offer veritable prospects for innovation. In this study, we report on single-cell RNA sequencing data integration with a generic Human1 model to generate context-specific genome-scale metabolic models for alpha, beta, delta, and PP cells for nondiabetic and T2D states and, importantly, at single-cell resolution. Moreover, flux balance analysis was performed for the investigation of metabolic activities in nondiabetic and T2D pancreatic cells. By altering glucose and oxygen uptakes to the metabolic networks, we documented the ways in which hypoglycemia, hyperglycemia, and hypoxia led to changes in metabolic activities in various cellular subsystems. Reporter metabolite analysis revealed significant transcriptional changes around several metabolites involved in sphingolipid and keratan sulfate metabolism in alpha cells, fatty acid metabolism in beta cells, and myoinositol phosphate metabolism in delta cells. Taken together, by leveraging genome-scale metabolic modeling, this research bridges the gap between metabolic theory and clinical practice, offering a comprehensive framework to advance our understanding of pancreatic metabolism in T2D, and contributes new knowledge toward the development of targeted precision medicine interventions.

Batch effect correction (BEC) is fundamental to integrate multiple single-cell RNA sequencing datasets, and its success is critical to empower in-depth interrogation for biological insights. However, no simple metric is available to evaluate BEC performance with sensitivity to data overcorrection, which erases true biological variations and leads to false biological discoveries. Here, we propose RBET, a reference-informed statistical framework for evaluating the success of BEC. Using extensive simulations and six real data examples including scRNA-seq and scATAC-seq datasets with different numbers of batches, batch effect sizes and numbers of cell types, we demonstrate that RBET evaluates the performance of BEC methods more fairly with biologically meaningful insights from data, while other methods may lead to false results. Moreover, RBET is computationally efficient, sensitive to overcorrection and robust to large batch effect sizes. Thus, RBET provides a robust guideline on selecting case-specific BEC method, and the concept of RBET is extendable to other modalities.

As single-cell genomics experiments increase in complexity and scale, the need to integrate multiple datasets has grown. Such integration enhances cellular feature identification by leveraging larger data volumes. However, batch effects-technical variations arising from differences in labs, times, or protocols-pose a significant challenge. Despite numerous proposed batch correction methods, many still have limitations, such as outputting only dimension-reduced data, relying on computationally intensive models, or resulting in overcorrection for batches with diverse cell type composition.

We introduce a novel method for batch effect correction named SCITUNA, a Single-Cell data Integration Tool Using Network Alignment. We perform evaluations on 39 individual batches from four real datasets and a simulated dataset, which include both scRNA-seq and scATAC-seq datasets, spanning multiple organisms and tissues. A thorough comparison of existing batch correction methods using 13 metrics reveals that SCITUNA outperforms current approaches and is successful at preserving biological signals present in the original data. In particular, SCITUNA shows a better performance than the current methods in all the comparisons except for the multiple batch integration of the lung dataset where the difference is 0.004.

SCITUNA effectively removes batch effects while retaining the biological signals present in the data. Our extensive experiments reveal that SCITUNA will be a valuable tool for diverse integration tasks.

Targeting beta cell proliferation is an appealing approach to restore glucose control in type 1 diabetes. However, the underlying mechanisms of beta cell proliferation remain incompletely understood, limiting identification of new therapeutic targets. Obesity is a naturally occurring process that potently induces human and rodent beta cell replication, representing an ideal model to study mechanisms of beta cell proliferation. We showed previously acute whole-body Lepr gene deletion in adult mice induces obesity and massive beta cell expansion. Here, using single-cell transcriptomics with female Lepr KO islets, we identified distinct populations of beta cells undergoing unfolded protein response (UPR), stress resolution, and cell cycle progression. Lepr KO beta cells undergoing UPR markedly increased chaperone protein, ribosomal biogenesis, and cell cycle transcriptional programs that were enriched for Xbp1 and Myc target genes. Our findings suggest a coordinated transcriptional mechanism involving Xbp1 and Myc to alleviate UPR and stimulate beta cell proliferation in obese female mice.

Mesenchymal stromal cells (MSC) play a critical role in the stem cell niche, a specialized microenvironment where stem cells reside and interact with surrounding cells and extracellular matrix components. Within the niche, MSC offer structural support, modulate inflammatory response, promote angiogenesis and release specific signaling molecules that influence stem cell behavior, including self-renewal, proliferation and differentiation. In epithelial tissues such as the intestine, stomach and liver, MSC act as an important source of cytokines and growth factors, but not much is known about their role in the pancreas. Our group has established a standardized technology for the generation of pancreatic organoids. Herein, we investigated the role of pancreatic mesenchymal stromal cells in the regulation of human pancreatic organoid proliferation and growth, using this 3D model in a co-culture system. We particularly focused on the capacity of pancreatic MSC to produce R-spondin factors, which are considered critical regulators of epithelial growth. We propose the development of a complex in vitro system that combines organoid technology and mesenchymal stromal cells, thereby promoting the assembloid new research era.

Diabetes is a systemic metabolic disorder primarily caused by insulin deficiency and insulin resistance, leading to chronic hyperglycemia. Prolonged diabetes can result in metabolic damage to multiple organs, including the heart, brain, liver, muscles, and adipose tissue, thereby causing various chronic fatal complications such as diabetic retinopathy, diabetic cardiomyopathy, and diabetic nephropathy. Single-cell RNA sequencing (scRNA-seq) has emerged as a valuable tool for investigating the cell diversity and pathogenesis of diabetes and identifying potential therapeutic targets in diabetes or diabetes complications. This review provides a comprehensive overview of recent applications of scRNA-seq in diabetes-related researches and highlights novel biomarkers and immunotherapy targets with cell-type information for diabetes and its associated complications.

We introduce CSsingle, a novel method that enhances the decomposition of bulk and spatial transcriptomic (ST) data by addressing key challenges in cellular heterogeneity. CSsingle applies cell size correction using ERCC spike-in controls, enabling it to account for variations in RNA content between cell types and achieve accurate bulk data deconvolution. In addition, it enables fine-scale analysis for ST data, advancing our understanding of tissue architecture and cellular interactions, particularly in complex microenvironments. We provide a unified tool for integrating bulk and ST with scRNA-seq data, advancing the study of complex biological systems and disease processes. The benchmark results demonstrate that CSsingle outperforms existing methods in accuracy and robustness. Validation using more than 700 normal and diseased samples from gastroesophageal tissue reveals the predominant presence of mosaic columnar cells (MCCs), which exhibit a gastric and intestinal mosaic phenotype in Barrett's esophagus and esophageal adenocarcinoma (EAC), in contrast to their very low detectable levels in esophageal squamous cell carcinoma and normal gastroesophageal tissue. We revealed a dynamic relationship between MCCs and squamous cells during immune checkpoint inhibitors (ICI)-based treatment in EAC patients, suggesting MCC expression signatures as predictive and prognostic markers of immunochemotherapy outcomes. Our findings reveal the critical role of MCC in the treatment of EAC and its potential as a biomarker to predict outcomes of immunochemotherapy, providing insight into tumor epithelial plasticity to guide personalized immunotherapeutic strategies.

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity by providing gene expression data at the single-cell level. Unlike bulk RNA-seq, scRNA-seq allows identification of different cell types within a given tissue, leading to a more nuanced comprehension of cell functions. However, the analysis of scRNA-seq data presents challenges due to its sparsity and high dimensionality. Since bioinformatics plays an important role in the analysis of big data and its utility for the welfare of living beings, it has been widely applied in analyzing scRNA-seq data. To address these challenges, we introduce the scMUG computational pipeline, which incorporates gene functional module information to enhance scRNA-seq clustering analysis. The pipeline includes data preprocessing, cell representation generation, cell-cell similarity matrix construction, and clustering analysis. The scMUG pipeline also introduces a novel similarity measure that combines local density and global distribution in the latent cell representation space. As far as we can tell, this is the first attempt to integrate gene functional associations into scRNA-seq clustering analysis. We curated nine human scRNA-seq datasets to evaluate our scMUG pipeline. With the help of gene functional information and the novel similarity measure, the clustering results from scMUG pipeline present deep insights into functional relationships between gene expression patterns and cellular heterogeneity. In addition, our scMUG pipeline also presents comparable or better clustering performances than other state-of-the-art methods. All source codes of scMUG have been deposited in a GitHub repository with instructions for reproducing all results (https://github.com/degiminnal/scMUG).

Glucagon is essential for glucose homeostasis, and its dysregulation is associated with diabetes. Despite extensive research, the mechanisms governing glucagon secretion remain incompletely understood. Here, we unveil that famsin, a gut-secreted hormone, promotes glucagon release and modulates glucose homeostasis. Mechanistically, famsin binds to its receptor OLFR796 in mice (OR10P1 in humans), initiating calcium release in the endoplasmic reticulum of islet α cells. This process triggers glucagon secretion, consequently promoting hepatic glucose production through glucagon signaling. Furthermore, deficiency of famsin signaling reduces hepatic glucose production and lowers blood glucose levels, underscoring the significance of the famsin-glucagon axis in glucose homeostasis. Therefore, our findings establish famsin as a crucial regulator of glucagon secretion and provide valuable insights into the intricate gut-islet-liver interorgan crosstalk that maintains glucose homeostasis.

This article reviews the impact of single-cell sequencing (SCS) on cancer biology research. SCS has revolutionized our understanding of cancer and tumor heterogeneity, clonal evolution, and the complex interplay between cancer cells and tumor microenvironment. SCS provides high-resolution profiling of individual cells in genomic, transcriptomic, and epigenomic landscapes, facilitating the detection of rare mutations, the characterization of cellular diversity, and the integration of molecular data with phenotypic traits. The integration of SCS with multi-omics has provided a multidimensional view of cellular states and regulatory mechanisms in cancer, uncovering novel regulatory mechanisms and therapeutic targets. Advances in computational tools, artificial intelligence (AI), and machine learning have been crucial in interpreting the vast amounts of data generated, leading to the identification of new biomarkers and the development of predictive models for patient stratification. Furthermore, there have been emerging technologies such as spatial transcriptomics and in situ sequencing, which promise to further enhance our understanding of tumor microenvironment organization and cellular interactions. As SCS and its related technologies continue to advance, they are expected to drive significant advances in personalized cancer diagnostics, prognosis, and therapy, ultimately improving patient outcomes in the era of precision oncology.

Type 1 diabetes (T1D) is a multifactorial disease involving genetic and environmental factors, including viral infection. We investigated the impact of interferon alpha (IFN-α), a cytokine produced during the immune response to viral infection or the presence of un-edited endogenous double-stranded RNAs, on human β-cell physiology. Intravital microscopy on transplanted human islets using a β-cell-selective reactive oxygen species (ROS) biosensor (RIP1-GRX1-roGFP2), revealed a subset of human β-cells that acutely produce ROS in response to IFN-α. Comparison to Integrated Islet Distribution Program (IIDP) phenotypic data revealed that healthier donors had more ROS accumulating cells. In vitro IFN-α treatment of human islets similarly elicited a heterogenous increase in superoxide production that originated in the mitochondria. To determine the unique molecular signature predisposing cells to IFN-α stimulated ROS production, we flow sorted human islets treated with IFN-α. RNA sequencing identified genes involved in inflammatory and immune response in the ROS-producing cells. Comparison with single cell RNA-Seq datasets available through the Human Pancreas Analysis Program (HPAP) showed that genes upregulated in ROS-producing cells are enriched in control β-cells rather than T1D donors. Combined, these data suggest that IFN-α stimulates mitochondrial ROS production in healthy human β-cells, potentially predicting a more efficient antiviral response.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for understanding cellular heterogeneity, providing unprecedented resolution in molecular regulation analysis. Existing supervised learning approaches for cell type annotation primarily utilize gene expression profiles from scRNA-seq data. Although some methods incorporated gene interaction network information, they fail to use cell-specific gene association networks. This limitation overlooks the unique gene interaction patterns within individual cells, potentially compromising the accuracy of cell type classification. We introduce WCSGNet, a graph neural network-based algorithm for automatic cell-type annotation that leverages Weighted Cell-Specific Networks (WCSNs). These networks are constructed based on highly variable genes and inherently capture both gene expression patterns and gene association network structure features. Extensive experimental validation demonstrates that WCSGNet consistently achieves superior cell type classification performance, ranking among the top-performing methods while maintaining robust stability across diverse datasets. Notably, WCSGNet exhibits a distinct advantage in handling imbalanced datasets, outperforming existing methods in these challenging scenarios. All datasets and codes for reproducing this work were deposited in a GitHub repository (https://github.com/Yi-ellen/WCSGNet).

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.

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.

The quest to find a progenitor cell in the adult pancreas has driven research in the field for decades. Many potential progenitor cell sources have been reported, but so far this is a matter of debate mainly due to reproducibility issues. The existence of adult Procr+ progenitor cells in mice islets has been recently reported. These were shown to comprise ~1% of islet cells, lack expression of Neurog3 and endocrine hormones, and to be capable of differentiating into all endocrine cell types. However, these findings had limited impact, as further evidence supporting the existence and function of Procr+ progenitors has not emerged.

We report here an unbiased comparison across mouse and human pancreatic samples, including adult islets and embryonic tissue, to track the existence of Procr+ progenitors originally described based on their global gene expression signature. We could not find Procr+ progenitors on other mouse or human adult pancreatic islet samples. Unexpectedly, our results revealed a transcriptionally close mesothelial cell population in the mouse and human embryonic pancreas. These Procr-like mesothelial cells of the embryonic pancreas share the salient transcriptional and epigenomic features of previously reported Procr+ progenitors found in adult pancreatic islets. Notably, we report here that Procr-like transcriptional signature is gradually established in mesothelial cells during mouse pancreas development from E12.5 to E17.5, which has its largest amount. Further supporting a developmentally relevant role in the human pancreas, we additionally report that a transcriptionally similar population is spontaneously differentiated from human pluripotent stem cells cultured in vitro along the pancreatic lineage.

Our results show that, although the previously reported Procr+ progenitor cell population could not be found in other adult pancreatic islet samples, a mesothelial cell population with a closely related transcriptional signature is present in both the mouse and human embryonic pancreas. Several lines of evidence presented in this work support a developmentally relevant function for these Procr-like mesothelial cells.

Few effective treatments have been developed for intractable pancreatic exocrine disorders due to the lack of suitable disease models using human cells. Pancreatic acinar cells differentiated from human induced pluripotent stem cells (hiPSCs) have the potential to solve this issue. In this study, we aimed to elucidate the developmental mechanisms of pancreatic exocrine acinar lineages to establish a directed differentiation method for pancreatic acinar cells from hiPSCs. hiPSC-derived pancreatic endoderm cells were spontaneously differentiated into both pancreatic exocrine and endocrine tissues by implantation into the renal subcapsular space of NOD/SCID mice. Single-cell RNA-seq analysis of the retrieved grafts confirmed the differentiation of pancreatic acinar lineage cells and identified REG4 as a candidate marker for pancreatic acinar progenitor cells. Furthermore, differential gene expression analysis revealed upregulated pathways, including cAMP-related signals, involved in the differentiation of hiPSC-derived pancreatic acinar lineage cells in vivo, and we found that a cAMP activator, forskolin, facilitates the differentiation from hiPSC-derived pancreatic endoderm into pancreatic acinar progenitor cells in our in vitro differentiation culture. Therefore, this platform contributes to our understanding of the developmental mechanisms of pancreatic acinar lineage cells and the establishment of differentiation methods for acinar cells from hiPSCs.

Characterization of the vascular heterogeneity within the pancreas has previously been lacking. Here, we develop strategies to enrich islet-specific endothelial cells (ISECs) and acinar-specific endothelial cells (ASECs) from three human pancreases and corroborate these findings with three published pancreatic datasets. Single-cell RNA sequencing reveals the unique molecular signatures of ISECs, including structural genes COL13A1, ESM1, PLVAP, UNC5B, and LAMA4, angiocrine genes KDR, THBS1, BMPs and CXCR4, and metabolic genes ACE, PASK and F2RL3. ASECs display distinct signatures including GPIHBP1, CCL14, CD74, AQP1, KLF4, and KLF2, which may manage the inflammatory and metabolic needs of the exocrine pancreas. Ligand-receptor analysis suggests ISECs and ASECs interact with LUM+ fibroblasts and RGS5+ pericytes and smooth muscle cells via VEGF-A:VEGFR2, CXCL12:CXCR4, and LIF:LIFR pathways. Comparative expression and immunohistochemistry indicate disruption of endothelial-expressed CD74, ESM1, PLVAP, THBD, VWA1, and VEGF-A cross-talk among vascular and other cell types in diabetes. Thus, our data provide a single-cell vascular atlas of human pancreas, enabling deeper understanding of pancreatic pathophysiology in health and disease.

Type 2 diabetes affects more than 30 million people in the US, and a major complication is kidney disease. During the analysis of lipotoxicity in diabetic kidney disease, global fatty acid transport protein-2 (FATP2) gene deletion was noted to markedly reduce plasma glucose in db/db mice due to sustained insulin secretion. To identify the mechanism, we observed that islet FATP2 expression was restricted to α-cells, and α-cell FATP2 was functional. Direct evidence of FATP2KO-induced α-cell-mediated GLP-1 secretion included increased GLP-1-positive α-cell mass in FATP2KO db/db mice, small molecule FATP2 inhibitor enhancement of GLP-1 secretion in αTC1-6 cells and human islets, and exendin[9-39]-inhibitable insulin secretion in FATP2 inhibitor-treated human islets. FATP2-dependent enteroendocrine GLP-1 secretion was excluded by demonstration of similar glucose tolerance and plasma GLP-1 concentrations in db/db FATP2KO mice following oral versus intraperitoneal glucose loading, non-overlapping FATP2 and preproglucagon mRNA expression, and lack of FATP2/GLP-1 co-immunolocalization in intestine. We conclude that FATP2 deletion or inhibition exerts glucose-lowering effects through α-cell-mediated GLP-1 secretion and paracrine β-cell insulin release.

Glucose-stimulated insulin release from pancreatic β-cells is critical for maintaining blood glucose homeostasis. An abrupt increase in blood glucose concentration evokes a rapid and transient rise in insulin secretion followed by a prolonged, slower phase. A diminished first phase is one of the earliest indicators of β-cell dysfunction in individuals predisposed to develop type 2 diabetes. Consequently, researchers have explored the underlying mechanisms for decades, starting with plasma insulin measurements under physiological conditions and advancing to single-vesicle exocytosis measurements in individual β-cells combined with molecular manipulations. Based on a chain of evidence gathered from genetic manipulation to in vivo mouse phenotyping, a widely accepted theory posits that distinct functional insulin vesicle pools in β-cells regulate biphasic glucose-stimulated insulin secretion (GSIS) via activation of different metabolic signal pathways. Recently, we developed a high-resolution imaging technique to visualize single vesicle exocytosis from β-cells within an intact islet. Our findings reveal that β-cells within the islet exhibit heterogeneity in their secretory capabilities, which also differs from the heterogeneous Ca2+ signals observed in islet β-cells in response to glucose stimulation. Most importantly, we demonstrate that biphasic GSIS emerges from the interactions among α-, β-, and δ-cells within the islet and is driven by a small subset of hypersecretory β-cells. Finally, we propose that a shift from reductionism to holism may be required to fully understand the etiology of complex diseases such as diabetes.

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.

Diabetes is characterized by variable loss of insulin-producing beta cells, and new regenerative approaches to increasing the functional beta cell mass of patients hold promise for reversing disease progression. In this Review, we summarize recent chemical biology breakthroughs advancing our knowledge of beta cell regeneration. We present current chemical-based tools, sensors and mechanistic insights into pathways that can be targeted to enhance beta cell regeneration in model organisms. We group the pathways according to the cellular processes they affect, that is, proliferation, conversion of other mature cell types to beta cells and beta cell differentiation from progenitor-like populations. We also suggest assays for assessing the functionality of the regenerated beta cells. Although regeneration processes differ between animal models, such as zebrafish, mice and pigs, regenerative mechanisms identified in any one animal model may be translatable to humans. Overall, chemical biology-based approaches in beta cell regeneration give hope that specific molecular pathways can be targeted to enhance beta cell regeneration.

Recent advancements in single-cell RNA sequencing have greatly expanded our knowledge of the heterogeneous nature of tissues. However, robust and accurate cell type annotation continues to be a major challenge, hindered by issues such as marker specificity, batch effects, and a lack of comprehensive spatial and interaction data. Traditional annotation methods often fail to adequately address the complexity of cellular interactions and gene regulatory networks.

We proposed scMCGraph, a comprehensive computational framework that integrates gene expression with pathway activity to accurately annotate cell types within diverse scRNA-seq datasets. Initially, our model constructs multiple pathway-specific views using various pathway databases, which reflect both gene expression and pathway activities. These pathway-specific views are then integrated into a consensus graph. The consensus graph is subsequently utilized to reconstruct the multiple pathway views. Our model demonstrated exceptional robustness and accuracy across various analyses, including cross-platform, cross-time, cross-sample, and clinical dataset evaluations.

scMCGraph represents a significant advance in cell type annotation. The experiments have demonstrated that introducing pathway information significantly improves the learning of cell-cell graphs, with their resulting consensus graph enhancing the predictive performance of cell type prediction. Different pathway databases provide complementary data, and an increase in the number of pathways can also boost model performance. Extensive testing shows that in various cross-dataset application scenarios, scMCGraph consistently exhibits both accuracy and robustness.

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.

Type 1 diabetes (T1D) is related to the autoimmune destruction of β-cells, leading to their almost complete absence in patients with longstanding T1D. However, endogenous insulin secretion persists in such patients as evidenced by the measurement of plasma C-peptide. Recently, a low level of insulin has been found in non-β islet cells of patients with longstanding T1D, indicating that other islet cell types may contribute to persistent insulin secretion. The present study aimed to test the ability of various antibodies to detect insulin in insulin-deficient islets of T1D patients. Pancreatic autopsies from two children with recent-onset T1D, two adults with longstanding T1D, and three control subjects were examined using double immunofluorescent labeling with antibodies to insulin, glucagon and somatostatin. Immunoreactivity to insulin in glucagon+ cells of insulin-deficient islets was revealed using polyclonal antibodies and monoclonal antibodies simultaneously recognizing insulin and proinsulin. Along with this, immunoreactivity to insulin was observed in the majority of glucagon+ cells of insulin-containing islets of control subjects and children with recent-onset T1D. These results suggest that islet α-cells may contain insulin and/or other insulin-like proteins (proinsulin, C-peptide). Future studies are needed to evaluate the role of α-cells in insulin secretion and diabetes pathogenesis.

Interferon (IFN)-α is the earliest cytokine signature observed in individuals at risk for type 1 diabetes (T1D), but the effect of IFN-α on the antigen repertoire of HLA Class I (HLA-I) in pancreatic β-cells is unknown. Here we characterize the HLA-I antigen presentation in resting and IFN-α-exposed β-cells and find that IFN-α increases HLA-I expression and expands peptide repertoire to those derived from alternative mRNA splicing, protein cis-splicing and post-translational modifications. While the resting β-cell immunopeptidome is dominated by HLA-A-restricted peptides, IFN-α largely favors HLA-B and only marginally upregulates HLA-A, translating into increased HLA-B-restricted peptide presentation and activation of HLA-B-restricted CD8+ T cells. Lastly, islets of patients with T1D show preferential HLA-B hyper-expression when compared with non-diabetic donors, and islet-infiltrating CD8+ T cells reactive to HLA-B-restricted granule peptides are found in T1D donors. Thus, the inflammatory milieu of insulitis may skew the autoimmune response toward alternative epitopes presented by HLA-B, hence recruiting T cells with a distinct repertoire that may be relevant to T1D pathogenesis.

Type 2 diabetes (T2D) has become a significant global health threat, yet its precise causes and mechanisms remain unclear. This study aims to identify gene expression patterns specific to T2D pancreatic islet cells and to explore the potential role of pancreatic stellate cells (PSCs) in T2D progression through regulatory networks involving lncRNA-mRNA interactions.

In this study, we screened for upregulated genes in T2D pancreatic islet samples using bulk sequencing (bulkseq) datasets and mapped these gene expression profiles onto three T2D single-cell RNA sequencing (scRNAseq) datasets. The identified T2D-specific gene features were further validated in an additional T2D scRNAseq dataset, a T1D scRNAseq dataset, and a T2D bulkseq dataset. To investigate regulatory networks, we analyzed the potential lncRNA-mRNA interactions within T2D peripheral blood mononuclear cell (PBMC) bulkseq data.

Our analysis identified a specific gene panel-COL1A2, VCAN, and SULF1-that was consistently upregulated in T2D pancreatic islet samples. Expression of this gene panel was strongly associated with the activation of pancreatic stellate cells (PSCs), suggesting a unique T2D-specific signature characterized by COL1A2hi/VCANhi/SULF1hi PSCs. This signature was exclusive to T2D and was not observed in Type 1 diabetes (T1D) samples, indicating a distinct role for activated PSCs in T2D progression. Furthermore, we identified six long non-coding RNAs (lncRNAs) that potentially interact with the COL1A2hi/VCANhi/SULF1hi PSCs. These lncRNAs were mapped to a lncRNA-mRNA network, suggesting they may modulate immune responses and potentially reshape the immune microenvironment in T2D.

Our findings highlight the potential immune-regulatory role of PSCs in T2D and suggest that PSC-related lncRNA-mRNA networks could serve as novel therapeutic targets for T2D treatment. This research provides insights into PSCs as a modulator in T2D progression, paving the way for innovative treatment strategies.

Single-cell sequencing (scRNA-seq) allows researchers to study cellular heterogeneity in individual cells. In single-cell transcriptomics analysis, identifying the cell type of individual cells is a key task. At present, single-cell datasets often face the challenges of high dimensionality, large number of samples, high sparsity and sample imbalance. The traditional methods of cell type recognition have been challenged. The authors propose a deep residual generation model based on semi-supervised learning (scRSSL) to address these challenges. ScRSSL creatively introduces residual networks into semi-supervised generative models. The authors take advantage of its semi-supervised learning to solve the problem of sample imbalance. During the training of the model, the authors use a residual neural network to accomplish the inference of cell types so that local features of single-cell data can be extracted. Because of the semi-supervised learning approach, it can automatically and accurately predict individual cell types in datasets, even with only a small number of cell labels. Experimentally, the authors' method has proven to have better performance compared to other methods.

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.

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.

Inflammation contributes to the pathophysiology of diabetes. Identifying signaling pathways involved in pancreatic β-cell failure and identity loss can give insight into novel potential treatment strategies to prevent the loss of functional β-cell mass in diabetes. It is reported earlier that the immunosuppressive drug tacrolimus has a detrimental effect on human β-cell identity and function by activating bone morphogenetic protein (BMP) signaling. Here it is hypothesized that enhanced BMP signaling plays a role in inflammation-induced β-cell failure. Single-cell transcriptomics analyses of primary human islets reveal that IL-1β+IFNγ and IFNα treatment activated BMP signaling in β-cells. These findings are validated by qPCR. Furthermore, enhanced BMP signaling with recombinant BMP2 or 4 triggers a reduced expression of key β-cell maturity genes, associated with increased ER stress, and impaired β-cell function. Altogether, these results indicate that inflammation-activated BMP signaling is detrimental to pancreatic β-cells and that BMP-signaling can be a target to preserve β-cell identity and function in a pro-inflammatory environment.

Cell type deconvolution methods can impute cell proportions from bulk transcriptomics data, revealing changes in disease progression or organ development. But benchmarking studies often use simulated bulk data from the same source as the reference, which limits its application scenarios. This study examines batch effects in deconvolution and introduces SCCAF-D, a computational workflow that ensures a Pearson Correlation Coefficient above 0.75 across simulated and real bulk data for various tissue types. Applied to non-alcoholic fatty liver disease, SCCAF-D unveils meaningful insights into changes in cell proportions during disease progression.