Deciphering Pancreatic Islet β Cell and α Cell Maturation Pathways and Characteristic Features at the Single-Cell Level

Discoveries in Pancreatic Physiology and Disease Biology Using Single-Cell RNA Sequencing

Transcriptome analysis is used to study gene expression in human tissues. It can promote the discovery of new therapeutic targets for related diseases by characterizing the endocrine function of pancreatic physiology and pathology, as well as the gene expression of pancreatic tumors. Compared to whole-tissue RNA sequencing, single-cell RNA sequencing (scRNA-seq) can detect transcriptional activity within a single cell. The scRNA-seq had an invaluable contribution to discovering previously unknown cell subtypes in normal and diseased pancreases, studying the functional role of rare islet cells, and studying various types of cells in diabetes as well as cancer. Here, we review the recent in vitro and in vivo advances in understanding the pancreatic physiology and pathology associated with single-cell sequencing technology, which may provide new insights into treatment strategy optimization for diabetes and pancreatic cancer.

Stability of scRNA-Seq Analysis Workflows is Susceptible to Preprocessing and is Mitigated by Regularized or Supervised Approaches

Background:

Statistical methods developed to address various questions in single-cell datasets show increased variability to different parameter regimes. In order to delineate further the robustness of commonly utilized methods for single-cell RNA-Seq, we aimed to comprehensively review scRNA-Seq analysis workflows in the setting of dimension reduction, clustering, and trajectory inference.

Methods:

We utilized datasets with temporal single-cell transcriptomics profiles from public repositories. Combining multiple methods at each level of the workflow, we have performed over 6 k analysis and evaluated the results of clustering and pseudotime estimation using adjusted rand index and rank correlation metrics. We have further integrated neural network methods to assess whether models with increased complexity can show increased bias/variance trade-off.

Results:

Combinatorial workflows showed that utilizing non-linear dimension reduction techniques such as t-SNE and UMAP are sensitive to initial preprocessing steps hence clustering results on dimension reduced space of single-cell datasets should be utilized carefully. Similarly, pseudotime estimation methods that depend on previous non-linear dimension reduction steps can result in highly variable trajectories. In contrast, methods that avoid non-linearity such as WOT can result in repeatable inferences of temporal gene expression dynamics. Furthermore, imputation methods do not improve clustering or trajectory inference results substantially in terms of repeatability. In contrast, the selection of the normalization method shows an increased effect on downstream analysis where ScTransform reduces variability overall.

Enhancing Acsl4 in absence of mTORC2/Rictor drove β-cell dedifferentiation via inhibiting FoxO1 and promoting ROS production

Rapamycin insensitive companion of mechanistic target of Rapamycin (Rictor), the key component of mTOR complex 2 (mTORC2), controls both β-cell proliferation and function. We sought to study whether long chain acyl-CoA synthetase 4 (Acsl4) worked downstream of Rictor/mTORC2 to maintain β-cell functional mass. We found Acsl4 was positively regulated by Rictor at transcriptional and posttranslational levels in mouse β-cell. Infecting adenovirus expressing Acsl4 in β-cell-specific-Rictor-knockout (βRicKO) islets and Min6 cells knocking down Rictor with lentivirus-expressing siRNA-oligos targeting Rictor(siRic), recovered the β-cell dysplasia but not dysfunction. Cell bioenergetic experiment performed with Seahorse XF showed that Acsl4 could not rescue the dampened glucose oxidation in Rictor-lacking β-cell, but further promoted lipid oxidation. Transposase-Accessible Chromatin (ATAC) and H3K27Ac chromatin immunoprecipitation (ChIP) sequencing studies reflected the epigenetic elevated molecular signature for β-cell dedifferentiation and mitigated oxidative defense/response. These results were confirmed by the observations of elevated acetylation and ubiquitination of FoxO1, increased protein levels of Gpx1 and Hif1an, excessive reactive oxygen species (ROS) production and diminished MafA in Acsl4 overexpressed Rictor-lacking β-cells. In these cells, antioxidant treatment significantly recovered MafA level and insulin content. Inducing lipid oxidation alone could not mimic the effect of Acsl4 in Rictor lacking β-cell. Our study suggested that Acsl4 function in β-cell was context dependent and might facilitate β-cell dedifferentiation with attenuated Rictor/mTORC2 activity or insulin signaling via posttranslational inhibiting FoxO1 and epigenetically enhancing ROS induced MafA degradation.

Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments

MOTIVATION

The negative binomial distribution has been shown to be a good model for counts data from both bulk and single-cell RNA-sequencing (RNA-seq). Gaussian process (GP) regression provides a useful non-parametric approach for modelling temporal or spatial changes in gene expression. However, currently available GP regression methods that implement negative binomial likelihood models do not scale to the increasingly large datasets being produced by single-cell and spatial transcriptomics.

RESULTS

The GPcounts package implements GP regression methods for modelling counts data using a negative binomial likelihood function. Computational efficiency is achieved through the use of variational Bayesian inference. The GP function models changes in the mean of the negative binomial likelihood through a logarithmic link function and the dispersion parameter is fitted by maximum likelihood. We validate the method on simulated time course data, showing better performance to identify changes in over-dispersed counts data than methods based on Gaussian or Poisson likelihoods. To demonstrate temporal inference, we apply GPcounts to single-cell RNA-seq datasets after pseudotime and branching inference. To demonstrate spatial inference, we apply GPcounts to data from the mouse olfactory bulb to identify spatially variable genes and compare to two published GP methods. We also provide the option of modelling additional dropout using a zero-inflated negative binomial. Our results show that GPcounts can be used to model temporal and spatial counts data in cases where simpler Gaussian and Poisson likelihoods are unrealistic.

AVAILABILITY AND IMPLEMENTATION

GPcounts is implemented using the GPflow library in Python and is available at https://github.com/ManchesterBioinference/GPcounts along with the data, code and notebooks required to reproduce the results presented here. The version used for this paper is archived at https://doi.org/10.5281/zenodo.5027066.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

SetD7 (Set7/9) is a novel target of PPARγ that promotes the adaptive pancreatic β-cell glycemic response

Loss of functional pancreatic β-cell mass leads to type 2 diabetes (T2D), attributable to modified β-cell-dependent adaptive gene expression patterns. SetD7 is a histone methyltransferase enriched in pancreatic islets that mono- and dimethylates histone-3-lysine-4 (H3K4), promoting euchromatin modifications, and also maintains the regulation of key β-cell function and survival genes. However, the transcriptional regulation of this important epigenetic modifier is unresolved. Here we identified the nuclear hormone receptor peroxisome proliferator-activated receptor-gamma (PPARγ) as a major transcriptional regulator of SetD7 and provide evidence for direct binding and functionality of PPARγ in the SetD7 promoter region. Furthermore, constitutive shRNA-mediated PPARγ knockdown in INS-1 β-cells or pancreas-specific PPARγ deletion in mice led to downregulation of SetD7 expression as well as its nuclear enrichment. The relevance of the SetD7-PPARγ interaction in β-cell adaptation was tested in normoglycemic 60% partial pancreatectomy (Px) and hyperglycemic 90% Px rat models. Whereas a synergistic increase in islet PPARγ and SetD7 expression was observed upon glycemic adaptation post-60% Px, in hyperglycemic 90% Px rats, islet PPARγ, and PPARγ targets SetD7 and Pdx1 were downregulated. PPARγ agonist pioglitazone treatment in 90% Px rats partially restored glucose homeostasis and β-cell mass and enhanced expression of SetD7 and Pdx1. Collectively, these data provide evidence that the SetD7-PPARγ interaction serves as an important element of the adaptive β-cell response.

Cancer Cells Retrace a Stepwise Differentiation Program during Malignant Progression

Pancreatic neuroendocrine tumors (PanNET) comprise two molecular subtypes, relatively benign islet tumors (IT) and invasive, metastasis-like primary (MLP) tumors. Until now, the origin of aggressive MLP tumors has been obscure. Herein, using multi-omics approaches, we revealed that MLP tumors arise from IT via dedifferentiation following a reverse trajectory along the developmental pathway of islet β cells, which results in the acquisition of a progenitor-like molecular phenotype. Functionally, the miR-181cd cluster induces the IT-to-MLP transition by suppressing expression of the Meis2 transcription factor, leading to upregulation of a developmental transcription factor, Hmgb3. Notably, the IT-to-MLP transition constitutes a distinct step of tumorigenesis and is separable from the classic proliferation-associated hallmark, temporally preceding accelerated proliferation of cancer cells. Furthermore, patients with PanNET with elevated HMGB3 expression and an MLP transcriptional signature are associated with higher-grade tumors and worse survival. Overall, our results unveil a new mechanism that modulates cancer cell plasticity to enable malignant progression. SIGNIFICANCE: Dedifferentiation has long been observed as a histopathologic characteristic of many cancers, albeit inseparable from concurrent increases in cell proliferation. Herein, we demonstrate that dedifferentiation is a mechanistically and temporally separable step in the multistage tumorigenesis of pancreatic islet cells, retracing the developmental lineage of islet β cells.This article is highlighted in the In This Issue feature, p. 2355.

Using single-nucleus RNA-sequencing to interrogate transcriptomic profiles of archived human pancreatic islets

Background

Human pancreatic islets are a central focus of research in metabolic studies. Transcriptomics is frequently used to interrogate alterations in cultured human islet cells using single-cell RNA-sequencing (scRNA-seq). We introduce single-nucleus RNA-sequencing (snRNA-seq) as an alternative approach for investigating transplanted human islets.

Methods

The Nuclei EZ protocol was used to obtain nuclear preparations from fresh and frozen human islet cells. Such preparations were first used to generate snRNA-seq datasets and compared to scRNA-seq output obtained from cells from the same donor. Finally, we employed snRNA-seq to obtain the transcriptomic profile of archived human islets engrafted in immunodeficient animals.

Results

We observed virtually complete concordance in identifying cell types and gene proportions as well as a strong association of global and islet cell type gene signatures between scRNA-seq and snRNA-seq applied to fresh and frozen cultured or transplanted human islet samples.

Conclusions

We propose snRNA-seq as a reliable strategy to probe transcriptomic profiles of freshly harvested or frozen sources of transplanted human islet cells especially when scRNA-seq is not ideal.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00941-8.

SARS-CoV-2 infection induces beta cell transdifferentiation

Recent clinical data have suggested a correlation between coronavirus disease 2019 (COVID-19) and diabetes. Here, we describe the detection of SARS-CoV-2 viral antigen in pancreatic beta cells in autopsy samples from individuals with COVID-19. Single-cell RNA sequencing and immunostaining from ex vivo infections confirmed that multiple types of pancreatic islet cells were susceptible to SARS-CoV-2, eliciting a cellular stress response and the induction of chemokines. Upon SARS-CoV-2 infection, beta cells showed a lower expression of insulin and a higher expression of alpha and acinar cell markers, including glucagon and trypsin1, respectively, suggesting cellular transdifferentiation. Trajectory analysis indicated that SARS-CoV-2 induced eIF2-pathway-mediated beta cell transdifferentiation, a phenotype that could be reversed with trans-integrated stress response inhibitor (trans-ISRIB). Altogether, this study demonstrates an example of SARS-CoV-2 infection causing cell fate change, which provides further insight into the pathomechanisms of COVID-19.

Transient antibiotic-induced changes in the neonatal swine intestinal microbiota impact islet expression profiles reducing subsequent function

Neonatal antibiotics administered to human infants initiate gut microbiota dysbiosis that may have long-term effects on body weight and metabolism. We examined antibiotic-induced adaptations in pancreatic islets of the piglet, a well-accepted model of human infant microbiota and pancreas development. Neonatal piglets randomized to amoxicillin [30 mg/kg body wt/day; n = 7, antibiotic (ANTI)] or placebo [vehicle control; n = 7, control (CON)] from postnatal day (PND)0-13 were euthanized at PND7, 14, and 49. The metabolic phenotype along with functional, immunohistological, and transcriptional phenotypes of the pancreatic islets were studied. The gut microbiome was characterized by 16S rRNA gene sequencing, and microbial metabolites and microbiome-sensitive host molecules were measured. Compared with CON, ANTI PND7 piglets had elevated transcripts of genes involved in glucagon-like peptide 1 ((GLP-1) synthesis or signaling in islets (P < 0.05) coinciding with higher plasma GLP-1 (P = 0.11), along with increased tumor necrosis factor α (Tnf) (P < 0.05) and protegrin 1 (Npg1) (P < 0.05). Antibiotic-induced relative increases in Escherichia, Coprococcus, Ruminococcus, Dehalobacterium, and Oscillospira of the ileal microbiome at PND7 normalized after antibiotic withdrawal. In ANTI islets at PND14, the expression of key regulators pancreatic and duodenal homeobox 1 (Pdx1), insulin-like growth factor-2 (Igf2), and transcription factor 7-like 2 (Tcf7l2) was downregulated, preceding a 40% reduction of β-cell area (P < 0.01) and islet insulin content at PND49 (P < 0.05). At PND49, a twofold elevated plasma insulin concentration (P = 0.07) was observed in ANTI compared with CON. We conclude that antibiotic treatment of neonatal piglets elicited gut microbial changes accompanied by phasic alterations in key regulatory genes in pancreatic islets at PND7 and 14. By PND49, reduced β-cell area and islet insulin content were accompanied by elevated nonfasted insulin despite normoglycemia, indicative of islet stress.

NEUROD1 Is Required for the Early α and β Endocrine Differentiation in the Pancreas

Diabetes is a metabolic disease that involves the death or dysfunction of the insulin-secreting β cells in the pancreas. Consequently, most diabetes research is aimed at understanding the molecular and cellular bases of pancreatic development, islet formation, β-cell survival, and insulin secretion. Complex interactions of signaling pathways and transcription factor networks regulate the specification, growth, and differentiation of cell types in the developing pancreas. Many of the same regulators continue to modulate gene expression and cell fate of the adult pancreas. The transcription factor NEUROD1 is essential for the maturation of β cells and the expansion of the pancreatic islet cell mass. Mutations of the Neurod1 gene cause diabetes in humans and mice. However, the different aspects of the requirement of NEUROD1 for pancreas development are not fully understood. In this study, we investigated the role of NEUROD1 during the primary and secondary transitions of mouse pancreas development. We determined that the elimination of Neurod1 impairs the expression of key transcription factors for α- and β-cell differentiation, β-cell proliferation, insulin production, and islets of Langerhans formation. These findings demonstrate that the Neurod1 deletion altered the properties of α and β endocrine cells, resulting in severe neonatal diabetes, and thus, NEUROD1 is required for proper activation of the transcriptional network and differentiation of functional α and β cells.

CBP/p300 HAT maintains the gene network critical for β cell identity and functional maturity

Loss of β cell identity and functional immaturity are thought to be involved in β cell failure in type 2 diabetes. CREB-binding protein (CBP) and its paralogue p300 act as multifunctional transcriptional co-activators and histone acetyltransferases (HAT) with extensive biological functions. However, whether the regulatory role of CBP/p300 in islet β cell function depends on the HAT activity remains uncertain. In this current study, A-485, a selective inhibitor of CBP/p300 HAT activity, greatly impaired glucose-stimulated insulin secretion from rat islets in vitro and in vivo. RNA-sequencing analysis showed a comprehensive downregulation of β cell and α cell identity genes in A-485-treated islets, without upregulation of dedifferentiation markers and derepression of disallowed genes. A-485 treatment decreased the expressions of genes involved in glucose sensing, not in glycolysis, tricarboxylic acid cycle, and oxidative phosphorylation. In the islets of prediabetic db/db mice, CBP/p300 displayed a significant decrease with key genes for β cell function. The deacetylation of histone H3K27 as well as the transcription factors Hnf1α and Foxo1 was involved in CBP/p300 HAT inactivation-repressed expressions of β cell identity and functional genes. These findings highlight the dominant role of CBP/p300 HAT in the maintenance of β cell identity by governing transcription network.

Temporal Transcriptome Analysis Reveals Dynamic Gene Expression Patterns Driving β-Cell Maturation

Newly differentiated pancreatic β cells lack proper insulin secretion profiles of mature functional β cells. The global gene expression differences between paired immature and mature β cells have been studied, but the dynamics of transcriptional events, correlating with temporal development of glucose-stimulated insulin secretion (GSIS), remain to be fully defined. This aspect is important to identify which genes and pathways are necessary for β-cell development or for maturation, as defective insulin secretion is linked with diseases such as diabetes. In this study, we assayed through RNA sequencing the global gene expression across six β-cell developmental stages in mice, spanning from β-cell progenitor to mature β cells. A computational pipeline then selected genes differentially expressed with respect to progenitors and clustered them into groups with distinct temporal patterns associated with biological functions and pathways. These patterns were finally correlated with experimental GSIS, calcium influx, and insulin granule formation data. Gene expression temporal profiling revealed the timing of important biological processes across β-cell maturation, such as the deregulation of β-cell developmental pathways and the activation of molecular machineries for vesicle biosynthesis and transport, signal transduction of transmembrane receptors, and glucose-induced Ca²⁺ influx, which were established over a week before β-cell maturation completes. In particular, β cells developed robust insulin secretion at high glucose several days after birth, coincident with the establishment of glucose-induced calcium influx. Yet the neonatal β cells displayed high basal insulin secretion, which decreased to the low levels found in mature β cells only a week later. Different genes associated with calcium-mediated processes, whose alterations are linked with insulin resistance and deregulation of glucose homeostasis, showed increased expression across β-cell stages, in accordance with the temporal acquisition of proper GSIS. Our temporal gene expression pattern analysis provided a comprehensive database of the underlying molecular components and biological mechanisms driving β-cell maturation at different temporal stages, which are fundamental for better control of the in vitro production of functional β cells from human embryonic stem/induced pluripotent cell for transplantation-based type 1 diabetes therapy.

PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

To investigate molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along the pseudotime inferred from single-cell RNA-sequencing data. However, existing methods do not account for pseudotime inference uncertainty, and they have either ill-posed p-values or restrictive models. Here we propose PseudotimeDE, a DE gene identification method that adapts to various pseudotime inference methods, accounts for pseudotime inference uncertainty, and outputs well-calibrated p-values. Comprehensive simulations and real-data applications verify that PseudotimeDE outperforms existing methods in false discovery rate control and power.

Endocrine Pancreas Development and Dysfunction Through the Lens of Single-Cell RNA-Sequencing

A chronic inability to maintain blood glucose homeostasis leads to diabetes, which can damage multiple organs. The pancreatic islets regulate blood glucose levels through the coordinated action of islet cell-secreted hormones, with the insulin released by β-cells playing a crucial role in this process. Diabetes is caused by insufficient insulin secretion due to β-cell loss, or a pancreatic dysfunction. The restoration of a functional β-cell mass might, therefore, offer a cure. To this end, major efforts are underway to generate human β-cells de novo, in vitro, or in vivo. The efficient generation of functional β-cells requires a comprehensive knowledge of pancreas development, including the mechanisms driving cell fate decisions or endocrine cell maturation. Rapid progress in single-cell RNA sequencing (scRNA-Seq) technologies has brought a new dimension to pancreas development research. These methods can capture the transcriptomes of thousands of individual cells, including rare cell types, subtypes, and transient states. With such massive datasets, it is possible to infer the developmental trajectories of cell transitions and gene regulatory pathways. Here, we summarize recent advances in our understanding of endocrine pancreas development and function from scRNA-Seq studies on developing and adult pancreas and human endocrine differentiation models. We also discuss recent scRNA-Seq findings for the pathological pancreas in diabetes, and their implications for better treatment.

Autocrine IGF2 programmes β-cell plasticity under conditions of increased metabolic demand

When exposed to nutrient excess and insulin resistance, pancreatic β-cells undergo adaptive changes in order to maintain glucose homeostasis. The role that growth control genes, highly expressed in early pancreas development, might exert in programming β-cell plasticity in later life is a poorly studied area. The imprinted Igf2 (insulin-like growth factor 2) gene is highly transcribed during early life and has been identified in recent genome-wide association studies as a type 2 diabetes susceptibility gene in humans. Hence, here we investigate the long-term phenotypic metabolic consequences of conditional Igf2 deletion in pancreatic β-cells (Igf2βKO) in mice. We show that autocrine actions of IGF2 are not critical for β-cell development, or for the early post-natal wave of β-cell remodelling. Additionally, adult Igf2βKO mice maintain glucose homeostasis when fed a chow diet. However, pregnant Igf2βKO females become hyperglycemic and hyperinsulinemic, and their conceptuses exhibit hyperinsulinemia and placentomegalia. Insulin resistance induced by congenital leptin deficiency also renders Igf2βKO females more hyperglycaemic compared to leptin-deficient controls. Upon high-fat diet feeding, Igf2βKO females are less susceptible to develop insulin resistance. Based on these findings, we conclude that in female mice, autocrine actions of β-cell IGF2 during early development determine their adaptive capacity in adult life.

Pancreatic β-cell heterogeneity in health and diabetes: classes, sources, and subtypes

Pancreatic β-cells perform glucose-stimulated insulin secretion, a process at the center of type 2 diabetes etiology. Efforts to understand how β-cells behave in healthy and stressful conditions have revealed a wide degree of morphological, functional, and transcriptional heterogeneity. Sources of heterogeneity include β-cell topography, developmental origin, maturation state, and stress response. Advances in sequencing and imaging technologies have led to the identification of β-cell subtypes, which play distinct roles in the islet niche. This review examines β-cell heterogeneity from morphological, functional, and transcriptional perspectives, and considers the relevance of topography, maturation, development, and stress response. It also discusses how these factors have been used to identify β-cell subtypes, and how heterogeneity is impacted by diabetes. We examine open questions in the field and discuss recent technological innovations that could advance understanding of β-cell heterogeneity in health and disease.

Insights from single cell studies of human pancreatic islets and stem cell-derived islet cells to guide functional beta cell maturation in vitro

There is now a sizeable number of single cell transcriptomics studies performed on human and rodent pancreatic islets that have shed light on the unique gene signatures and level of heterogeneity within each individual islet cell type. Following closely from these studies, there is also rapidly-growing activity on characterizing islet-like cells derived from in vitro differentiation of human pluripotent stem cells (hPSCs) at the single cell level. The overall consensus across the studies so far suggests that the first few stages of differentiation are largely uniform, whereas during pancreatic endocrine commitment, cell trajectories start to diverge, resulting in multiple end-stage pancreatic cells that include progenitor-like, endocrine and non-endocrine cells. Comprehensive transcriptional profiling is important for understanding how and why islet cells, especially the insulin-secreting beta cells, exist in subpopulations that differ in maturity, proliferation rate, sensitivity to stress, and insulin secretion function. For hPSC-derived beta cells to be used confidently for cell therapy, optimal differentiation and thorough characterization is required. The key questions to address are-What is the trajectory of differentiation? Is heterogeneity a natural occurrence or is it a consequence of imperfect differentiation protocols? Can lessons be drawn from the extensive single cell transcriptomic data to help guide maturation of beta cells in vitro? This book chapter seeks to address some of these questions, and facilitate ongoing efforts in improving the beta cell differentiation pipeline or enriching for desired beta cell populations following differentiation, to make way for better mechanistic studies and future clinical translation.

Modeling different types of diabetes using human pluripotent stem cells

Diabetes mellitus (DM) is a metabolic disease characterized by chronic hyperglycemia as a result of progressive loss of pancreatic β cells, which could lead to several debilitating complications. Different paths, triggered by several genetic and environmental factors, lead to the loss of pancreatic β cells and/or function. Understanding these many paths to β cell damage or dysfunction could help in identifying therapeutic approaches specific for each path. Most of our knowledge about diabetes pathophysiology has been obtained from studies on animal models, which do not fully recapitulate human diabetes phenotypes. Currently, human pluripotent stem cell (hPSC) technology is a powerful tool for generating in vitro human models, which could provide key information about the disease pathogenesis and provide cells for personalized therapies. The recent progress in generating functional hPSC-derived β cells in combination with the rapid development in genomic and genome-editing technologies offer multiple options to understand the cellular and molecular mechanisms underlying the development of different types of diabetes. Recently, several in vitro hPSC-based strategies have been used for studying monogenic and polygenic forms of diabetes. This review summarizes the current knowledge about different hPSC-based diabetes models and how these models improved our current understanding of the pathophysiology of distinct forms of diabetes. Also, it highlights the progress in generating functional β cells in vitro, and discusses the current challenges and future perspectives related to the use of the in vitro hPSC-based strategies.

Human Pluripotent Stem Cells to Model Islet Defects in Diabetes

Diabetes mellitus is characterized by elevated levels of blood glucose and is ultimately caused by insufficient insulin production from pancreatic beta cells. Different research models have been utilized to unravel the molecular mechanisms leading to the onset of diabetes. The generation of pancreatic endocrine cells from human pluripotent stem cells constitutes an approach to study genetic defects leading to impaired beta cell development and function. Here, we review the recent progress in generating and characterizing functional stem cell-derived beta cells. We summarize the diabetes disease modeling possibilities that stem cells offer and the challenges that lie ahead to further improve these models.

Single-cell RNA sequencing for engineering and studying human islets

The islets of Langerhans are complex tissues composed of several cell types that secrete hormones. Loss or dysfunction of the insulin-producing β cells leads to dysregulation of blood glucose levels, resulting in diabetes. A major goal in cellular engineering has been to generate β cells from stem cells for use in cell-based therapies. However, the presence of other cell types within these islets can mask important details about β cells when using population-level assays. Single-cell RNA sequencing have enabled transcriptional assessment of individual cells within mixed populations. These technologies allow for accurate assessment of specific cell types and subtypes of β cells. Studies investigating different stages of β cell maturity have led to several insights into understanding islet development and diabetes pathology. Here, we highlight the key findings from the use of single-cell RNA sequencing on stem cell-derived and primary human islet cells found in different maturation and diabetic states.

Transcriptional dynamics of induced pluripotent stem cell differentiation into β cells reveals full endodermal commitment and homology with human islets

BACKGROUND AIMS

Induced pluripotent stem cells (iPSCs) have the capacity to generate β cells in vitro, but the differentiation is incomplete and generates a variable percentage of off-target cells. Single-cell RNA sequencing offers the possibility of characterizing the transcriptional dynamics throughout differentiation and determining the identity of the final differentiation product.

METHODS

Single-cell transcriptomics data were obtained from four stages across differentiation of iPSCs into β cells and from human donor islets.

RESULTS

Clustering analysis revealed that iPSCs undertake a full endoderm commitment, and the obtained endocrine pancreatic cells have high homology with mature islets. The iPSC-derived β cells were devoid of pluripotent residual cells, and the differentiation was pancreas-specific, as it did not generate ectodermal or mesodermal cells. Pseudotime trajectory identified a dichotomic endocrine/non-endocrine cell fate and distinct subgroups in the endocrine branch.

CONCLUSIONS

Future efforts to produce β cells from iPSCs must aim not only to improve the resulting endocrine cell but also to avoid differentiation into non-pancreatic endoderm cells.

Single-cell analyses identify distinct and intermediate states of zebrafish pancreatic islet development

Pancreatic endocrine islets are vital for glucose homeostasis. However, the islet developmental trajectory and its regulatory network are not well understood. To define the features of these specification and differentiation processes, we isolated individual islet cells from TgBAC(neurod1:EGFP) transgenic zebrafish and analyzed islet developmental dynamics across four different embryonic stages using a single-cell RNA-seq strategy. We identified proliferative endocrine progenitors, which could be further categorized by different cell cycle phases with the G1/S subpopulation displaying a distinct differentiation potential. We identified endocrine precursors, a heterogeneous intermediate-state population consisting of lineage-primed alpha, beta and delta cells that were characterized by the expression of lineage-specific transcription factors and relatively low expression of terminally differentiation markers. The terminally differentiated alpha, beta, and delta cells displayed stage-dependent differentiation states, which were related to their functional maturation. Our data unveiled distinct states, events and molecular features during the islet developmental transition, and provided resources to comprehensively understand the lineage hierarchy of islet development at the single-cell level.

Characterizing pancreatic β-cell heterogeneity in the streptozotocin model by single-cell transcriptomic analysis

OBJECTIVES

The streptozotocin (STZ) model is widely used in diabetes research. However, the cellular and molecular states of pancreatic endocrine cells in this model remain unclear. This study explored the molecular characteristics of islet cells treated with STZ and re-evaluated β-cell dysfunction and regeneration in the STZ model.

METHODS

We performed single-cell RNA sequencing of pancreatic endocrine cells from STZ-treated mice. High-quality sequencing data from 2,999 cells were used to identify clusters via Louvain clustering analysis. Principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), force-directed layout (FDL), and differential expression analysis were used to define the heterogeneity and transcriptomic changes in islet cells. In addition, qPCR and immunofluorescence staining were used to confirm findings from the sequencing data.

RESULTS

Untreated β-cells were divided into two populations at the transcriptomic level, a large high-Glut2 expression (Glut2high) population and a small low-Glut2 expression (Glut2low) population. At the transcriptomic level, Glut2low β-cells in adult mice did not represent a developmentally immature state, although a fraction of genes associated with β-cell maturation and function were downregulated in Glut2low cells. After a single high-dose STZ treatment, most Glut2high cells were killed, but Glut2low cells survived and over time changed to a distinct cell state. We did not observe conversion of Glut2low to Glut2high β-cells up to 9 months after STZ treatment. In addition, we did not detect transcriptomic changes in the non-β endocrine cells or a direct trans-differentiation pathway from the α-cell lineage to the β-cell lineage in the STZ model.

CONCLUSIONS

We identified the heterogeneity of β-cells in both physiological and pathological conditions. However, we did not observe conversion of Glut2low to Glut2high β-cells, transcriptomic changes in the non-β endocrine cells, or direct trans-differentiation from the α-cell lineage to the β-cell lineage in the STZ model. Our results clearly define the states of islet cells treated with STZ and allow us to re-evaluate the STZ model widely used in diabetes studies.

Vitamin-D-Binding Protein Contributes to the Maintenance of α Cell Function and Glucagon Secretion

Vitamin-D-binding protein (DBP) or group-specific component of serum (GC-globulin) carries vitamin D metabolites from the circulation to target tissues. DBP is highly localized to the liver and pancreatic α cells. Although DBP serum levels, gene polymorphisms, and autoantigens have all been associated with diabetes risk, the underlying mechanisms remain unknown. Here, we show that DBP regulates α cell morphology, α cell function, and glucagon secretion. Deletion of DBP leads to smaller and hyperplastic α cells, altered Na+ channel conductance, impaired α cell activation by low glucose, and reduced rates of glucagon secretion both in vivo and in vitro. Mechanistically, this involves reversible changes in islet microfilament abundance and density, as well as changes in glucagon granule distribution. Defects are also seen in β cell and δ cell function. Immunostaining of human pancreata reveals generalized loss of DBP expression as a feature of late-onset and long-standing, but not early-onset, type 1 diabetes. Thus, DBP regulates α cell phenotype, with implications for diabetes pathogenesis.

Single-cell RNA-seq with spike-in cells enables accurate quantification of cell-specific drug effects in pancreatic islets

BACKGROUND

Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool to dissect cell-specific effects of drug treatment in complex tissues. This application requires high levels of precision, robustness, and quantitative accuracy-beyond those achievable with existing methods for mainly qualitative single-cell analysis. Here, we establish the use of standardized reference cells as spike-in controls for accurate and robust dissection of single-cell drug responses.

RESULTS

We find that contamination by cell-free RNA can constitute up to 20% of reads in human primary tissue samples, and we show that the ensuing biases can be removed effectively using a novel bioinformatics algorithm. Applying our method to both human and mouse pancreatic islets treated ex vivo, we obtain an accurate and quantitative assessment of cell-specific drug effects on the transcriptome. We observe that FOXO inhibition induces dedifferentiation of both alpha and beta cells, while artemether treatment upregulates insulin and other beta cell marker genes in a subset of alpha cells. In beta cells, dedifferentiation and insulin repression upon artemether treatment occurs predominantly in mouse but not in human samples.

CONCLUSIONS

This new method for quantitative, error-correcting, scRNA-seq data normalization using spike-in reference cells helps clarify complex cell-specific effects of pharmacological perturbations with single-cell resolution and high quantitative accuracy.

Single-cell transcriptional diversity is a hallmark of developmental potential

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation is challenging. Here, we demonstrate a simple, yet robust, determinant of developmental potential-the number of expressed genes per cell-and leverage this measure of transcriptional diversity to develop a computational framework (CytoTRACE) for predicting differentiation states from scRNA-seq data. When applied to diverse tissue types and organisms, CytoTRACE outperformed previous methods and nearly 19,000 annotated gene sets for resolving 52 experimentally determined developmental trajectories. Additionally, it facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis. This study thus establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.

Understanding generation and regeneration of pancreatic β cells from a single-cell perspective

Understanding the mechanisms that underlie the generation and regeneration of β cells is crucial for developing treatments for diabetes. However, traditional research methods, which are based on populations of cells, have limitations for defining the precise processes of β-cell differentiation and trans-differentiation, and the associated regulatory mechanisms. The recent development of single-cell technologies has enabled re-examination of these processes at a single-cell resolution to uncover intermediate cell states, cellular heterogeneity and molecular trajectories of cell fate specification. Here, we review recent advances in understanding β-cell generation and regeneration, in vivo and in vitro, from single-cell technologies, which could provide insights for optimization of diabetes therapy strategies.

Molecular and genetic regulation of pig pancreatic islet cell development

Reliance on rodents for understanding pancreatic genetics, development and islet function could limit progress in developing interventions for human diseases such as diabetes mellitus. Similarities of pancreas morphology and function suggest that porcine and human pancreas developmental biology may have useful homologies. However, little is known about pig pancreas development. To fill this knowledge gap, we investigated fetal and neonatal pig pancreas at multiple, crucial developmental stages using modern experimental approaches. Purification of islet β-, α- and δ-cells followed by transcriptome analysis (RNA-seq) and immunohistology identified cell- and stage-specific regulation, and revealed that pig and human islet cells share characteristic features that are not observed in mice. Morphometric analysis also revealed endocrine cell allocation and architectural similarities between pig and human islets. Our analysis unveiled scores of signaling pathways linked to native islet β-cell functional maturation, including evidence of fetal α-cell GLP-1 production and signaling to β-cells. Thus, the findings and resources detailed here show how pig pancreatic islet studies complement other systems for understanding the developmental programs that generate functional islet cells, and that are relevant to human pancreatic diseases.

Transient FOXO1 inhibition in pancreatic endoderm promotes the generation of NGN3+ endocrine precursors from human iPSCs

In the multi-step differentiation protocol used to generate pancreatic endocrine cells from human pluripotent stem cells, the induction of NGN3+ endocrine precursors from the PDX1+/NKX6.1+ pancreatic endoderm is crucial for efficient endocrine cell production. Here, we demonstrate that transient, not prolonged FOXO1 inhibition results in enhanced NGN3+ endocrine precursors and hormone-producing cell production. FOXO1 inhibition does not directly induce NGN3 expression but stimulates PDX1+/NKX6.1+ cell proliferation. NOTCH activity, whose suppression is important for Ngn3 expression, is not suppressed but Wnt signaling is stimulated by FOXO1 inhibition. Reversely, Wnt inhibition suppresses the effects of FOXO1 inhibitor. These findings indicate that FOXO1 and Wnt are involved in regulating the proliferation of PDX1+/NKX6.1+ pancreatic endoderm that gives rise to NGN3+ endocrine precursors.

Identification of a LIF-Responsive, Replication-Competent Subpopulation of Human β Cells

The beta (β)-cell mass formed during embryogenesis is amplified by cell replication during fetal and early postnatal development. Thereafter, β cells become functionally mature, and their mass is maintained by a low rate of replication. For those few β cells that replicate in adult life, it is not known how replication is initiated nor whether this occurs in a specialized subset of β cells. We capitalized on a YAP overexpression system to induce replication of stem-cell-derived β cells and, by single-cell RNA sequencing, identified an upregulation of the leukemia inhibitory factor (LIF) pathway. Activation of the LIF pathway induces replication of human β cells in vitro and in vivo. The expression of the LIF receptor is restricted to a subset of transcriptionally distinct human β cells with increased proliferative capacity. This study delineates novel genetic networks that control the replication of LIF-responsive, replication-competent human β cells.

Targeted pharmacological therapy restores β-cell function for diabetes remission

Dedifferentiation of insulin-secreting β cells in the islets of Langerhans has been proposed to be a major mechanism of β-cell dysfunction. Whether dedifferentiated β cells can be targeted by pharmacological intervention for diabetes remission, and ways in which this could be accomplished, are unknown as yet. Here we report the use of streptozotocin-induced diabetes to study β-cell dedifferentiation in mice. Single-cell RNA sequencing (scRNA-seq) of islets identified markers and pathways associated with β-cell dedifferentiation and dysfunction. Single and combinatorial pharmacology further show that insulin treatment triggers insulin receptor pathway activation in β cells and restores maturation and function for diabetes remission. Additional β-cell selective delivery of oestrogen by Glucagon-like peptide-1 (GLP-1-oestrogen conjugate) decreases daily insulin requirements by 60%, triggers oestrogen-specific activation of the endoplasmic-reticulum-associated protein degradation system, and further increases β-cell survival and regeneration. GLP-1-oestrogen also protects human β cells against cytokine-induced dysfunction. This study not only describes mechanisms of β-cell dedifferentiation and regeneration, but also reveals pharmacological entry points to target dedifferentiated β cells for diabetes remission.

Adaptive Landscape Shaped by Core Endogenous Network Coordinates Complex Early Progenitor Fate Commitments in Embryonic Pancreas

The classical development hierarchy of pancreatic cell fate commitments describes that multipotent progenitors (MPs) first bifurcate into tip cells and trunk cells, and then these cells give rise to acinar cells and endocrine/ductal cells separately. However, lineage tracings reveal that pancreatic progenitors are highly heterogeneous in tip and trunk domains in embryonic pancreas. The progenitor fate commitments from multipotency to unipotency during early pancreas development is insufficiently characterized. In pursuing a mechanistic understanding of the complexity in progenitor fate commitments, we construct a core endogenous network for pancreatic lineage decisions based on genetic regulations and quantified its intrinsic dynamic properties using dynamic modeling. The dynamics reveal a developmental landscape with high complexity that has not been clarified. Not only well-characterized pancreatic cells are reproduced, but also previously unrecognized progenitors-tip progenitor (TiP), trunk progenitor (TrP), later endocrine progenitor (LEP), and acinar progenitors (AciP/AciP2) are predicted. Further analyses show that TrP and LEP mediate endocrine lineage maturation, while TiP, AciP, AciP2 and TrP mediate acinar and ductal lineage maturation. The predicted cell fate commitments are validated by analyzing single-cell RNA sequencing (scRNA-seq) data. Significantly, this is the first time that a redefined hierarchy with detailed early pancreatic progenitor fate commitment is obtained.

Dual Effect of Raptor on Neonatal β-Cell Proliferation and Identity Maintenance

Immature pancreatic β-cells are highly proliferative, and the expansion of β-cells during the early neonatal period largely determines functional β-cell mass; however, the mechanisms are poorly characterized. We generated Ngn3RapKO mice (ablation of Raptor, an essential component of mechanistic target of rapamycin [mTORC1] in Ngn3⁺ endocrine progenitor cells) and found that mTORC1 was dispensable for endocrine cell lineage formation but specifically regulated both proliferation and identity maintenance of neonatal β-cells. Ablation of Raptor in neonatal β-cells led to autonomous loss of cell identity, decelerated cell cycle progression, compromised proliferation, and caused neonatal diabetes as a result of inadequate establishment of functional β-cell mass at postnatal day 14. Completely different from mature β-cells, Raptor regulated G1/S and G2/M phase cell cycle transition, thus permitting a high proliferation rate in neonatal β-cells. Moreover, Ezh2 was identified as a critical downstream target of mTORC1 in neonatal β-cells, which was responsible for G2/M phase transition and proliferation. Our discovery of the dual effect of mTORC1 in immature β-cells has revealed a potential target for replenishing functional β-cell pools by promoting both expansion and functional maturation of newly formed immature β-cells.

Single-Cell Transcriptome Profiling of Mouse and hESC-Derived Pancreatic Progenitors

Human embryonic stem cells (hESCs) are a potential unlimited source of insulin-producing β cells for diabetes treatment. A greater understanding of how β cells form during embryonic development will improve current hESC differentiation protocols. All pancreatic endocrine cells, including β cells, are derived from Neurog3-expressing endocrine progenitors. This study characterizes the single-cell transcriptomes of 6,905 mouse embryonic day (E) 15.5 and 6,626 E18.5 pancreatic cells isolated from Neurog3-Cre; Rosa26^mT/mG embryos, allowing for enrichment of endocrine progenitors (yellow; tdTomato + EGFP) and endocrine cells (green; EGFP). Using a NEUROG3-2A-eGFP CyT49 hESC reporter line (N5-5), 4,462 hESC-derived GFP+ cells were sequenced. Differential expression analysis revealed enrichment of markers that are consistent with progenitor, endocrine, or previously undescribed cell-state populations. This study characterizes the single-cell transcriptomes of mouse and hESC-derived endocrine progenitors and serves as a resource (https://lynnlab.shinyapps.io/embryonic_pancreas) for improving the formation of functional β-like cells from hESCs.

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Single-cell transcriptome of embryonic mouse pancreas and hESC-derived cells

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Identification of novel cell types during mouse pancreas development

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Pseudotime analysis reveals developmental trajectories of endocrine cell lineage

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hESC-derived endocrine cells resemble immature β cells

scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data

Technical variation in feature measurements, such as gene expression and locus accessibility, is a key challenge of large-scale single-cell genomic datasets. We show that this technical variation in both scRNA-seq and scATAC-seq datasets can be mitigated by analyzing feature detection patterns alone and ignoring feature quantification measurements. This result holds when datasets have low detection noise relative to quantification noise. We demonstrate state-of-the-art performance of detection pattern models using our new framework, scBFA, for both cell type identification and trajectory inference. Performance gains can also be realized in one line of R code in existing pipelines.

Alterations in Beta Cell Identity in Type 1 and Type 2 Diabetes

PURPOSE OF REVIEW

To discuss the current understanding of "β cell identity" and factors underlying altered identity of pancreatic β cells in diabetes, especially in humans.

RECENT FINDINGS

Altered identity of β cells due to dedifferentiation and/or transdifferentiation has been proposed as a mechanism of loss of β cells in diabetes. In dedifferentiation, β cells do not undergo apoptosis; rather, they lose their identity and function. Dedifferentiation is well characterized by the decrease in expression of key β cell markers such as genes encoding major transcription factors, e.g., MafA, NeuroD1, Nkx6.1, and Foxo1, and an increase in atypical or "disallowed" genes for β cells such as lactate dehydrogenase, monocarboxylate transporter MCT1, or progenitor cell genes (Neurog3, Pax4, or Sox9). Moreover, altered identity of mature β cells in diabetes also involves transdifferentiation of β cells into other islet hormone producing cells. For example, overexpression of α cell specific transcription factor Arx or ablation of Pdx1 resulted in an increase of α cell numbers and a decrease in β cell numbers in rodents. The frequency of α-β double-positive cells was also prominent in human subjects with T2D. These altered identities of β cells likely serve as a compensatory response to enhance function/expand cell numbers and may also camouflage/protect cells from ongoing stress. However, it is equally likely that this may be a reflection of new cell formation as a frank regenerative response to ongoing tissue injury. Physiologically, all these responses are complementary. In diabetes, (1) endocrine identity recapitulates the less mature/less-differentiated fetal/neonatal cell type, possibly representing an adaptive mechanism; (2) residual β cells may be altered in their subtype proportions or other molecular features; (3) in humans, "altered identity" is a preferable term to dedifferentiation as their cellular fate (differentiated cells losing identity or progenitors becoming more differentiated) is unclear as yet.

Navigating the Depths and Avoiding the Shallows of Pancreatic Islet Cell Transcriptomes

Islet gene expression has been widely studied to better understand the transcriptional features that define a healthy β-cell. Transcriptomes of FACS-purified α-, β-, and δ-cells using bulk RNA-sequencing have facilitated our understanding of the complex network of cross talk between islet cells and its effects on β-cell function. However, these approaches were by design not intended to resolve heterogeneity between individual cells. Several recent studies used single-cell RNA sequencing (scRNA-Seq) to report considerable heterogeneity within mouse and human β-cells. In this Perspective, we assess how this newfound ability to assess gene expression at single-cell resolution has enhanced our understanding of β-cell heterogeneity. We conduct a comprehensive assessment of several single human β-cell transcriptome data sets and ask if the heterogeneity reported by these studies showed overlap and concurred with previously known examples of β-cell heterogeneity. We also illustrate the impact of the inevitable limitations of working at or below the limit of detection of gene expression at single cell resolution and their consequences for the quality of single-islet cell transcriptome data. Finally, we offer some guidance on when to opt for scRNA-Seq and when bulk sequencing approaches may be better suited.

Concepts and limitations for learning developmental trajectories from single cell genomics

Single cell genomics has become a popular approach to uncover the cellular heterogeneity of progenitor and terminally differentiated cell types with great precision. This approach can also delineate lineage hierarchies and identify molecular programmes of cell-fate acquisition and segregation. Nowadays, tens of thousands of cells are routinely sequenced in single cell-based methods and even more are expected to be analysed in the future. However, interpretation of the resulting data is challenging and requires computational models at multiple levels of abstraction. In contrast to other applications of single cell sequencing, where clustering approaches dominate, developmental systems are generally modelled using continuous structures, trajectories and trees. These trajectory models carry the promise of elucidating mechanisms of development, disease and stimulation response at very high molecular resolution. However, their reliable analysis and biological interpretation requires an understanding of their underlying assumptions and limitations. Here, we review the basic concepts of such computational approaches and discuss the characteristics of developmental processes that can be learnt from trajectory models.

Heterogeneity of the Human Pancreatic Islet

Pancreatic β-cells play a pivotal role in maintaining normoglycemia. Recent studies have revealed that the β-cell is not a homogeneous cell population but, rather, is heterogeneous in a number of properties such as electrical activity, gene expression, and cell surface markers. Identification of specific β-cell subpopulations altered in diabetic conditions would open a new avenue to develop targeted therapeutic interventions. As intense studies of β-cell heterogeneity are anticipated in the next decade, it is important that heterogeneity of the islet be recognized. Many studies in the past were undertaken with a small sample of islets, which might overlook important individual variance. In this study, by systematic analyses of the human islet in two and three dimensions, we demonstrate islet heterogeneity in size, number, architecture, cellular composition, and capillary density. There is no stereotypic human islet, and thus, a sufficient number of islets should be examined to ensure study reproducibility.

Defining multistep cell fate decision pathways during pancreatic development at single-cell resolution

The generation of terminally differentiated cell lineages during organogenesis requires multiple, coordinated cell fate choice steps. However, this process has not been clearly delineated, especially in complex solid organs such as the pancreas. Here, we performed single-cell RNA-sequencing in pancreatic cells sorted from multiple genetically modified reporter mouse strains at embryonic stages E9.5-E17.5. We deciphered the developmental trajectories and regulatory strategies of the exocrine and endocrine pancreatic lineages as well as intermediate progenitor populations along the developmental pathways. Notably, we discovered previously undefined programs representing the earliest events in islet α- and β-cell lineage allocation as well as the developmental pathway of the "first wave" of α-cell generation. Furthermore, we demonstrated that repressing ERK pathway activity is essential for inducing both α- and β-lineage differentiation. This study provides key insights into the regulatory mechanisms underlying cell fate choice and stepwise cell fate commitment and can be used as a resource to guide the induction of functional islet lineage cells from stem cells in vitro.

Inferring population dynamics from single-cell RNA-sequencing time series data

Recent single-cell RNA-sequencing studies have suggested that cells follow continuous transcriptomic trajectories in an asynchronous fashion during development. However, observations of cell flux along trajectories are confounded with population size effects in snapshot experiments and are therefore hard to interpret. In particular, changes in proliferation and death rates can be mistaken for cell flux. Here we present pseudodynamics, a mathematical framework that reconciles population dynamics with the concepts underlying developmental trajectories inferred from time-series single-cell data. Pseudodynamics models population distribution shifts across trajectories to quantify selection pressure, population expansion, and developmental potentials. Applying this model to time-resolved single-cell RNA-sequencing of T-cell and pancreatic beta cell maturation, we characterize proliferation and apoptosis rates and identify key developmental checkpoints, data inaccessible to existing approaches.

Modelling the endocrine pancreas in health and disease

Diabetes mellitus is a multifactorial disease affecting increasing numbers of patients worldwide. Progression to insulin-dependent diabetes mellitus is characterized by the loss or dysfunction of pancreatic β-cells, but the pathomechanisms underlying β-cell failure in type 1 diabetes mellitus and type 2 diabetes mellitus are still poorly defined. Regeneration of β-cell mass from residual islet cells or replacement by β-like cells derived from stem cells holds great promise to stop or reverse disease progression. However, the development of new treatment options is hampered by our limited understanding of human pancreas organogenesis due to the restricted access to primary tissues. Therefore, the challenge is to translate results obtained from preclinical model systems to humans, which requires comparative modelling of β-cell biology in health and disease. Here, we discuss diverse modelling systems across different species that provide spatial and temporal resolution of cellular and molecular mechanisms to understand the evolutionary conserved genotype-phenotype relationship and translate them to humans. In addition, we summarize the latest knowledge on organoids, stem cell differentiation platforms, primary micro-islets and pseudo-islets, bioengineering and microfluidic systems for studying human pancreas development and homeostasis ex vivo. These new modelling systems and platforms have opened novel avenues for exploring the developmental trajectory, physiology, biology and pathology of the human pancreas.

Recapitulating endocrine cell clustering in culture promotes maturation of human stem-cell-derived β cells

Despite advances in the differentiation of insulin-producing cells from human embryonic stem cells, the generation of mature functional β cells in vitro has remained elusive. To accomplish this goal, we have developed cell culture conditions to closely mimic events occurring during pancreatic islet organogenesis and β cell maturation. In particular, we have focused on recapitulating endocrine cell clustering by isolating and reaggregating immature β-like cells to form islet-sized enriched β-clusters (eBCs). eBCs display physiological properties analogous to primary human β cells, including robust dynamic insulin secretion, increased calcium signalling in response to secretagogues, and improved mitochondrial energization. Notably, endocrine cell clustering induces metabolic maturation by driving mitochondrial oxidative respiration, a process central to stimulus-secretion coupling in mature β cells. eBCs display glucose-stimulated insulin secretion as early as three days after transplantation in mice. In summary, replicating aspects of endocrine cell clustering permits the generation of stem-cell-derived β cells that resemble their endogenous counterparts.

A transcriptomic roadmap to alpha- and beta cell differentiation in the embryonic pancreas

During pancreatic development, endocrine cells appear from the pancreatic epithelium when Neurog3 positive cells delaminate and differentiate into alpha, beta, gamma and delta cells. The mechanisms involved in this process are still incompletely understood. We characterized the temporal, lineage-specific developmental programs during pancreatic development by sequencing the transcriptome of thousands of individual pancreatic cells from embryonic day E12.5 to E18.5 in mice, and identified all known cell types that are present in the embryonic pancreas, but focused specifically on alpha and beta cell differentiation by enrichment of a MIP-GFP reporter. We characterized transcriptomic heterogeneity in the tip domain based on proliferation, and characterized two endocrine precursor clusters marked by expression of Neurog3 and Fev. Pseudotime analysis revealed specific branches for developing alpha- and beta cells, which allowed identification of specific gene regulation patterns. These include some known and many previously unreported genes that appear to define pancreatic cell fate transitions. This resource allows dynamic profiling of embryonic pancreas development at single cell resolution and reveals novel gene signatures during pancreatic differentiation into alpha and beta cells.

The Effect of Wnt Pathway Modulators on Human iPSC-Derived Pancreatic Beta Cell Maturation

Current published protocols for targeted differentiation of human stem cells toward pancreatic β-cells fail to deliver sufficiently mature cells with functional properties comparable to human islet β-cells. We aimed to assess whether Wnt-modulation could promote the final protocol stages of β-cell maturation, building our hypothesis on our previous findings of Wnt activation in immature hiPSC-derived stage 7 (S7) cells compared to adult human islets and with recent data reporting a link between Wnt/PCP and in vitro β-cell maturation. In this study, we stimulated canonical and non-canonical Wnt signaling in hiPSC-derived S7 cells using syntetic proteins including WNT3A, WNT4, WNT5A and WNT5B, and we inhibited endogenous Wnt signaling with the Tankyrase inhibitor G007-LK (TKi). Whereas neither canonical nor non-canonical Wnt stimulation alone was able to mature hiPSC-derived S7 cells, WNT-inhibition with TKi increased the fraction of monohormonal cells and global proteomics of TKi-treated S7 cells showed a proteomic signature more similar to adult human islets, suggesting that inhibition of endogenous Wnt contributes toward final β-cell maturation.

A complex auxiliary: IL-17/Th17 signaling during type 1 diabetes progression

Type 1 diabetes (T1D) is an autoimmune disease centered around the loss of the beta cells of the islets of Langerhans, and consequent inability of the islets to produce the insulin necessary to maintain glycemic control. While most therapeutic approaches have been centered on insulin replacement, newer approaches to target the underlying immune response have become an area of focus. However, the immune landscape in T1D is extremely complex, and the roles played by individual cytokines during disease progression are incompletely understood, making the development of immunotherapies very difficult. In this review, we discuss the complex auxiliary role played by IL-17, both around the islet and in peripheral tissues such as the gut and kidney, which might influence T1D progression. Through our re-analysis of the key factors involved IL-17 signaling in recently published single-cell sequencing and sorted-cell bulk sequencing datasets, we find supporting evidence for the general existence of the signaling apparatus in islet endocrine cells. We also explore the emerging evidence of IL-17 serving as an influential factor in diabetic complications that affect distal tissues. While anti-IL-17 therapies are emerging as an option for psoriasis and other autoimmune disorders, we highlight here a number of questions that would need to be addressed before their potential applicability to treating T1D can be fully evaluated.

Single-cell Transcriptomic Analyses of Mouse Pancreatic Endocrine Cells

Pancreatic endocrine cells, which are clustered in islets, regulate blood glucose stability and energy metabolism. The distinct cell types in islets, including insulin-secreting β cells, are differentiated from common endocrine progenitors during the embryonic stage. Immature endocrine cells expand via cell proliferation and mature during a long postnatal developmental period. However, the mechanisms underlying these processes are not clearly defined. Single-cell RNA-sequencing is a promising approach for the characterization of distinct cell populations and tracing cell lineage differentiation pathways. Here, we describe a method for the single-cell RNA-sequencing of isolated pancreatic β cells from embryonic, neonatal and postnatal pancreases.

Lineage dynamics of murine pancreatic development at single-cell resolution

Organogenesis requires the complex interactions of multiple cell lineages that coordinate their expansion, differentiation, and maturation over time. Here, we profile the cell types within the epithelial and mesenchymal compartments of the murine pancreas across developmental time using a combination of single-cell RNA sequencing, immunofluorescence, in situ hybridization, and genetic lineage tracing. We identify previously underappreciated cellular heterogeneity of the developing mesenchyme and reconstruct potential lineage relationships among the pancreatic mesothelium and mesenchymal cell types. Within the epithelium, we find a previously undescribed endocrine progenitor population, as well as an analogous population in both human fetal tissue and human embryonic stem cells differentiating toward a pancreatic beta cell fate. Further, we identify candidate transcriptional regulators along the differentiation trajectory of this population toward the alpha or beta cell lineages. This work establishes a roadmap of pancreatic development and demonstrates the broad utility of this approach for understanding lineage dynamics in developing organs.

Coordinated proliferation and differentiation of diverse cell populations drive pancreatic epithelial and mesenchymal development. Here, the authors profile cell type dynamics in the developing mouse pancreas using single-cell RNA sequencing, identifying mesenchymal subtypes and undescribed endocrine progenitors.

Epigenetic Control of Pancreatic Regeneration in Diabetes

Both type 1 and type 2 diabetes are conditions that are associated with the loss of insulin-producing β-cells within the pancreas. An active research therefore aims at regenerating these β-cells with the hope that they could restore euglycemia. The approaches classically used consist in mimicking embryonic development, making use of diverse cell sources or converting pre-existing pancreatic cells. Despite impressive progresses and promising successes, it appears that we still need to gain further insight into the molecular mechanisms underlying β-cell development. This becomes even more obvious with the emergence of a relatively new field of research, epigenetics. The current review therefore focuses on the latest advances in this field in the context of β-cell (neo-)genesis research.