Integration of single-cell datasets reveals novel transcriptomic signatures of β-cells in human type 2 diabetes

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.

14-3-3ζ regulates adipogenesis by modulating chromatin accessibility during the early stages of adipocyte differentiation

We previously established the scaffold protein 14-3-3ζ as a critical regulator of adipogenesis and adiposity, but the temporal specificity of its action during adipocyte differentiation remains unclear. To decipher if 14-3-3ζ exerts its regulatory functions on mature adipocytes or on adipose precursor cells (APCs), we generated Adipoq14-3-3ζKO and Pdgfra14-3-3ζKO mouse models. Our findings revealed a pivotal role for 14-3-3ζ in APC differentiation in a sex-dependent manner, whereby male and female Pdgfra14-3-3ζKO mice display impaired or potentiated weight gain, respectively, as well as fat mass. To better understand how 14-3-3ζ regulates the adipogenic transcriptional program in APCs, CRISPR-Cas9 was used to generate TAP-tagged 14-3-3ζ-expressing 3T3-L1 preadipocytes. Using these cells, we examined if the 14-3-3ζ nuclear interactome is enriched with adipogenic regulators during differentiation. Regulators of chromatin remodeling, such as DNMT1 and HDAC1, were enriched in the nuclear interactome of 14-3-3ζ, and their activities were impacted upon 14-3-3ζ depletion. The interactions between 14-3-3ζ and chromatin-modifying enzymes suggested that 14-3-3ζ may control chromatin remodeling during adipogenesis, and this was confirmed by ATAC-seq, which revealed that 14-3-3ζ depletion impacted the accessibility of up to 1,244 chromatin regions corresponding in part to adipogenic genes, promoters, and enhancers during the initial stages of adipogenesis. Moreover, 14-3-3ζ-dependent chromatin accessibility was found to directly correlate with the expression of key adipogenic genes. Altogether, our study establishes 14-3-3ζ as a crucial epigenetic regulator of adipogenesis and highlights the usefulness of deciphering the nuclear 14-3-3ζ interactome to identify novel pro-adipogenic factors and pathways.

Integrative single-cell characterization of a frugivorous and an insectivorous bat kidney and pancreas

Frugivory evolved multiple times in mammals, including bats. However, the cellular and molecular components driving it remain largely unknown. Here, we use integrative single-cell sequencing (scRNA-seq and scATAC-seq) on insectivorous (Eptesicus fuscus; big brown bat) and frugivorous (Artibeus jamaicensis; Jamaican fruit bat) bat kidneys and pancreases and identify key cell population, gene expression and regulatory differences associated with the Jamaican fruit bat that also relate to human disease, particularly diabetes. We find a decrease in loop of Henle and an increase in collecting duct cells, and differentially active genes and regulatory elements involved in fluid and electrolyte balance in the Jamaican fruit bat kidney. The Jamaican fruit bat pancreas shows an increase in endocrine and a decrease in exocrine cells, and differences in genes and regulatory elements involved in insulin regulation. We also find that these frugivorous bats share several molecular characteristics with human diabetes. Combined, our work provides insights from a frugivorous mammal that could be leveraged for therapeutic purposes.

Is 14-3-3 the Combination to Unlock New Pathways to Improve Metabolic Homeostasis and β-Cell Function?

Since their discovery nearly five decades ago, molecular scaffolds belonging to the 14-3-3 protein family have been recognized as pleiotropic regulators of diverse cellular and physiological functions. With their ability to bind to proteins harboring specific serine and threonine phosphorylation motifs, 14-3-3 proteins can interact with and influence the function of docking proteins, enzymes, transcription factors, and transporters that have essential roles in metabolism and glucose homeostasis. Here, we will discuss the regulatory functions of 14-3-3 proteins that will be of great interest to the fields of metabolism, pancreatic β-cell biology, and diabetes. We first describe how 14-3-3 proteins play a central role in glucose and lipid homeostasis by modulating key pathways of glucose uptake, glycolysis, oxidative phosphorylation, and adipogenesis. This is followed by a discussion of the contributions of 14-3-3 proteins to calcium-dependent exocytosis and how this relates to insulin secretion from β-cells. As 14-3-3 proteins are major modulators of apoptosis and cell cycle progression, we will explore if 14-3-3 proteins represent a viable target for promoting β-cell regeneration and discuss the feasibility of targeting 14-3-3 proteins to treat metabolic diseases such as diabetes.

ARTICLE HIGHLIGHTS

14-3-3 proteins are ubiquitously expressed scaffolds with multiple roles in glucose homeostasis and metabolism. 14-3-3ζ regulates adipogenesis via distinct mechanisms and is required for postnatal adiposity and adipocyte function. 14-3-3ζ controls glucose-stimulated insulin secretion from pancreatic β-cells by regulating mitochondrial function and ATP synthesis as well as facilitating cross talk between β-cells and α-cells.

Integrative single-cell characterization of frugivory adaptations in the bat kidney and pancreas

Frugivory evolved multiple times in mammals, including bats. However, the cellular and molecular components driving it remain largely unknown. Here, we used integrative single-cell sequencing on insectivorous and frugivorous bat kidneys and pancreases and identified key cell population, gene expression and regulatory element differences associated with frugivorous adaptation that also relate to human disease, particularly diabetes. We found an increase in collecting duct cells and differentially active genes and regulatory elements involved in fluid and electrolyte balance in the frugivore kidney. In the frugivorous pancreas, we observed an increase in endocrine and a decrease in exocrine cells and differences in genes and regulatory elements involved in insulin regulation. Combined, our work provides novel insights into frugivorous adaptation that also could be leveraged for therapeutic purposes.

Human alpha cell transcriptomic signatures of types 1 and 2 diabetes highlight disease-specific dysfunction pathways

Although glucagon secretion is perturbed in both T1D and T2D, the pathophysiological changes in individual pancreatic alpha cells are still obscure. Using recently curated single-cell RNASeq data from T1D or T2D donors and their controls, we identified alpha cell transcriptomic alterations consistent with both common and discrete pathways. Although alterations in alpha cell identity gene (ARX, MAFB) expression were conserved, cytokine-regulated genes and genes involved in glucagon biosynthesis and processing were up-regulated in T1D. Conversely, mitochondrial genes associated with ROS (COX7B, NQO2) were dysregulated in T2D. Additionally, T1D alpha cells displayed altered expression of autoimmune-induced ER stress genes (ERLEC1, HSP90), whilst those from T2D subjects showed modified glycolytic and citrate cycle gene (LDHA?, PDHB, PDK4) expression. Thus, despite conserved alterations related to loss of function, alpha cells display disease-specific gene signatures which may be secondary to the main pathogenic events in each disease, namely immune- or metabolism-mediated-stress, in T1D and T2D, respectively.

A single-cell human islet interactome atlas identifies disrupted autocrine and paracrine communications in type 2 diabetes

A sensible control of hormone secretion from pancreatic islets requires concerted inter-cellular communications, but a comprehensive picture of the whole islet interactome is presently missing. Single-cell transcriptomics allows to overcome this and we used here a single-cell dataset from type 2 diabetic (T2D) and non-diabetic (ND) donors to leverage islet interaction networks. The single-cell dataset contains 3046 cells classified in 7 cell types. The interactions across cell types in T2D and ND were obtained and resulting networks analysed to identify high-centrality genes and altered interactions in T2D. The T2D interactome displayed a higher number of interactions (10 787) than ND (9707); 1289 interactions involved beta cells (1147 in ND). High-centrality genes included EGFR, FGFR1 and FGFR2, important for cell survival and proliferation. In conclusion, this analysis represents the first in silico model of the human islet interactome, enabling the identification of signatures potentially relevant for T2D pathophysiology.

Lessons from single-cell RNA sequencing of human islets

Islet dysfunction is central in type 2 diabetes and full-blown type 2 diabetes develops first when the beta cells lose their ability to secrete adequate amounts of insulin in response to raised plasma glucose. Several mechanisms behind beta cell dysfunction have been put forward but many important questions still remain. Furthermore, our understanding of the contribution of each islet cell type in type 2 diabetes pathophysiology has been limited by technical boundaries. Closing this knowledge gap will lead to a leap forward in our understanding of the islet as an organ and potentially lead to improved treatments. The development of single-cell RNA sequencing (scRNAseq) has led to a breakthrough for characterising the transcriptome of each islet cell type and several important observations on the regulation of cell-type-specific gene expression have been made. When it comes to identifying type 2 diabetes disease mechanisms, the outcome is still limited. Several studies have identified differentially expressed genes, although there is very limited consensus between the studies. As with all new techniques, scRNAseq has limitations; in addition to being extremely expensive, genes expressed at low levels may not be detected, noise may not be appropriately filtered and selection biases for certain cell types are at hand. Furthermore, recent advances suggest that commonly used computational tools may be suboptimal for analysis of scRNAseq data in small-scale studies. Fortunately, development of new computational tools holds promise for harnessing the full potential of scRNAseq data. Here we summarise how scRNAseq has contributed to increasing the understanding of various aspects of islet biology as well as type 2 diabetes disease mechanisms. We also focus on challenges that remain and propose steps to promote the utilisation of the full potential of scRNAseq in this area.

DGCyTOF: Deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data

Single-cell mass cytometry, also known as cytometry by time of flight (CyTOF) is a powerful high-throughput technology that allows analysis of up to 50 protein markers per cell for the quantification and classification of single cells. Traditional manual gating utilized to identify new cell populations has been inadequate, inefficient, unreliable, and difficult to use, and no algorithms to identify both calibration and new cell populations has been well established. A deep learning with graphic cluster (DGCyTOF) visualization is developed as a new integrated embedding visualization approach in identifying canonical and new cell types. The DGCyTOF combines deep-learning classification and hierarchical stable-clustering methods to sequentially build a tri-layer construct for known cell types and the identification of new cell types. First, deep classification learning is constructed to distinguish calibration cell populations from all cells by softmax classification assignment under a probability threshold, and graph embedding clustering is then used to identify new cell populations sequentially. In the middle of two-layer, cell labels are automatically adjusted between new and unknown cell populations via a feedback loop using an iteration calibration system to reduce the rate of error in the identification of cell types, and a 3-dimensional (3D) visualization platform is finally developed to display the cell clusters with all cell-population types annotated. Utilizing two benchmark CyTOF databases comprising up to 43 million cells, we compared accuracy and speed in the identification of cell types among DGCyTOF, DeepCyTOF, and other technologies including dimension reduction with clustering, including Principal Component Analysis (PCA), Factor Analysis (FA), Independent Component Analysis (ICA), Isometric Feature Mapping (Isomap), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) with k-means clustering and Gaussian mixture clustering. We observed the DGCyTOF represents a robust complete learning system with high accuracy, speed and visualization by eight measurement criteria. The DGCyTOF displayed F-scores of 0.9921 for CyTOF1 and 0.9992 for CyTOF2 datasets, whereas those scores were only 0.507 and 0.529 for the t-SNE+k-means; 0.565 and 0.59, for UMAP+ k-means. Comparison of DGCyTOF with t-SNE and UMAP visualization in accuracy demonstrated its approximately 35% superiority in predicting cell types. In addition, observation of cell-population distribution was more intuitive in the 3D visualization in DGCyTOF than t-SNE and UMAP visualization. The DGCyTOF model can automatically assign known labels to single cells with high accuracy using deep-learning classification assembling with traditional graph-clustering and dimension-reduction strategies. Guided by a calibration system, the model seeks optimal accuracy balance among calibration cell populations and unknown cell types, yielding a complete and robust learning system that is highly accurate in the identification of cell populations compared to results using other methods in the analysis of single-cell CyTOF data. Application of the DGCyTOF method to identify cell populations could be extended to the analysis of single-cell RNASeq data and other omics data.

Single-cell mass cytometry, also known as cytometry by time of flight is a powerful high-throughput technology that allows analysis of up to 50 protein markers per cell for the quantification and classification of single cells. Traditional manual gating utilized to identify new cell populations has been inadequate, inefficient, unreliable, and difficult to use. A deep learning with graphic cluster (DGCyTOF) visualization is developed in identifying canonical and new cell types. Utilizing two benchmark CyTOF databases comprising up to 43 million cells, we compared accuracy and speed in the identification of cell types among DGCyTOF, DeepCyTOF, and other technologies including dimension reduction with clustering, including Principal Component Analysis, Factor Analysis, Independent Component Analysis, Isometric Feature Mapping, t-distributed Stochastic Neighbor Embedding, and Uniform Manifold Approximation and Projection with k-means clustering and Gaussian mixture clustering. We observed that DGCyTOF outperformed all the other methods in accuracy, speed and visualization. The application of the DGCyTOF method to identify cell populations could be extended to the analysis of single-cell RNASeq data and other omics data.

Mechanisms underlying the pathophysiology of type 2 diabetes: From risk factors to oxidative stress, metabolic dysfunction, and hyperglycemia

Type 2 diabetes (T2D) is a complex multifactorial disease that emerges from the combination of genetic and environmental factors, and obesity, lifestyle, and aging are the most relevant risk factors. Hyperglycemia is the main metabolic feature of T2D as a consequence of insulin resistance and β-cell dysfunction. Among the cellular alterations induced by hyperglycemia, the overproduction of reactive oxygen species (ROS) and consequently oxidative stress, accompanied by a reduced antioxidant response and impaired DNA repair pathways, represent essential mechanisms underlying the pathophysiology of T2D and the development of late complications. Mitochondrial dysfunction, endoplasmic reticulum (ER) stress, and inflammation are also closely correlated with insulin resistance and β-cell dysfunction. This review focus on the mechanisms by which oxidative stress, mitochondrial dysfunction, ER stress, and inflammation are involved in the pathophysiology of T2D, highlighting the importance of the antioxidant response and DNA repair mechanisms counteracting the development of the disease. Moreover, we indicate evidence on how nutritional interventions effectively improve diabetes care. Additionally, we address key molecular characteristics and signaling pathways shared between T2D and Alzheimer's disease (AD), which might probably be implicated in the risk of T2D patients to develop AD.

Deciphering regulatory protein activity in human pancreatic islets via reverse engineering of single-cell sequencing data

The loss of functional β cell mass contributes to development and progression of type 2 diabetes (T2D). However, the molecular mechanisms differentiating islet dysfunction in T2D from nondiabetic states remain elusive. In this issue of the JCI, Son et al. applied reverse engineering to obtain the activity of gene expression regulatory proteins from single-cell RNA sequencing data of nondiabetic and T2D human islets. The authors identify unique patterns of regulatory protein activities associated with T2D. Furthermore, BACH2 emerged as a potential transcription factor that drives activation of T2D-associated regulatory proteins in human islets.

A functional genomic approach to identify reference genes for human pancreatic beta cell real-time quantitative RT-PCR analysis

Exposure of human pancreatic beta cells to pro-inflammatory cytokines or metabolic stressors is used to model events related to type 1 and type 2 diabetes, respectively. Quantitative real-time PCR is commonly used to quantify changes in gene expression. The selection of the most adequate reference gene(s) for gene expression normalization is an important pre-requisite to obtain accurate and reliable results. There are no universally applicable reference genes, and the human beta cell expression of commonly used reference genes can be altered by different stressors. Here we aimed to identify the most stably expressed genes in human beta cells to normalize quantitative real-time PCR gene expression.We used comprehensive RNA-sequencing data from the human pancreatic beta cell line EndoC-βH1, human islets exposed to cytokines or the free fatty acid palmitate in order to identify the most stably expressed genes. Genes were filtered based on their level of significance (adjusted P-value >0.05), fold-change (|fold-change| <1.5) and a coefficient of variation <10%. Candidate reference genes were validated by quantitative real-time PCR in independent samples.We identified a total of 264 genes stably expressed in EndoC-βH1 cells and human islets following cytokines - or palmitate-induced stress, displaying a low coefficient of variation. Validation by quantitative real-time PCR of the top five genes ARF1, CWC15, RAB7A, SIAH1 and VAPA corroborated their expression stability under most of the tested conditions. Further validation in independent samples indicated that the geometric mean of ACTB and VAPA expression can be used as a reliable normalizing factor in human beta cells.