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

A systematic review of single-cell RNA sequencing applications and innovations

Bulk RNA sequencing is one type of RNA sequencing technique, as well as targeted RNA sequencing and whole transcriptome sequencing. It provides valuable insights into gene expression in specific cell populations or regions. However, these methods often miss the diversity of cells within complex tissues. This restriction is overcome by single-cell RNA sequencing, which records gene expression at the single-cell level. It offers a detailed picture of the diversity of cells. It is essential to study glucose homeostasis. It offers thorough explanations of cellular variation. Networks and Governance Dynamics The use of scRNA-seq in islet cells is reviewed in this study, along with sample preparation, sequencing, and computational analysis. It highlights advances in understanding cell types. Gene activity and cell interactions. Along with the challenges and limitations of scRNA-seq, this review highlights the importance of scRNA-seq in understanding complex biological processes and diseases. It is an essential resource for future research and method development in this field, which will help to build personalized treatment.

Alleviating batch effects in cell type deconvolution with SCCAF-D

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.

Single-Cell Meta-Analysis Uncovers the Pancreatic Endothelial Cell Transcriptomic Signature and Reveals a Key Role for NKX2-3 in PLVAP Expression

BACKGROUND

The pancreatic vasculature displays tissue-specific physiological and functional adaptations that support rapid insulin response by β-cells. However, the digestive enzymes have made it difficult to characterize pancreatic endothelial cells (ECs), resulting in the poor understanding of pancreatic EC specialization.

METHODS

Available single-nuclei/single-cell RNA-sequencing data sets were mined to identify pancreatic EC-enriched signature genes and to develop an integrated atlas of human pancreatic ECs. We validated the findings using independent single-nuclei/single-cell RNA-sequencing data, bulk RNA-sequencing data of isolated ECs, spatial transcriptomics data, immunofluorescence, and RNAScope of selected markers. The NK2 homeobox 3 (NKX2-3) TF (transcription factor) was expressed in HUVECs via gene transfection, and the expression of pancreatic EC-enriched signature genes was assessed via RT-qPCR.

RESULTS

We defined a pancreatic EC-enriched gene signature conserved across species and developmental stages that included genes involved in ECM (extracellular matrix) composition (COL15A1 and COL4A1), permeability and barrier function (PLVAP, EHD4, CAVIN3, HSPG2, ROBO4, HEG1, and CLEC14A), and key signaling pathways (S1P [sphingosine-1-phosphate], TGF-β [transforming growth factor-β], RHO/RAC GTPase [guanosine triphosphatase], PI3K/AKT [phosphoinositide 3-kinase/protein kinase B], and PDGF [platelet-derived growth factor]). The integrated atlas revealed the vascular hierarchy within the pancreas. We identified and validated a specialized islet capillary subpopulation characterized by genes involved in permeability (PLVAP and EHD4), immune-modulation (FABP5, HLA-C, and B2M), ECM composition (SPARC and SPARCL1), IGF (insulin-like growth factor) signaling (IGFBP7), and membrane transport (SLCO2A1, SLC2A3, and CD320). Importantly, we identified NKX2-3 as a key TF enriched in pancreatic ECs. DNA-binding motif analysis found NKX2-3 motifs in ≈40% of the signature genes. Induction of NKX2-3 in HUVECs promoted the expression of the islet capillary EC-enriched genes PLVAP and SPARCL1.

CONCLUSIONS

We defined a validated transcriptomic signature of pancreatic ECs and uncovered their intratissue transcriptomic heterogeneity. We showed that NKX2-3 acts upstream of PLVAP and provided a single-cell online resource that can be further explored by the community: https://vasconcelos.shinyapps.io/pancreatic_endothelial/.

Exploring single-cell RNA sequencing as a decision-making tool in the clinical management of Fuchs' endothelial corneal dystrophy

Single-cell RNA sequencing (scRNA-seq) has enabled the identification of novel gene signatures and cell heterogeneity in numerous tissues and diseases. Here we review the use of this technology for Fuchs' Endothelial Corneal Dystrophy (FECD). FECD is the most common indication for corneal endothelial transplantation worldwide. FECD is challenging to manage because it is genetically heterogenous, can be autosomal dominant or sporadic, and progress at different rates. Single-cell RNA sequencing has enabled the discovery of several FECD subtypes, each with associated gene signatures, and cell heterogeneity. Current FECD treatments are mainly surgical, with various Rho kinase (ROCK) inhibitors used to promote endothelial cell metabolism and proliferation following surgery. A range of emerging therapies for FECD including cell therapies, gene therapies, tissue engineered scaffolds, and pharmaceuticals are in preclinical and clinical trials. Unlike conventional disease management methods based on clinical presentations and family history, targeting FECD using scRNA-seq based precision-medicine has the potential to pinpoint the disease subtypes, mechanisms, stages, severities, and help clinicians in making the best decision for surgeries and the applications of therapeutics. In this review, we first discuss the feasibility and potential of using scRNA-seq in clinical diagnostics for FECD, highlight advances from the latest clinical treatments and emerging therapies for FECD, integrate scRNA-seq results and clinical notes from our FECD patients and discuss the potential of applying alternative therapies to manage these cases clinically.

Transcriptome data are insufficient to control false discoveries in regulatory network inference

Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring "causal sufficiency," meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information.

scCDC: a computational method for gene-specific contamination detection and correction in single-cell and single-nucleus RNA-seq data

In droplet-based single-cell and single-nucleus RNA-seq assays, systematic contamination of ambient RNA molecules biases the quantification of gene expression levels. Existing methods correct the contamination for all genes globally. However, there lacks specific evaluation of correction efficacy for varying contamination levels. Here, we show that DecontX and CellBender under-correct highly contaminating genes, while SoupX and scAR over-correct lowly/non-contaminating genes. Here, we develop scCDC as the first method to detect the contamination-causing genes and only correct expression levels of these genes, some of which are cell-type markers. Compared with existing decontamination methods, scCDC excels in decontaminating highly contaminating genes while avoiding over-correction of other genes.

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

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

Follicle-stimulating hormone orchestrates glucose-stimulated insulin secretion of pancreatic islets

Follicle-stimulating hormone (FSH) is involved in mammalian reproduction via binding to FSH receptor (FSHR). However, several studies have found that FSH and FSHR play important roles in extragonadal tissue. Here, we identified the expression of FSHR in human and mouse pancreatic islet β-cells. Blocking FSH signaling by Fshr knock-out led to impaired glucose tolerance owing to decreased insulin secretion, while high FSH levels caused insufficient insulin secretion as well. In vitro, we found that FSH orchestrated glucose-stimulated insulin secretion (GSIS) in a bell curve manner. Mechanistically, FSH primarily activates Gαs via FSHR, promoting the cAMP/protein kinase A (PKA) and calcium pathways to stimulate GSIS, whereas high FSH levels could activate Gαi to inhibit the cAMP/PKA pathway and the amplified effect on GSIS. Our results reveal the role of FSH in regulating pancreatic islet insulin secretion and provide avenues for future clinical investigation and therapeutic strategies for postmenopausal diabetes.

Transcriptional signature of islet neogenesis-associated protein peptide-treated rat pancreatic islets reveals induction of novel long non-coding RNAs

Background

Diabetes mellitus is characterized by chronic hyperglycemia with loss of β-cell function and mass. An attractive therapeutic approach to treat patients with diabetes in a non-invasive way is to harness the innate regenerative potential of the pancreas. The Islet Neogenesis-Associated Protein pentadecapeptide (INGAP-PP) has been shown to induce β-cell regeneration and improve their function in rodents. To investigate its possible mechanism of action, we report here the global transcriptional effects induced by the short-term INGAP-PP in vitro treatment of adult rat pancreatic islets.

Methods and findings

Rat pancreatic islets were cultured in vitro in the presence of INGAP-PP for 4 days, and RNA-seq was generated from triplicate treated and control islet samples. We performed a de novo rat gene annotation based on the alignment of RNA-seq reads. The list of INGAP-PP-regulated genes was integrated with epigenomic data. Using the new gene annotation generated in this work, we quantified RNA-seq data profiled in INS-1 cells treated with IL1β, IL1β+Calcipotriol (a vitamin D agonist) or vehicle, and single-cell RNA-seq data profiled in rat pancreatic islets. We found 1,669 differentially expressed genes by INGAP-PP treatment, including dozens of previously unannotated rat transcripts. Genes differentially expressed by the INGAP-PP treatment included a subset of upregulated transcripts that are associated with vitamin D receptor activation. Supported by epigenomic and single-cell RNA-seq data, we identified 9 previously unannotated long noncoding RNAs (lncRNAs) upregulated by INGAP-PP, some of which are also differentially regulated by IL1β and vitamin D in β-cells. These include Ri-lnc1, which is enriched in mature β-cells.

Conclusions

Our results reveal the transcriptional program that could explain the enhancement of INGAP-PP-mediated physiological effects on β-cell mass and function. We identified novel lncRNAs that are induced by INGAP-PP in rat islets, some of which are selectively expressed in pancreatic β-cells and downregulated by IL1β treatment of INS-1 cells. Our results suggest a relevant function for Ri-lnc1 in β-cells. These findings are expected to provide the basis for a deeper understanding of islet translational results from rodents to humans, with the ultimate goal of designing new therapies for people with diabetes.

Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas

Although multiple pancreatic islet single-cell RNA-sequencing (scRNA-seq) datasets have been generated, a consensus on pancreatic cell states in development, homeostasis and diabetes as well as the value of preclinical animal models is missing. Here, we present an scRNA-seq cross-condition mouse islet atlas (MIA), a curated resource for interactive exploration and computational querying. We integrate over 300,000 cells from nine scRNA-seq datasets consisting of 56 samples, varying in age, sex and diabetes models, including an autoimmune type 1 diabetes model (NOD), a glucotoxicity/lipotoxicity type 2 diabetes model (db/db) and a chemical streptozotocin β-cell ablation model. The β-cell landscape of MIA reveals new cell states during disease progression and cross-publication differences between previously suggested marker genes. We show that β-cells in the streptozotocin model transcriptionally correlate with those in human type 2 diabetes and mouse db/db models, but are less similar to human type 1 diabetes and mouse NOD β-cells. We also report pathways that are shared between β-cells in immature, aged and diabetes models. MIA enables a comprehensive analysis of β-cell responses to different stressors, providing a roadmap for the understanding of β-cell plasticity, compensation and demise.

The controversy about the effects of artemisinins on pancreatic α cell reprogramming and diabetes

It has been reported that artemisinin treatment induces β cell regeneration and alleviates hyperglycemia, although the therapeutic potential and mechanism have been questioned by various groups. We discuss the existing evidence and future plans for studies on artemisinins in the context of diabetes research.

Biologically informed deep learning to query gene programs in single-cell atlases

The increasing availability of large-scale single-cell atlases has enabled the detailed description of cell states. In parallel, advances in deep learning allow rapid analysis of newly generated query datasets by mapping them into reference atlases. However, existing data transformations learned to map query data are not easily explainable using biologically known concepts such as genes or pathways. Here we propose expiMap, a biologically informed deep-learning architecture that enables single-cell reference mapping. ExpiMap learns to map cells into biologically understandable components representing known 'gene programs'. The activity of each cell for a gene program is learned while simultaneously refining them and learning de novo programs. We show that expiMap compares favourably to existing methods while bringing an additional layer of interpretability to integrative single-cell analysis. Furthermore, we demonstrate its applicability to analyse single-cell perturbation responses in different tissues and species and resolve responses of patients who have coronavirus disease 2019 to different treatments across cell types.

Pathway trajectory analysis with tensor imputation reveals drug-induced single-cell transcriptomic landscape

Genome-wide identification of single-cell transcriptomic responses of drugs in various human cells is a challenging issue in medical and pharmaceutical research. Here we present a computational method, tensor-based imputation of gene-expression data at the single-cell level (TIGERS), which reveals the drug-induced single-cell transcriptomic landscape. With this algorithm, we predict missing drug-induced single-cell gene-expression data with tensor imputation, and identify trajectories of regulated pathways considering intercellular heterogeneity. Tensor imputation outperformed existing imputation methods for data completion, and provided cell-type-specific transcriptomic responses for unobserved drugs. For example, TIGERS correctly predicted the cell-type-specific expression of maker genes for pancreatic islets. Pathway trajectory analysis of the imputed gene-expression profiles of all combinations of drugs and human cells identified single-cell-specific drug activities and pathway trajectories that reflect drug-induced changes in pathway regulation. The proposed method is expected to expand our understanding of the single-cell mechanisms of drugs at the pathway level.

Library adaptors with integrated reference controls improve the accuracy and reliability of nanopore sequencing

Library adaptors are short oligonucleotides that are attached to RNA and DNA samples in preparation for next-generation sequencing (NGS). Adaptors can also include additional functional elements, such as sample indexes and unique molecular identifiers, to improve library analysis. Here, we describe Control Library Adaptors, termed CAPTORs, that measure the accuracy and reliability of NGS. CAPTORs can be integrated within the library preparation of RNA and DNA samples, and their encoded information is retrieved during sequencing. We show how CAPTORs can measure the accuracy of nanopore sequencing, evaluate the quantitative performance of metagenomic and RNA sequencing, and improve normalisation between samples. CAPTORs can also be customised for clinical diagnoses, correcting systematic sequencing errors and improving the diagnosis of pathogenic BRCA1/2 variants in breast cancer. CAPTORs are a simple and effective method to increase the accuracy and reliability of NGS, enabling comparisons between samples, reagents and laboratories, and supporting the use of nanopore sequencing for clinical diagnosis.

Single-cell transcriptomics to understand the cellular heterogeneity in toxicology

Background

Identification of molecular signatures from omics studies is widely applied in toxicological studies, and the evaluation of potential toxic effects provides novel insights into molecular resolution.

Objective

The prediction of toxic effects and drug tolerance provides important clues regarding the mode of action of target compounds. However, heterogeneity within samples makes toxicology studies challenging because the purity of the target cell in the samples remains unknown until their actual utilization.

Result

Single-cell resolution studies have been suggested in toxicogenomics, and several studies have explained toxic effects and drug tolerance using heterogeneous cells in both in vivo and in vitro conditions. In this review, we presented an understanding of single-cell transcriptomes and their applications in toxicogenomics.

Conclusion

The most toxicological mechanism in organisms occurs through intramolecular combinations, and heterogeneity issues have reached a surmountable level. We hope this review provides insights to successfully conduct future studies on toxicology.

Purpose of the review

Toxicogenomics is an interdisciplinary field between toxicology and genomics that was successfully applied to construct molecular profiles in a broad spectrum of toxicology. However, heterogeneity within samples makes toxicology studies challenging because the purity of target cell in the samples remains unknown until their actual utilisation. In this review, we presented an understanding of single-cell transcriptomes and their applications in toxicogenomics.

Recent findings

A high-throughput techniques have been used to understand cellular heterogeneity and molecular mechanisms at toxicogenomics. Single-cell resolution analysis is required to identify biomarkers of explain toxic effect and in order to understand drug tolerance.

Challenges in translational machine learning

Machine learning (ML) algorithms are increasingly being used to help implement clinical decision support systems. In this new field, we define as "translational machine learning", joint efforts and strong communication between data scientists and clinicians help to span the gap between ML and its adoption in the clinic. These collaborations also improve interpretability and trust in translational ML methods and ultimately aim to result in generalizable and reproducible models. To help clinicians and bioinformaticians refine their translational ML pipelines, we review the steps from model building to the use of ML in the clinic. We discuss experimental setup, computational analysis, interpretability and reproducibility, and emphasize the challenges involved. We highly advise collaboration and data sharing between consortia and institutes to build multi-centric cohorts that facilitate ML methodologies that generalize across centers. In the end, we hope that this review provides a way to streamline translational ML and helps to tackle the challenges that come with it.

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.

From single-omics to interactomics: How can ligand-induced perturbations modulate single-cell phenotypes?

Cells suffer from perturbations by different stimuli, which, consequently, rise to individual alterations in their profile and function that may end up affecting the tissue as a whole. This is no different if we consider the effect of a therapeutic agent on a biological system. As cells are exposed to external ligands their profile can change at different single-omics levels. Detecting how these changes take place through different sequencing technologies is key to a better understanding of the effects of therapeutic agents. Single-cell RNA-sequencing stands out as one of the most common approaches for cell profiling and perturbation analysis. As a result, single-cell transcriptomics data can be integrated with other omics data sources, such as proteomics and epigenomics data, to clarify the perturbation effects and mechanism at the cell level. Appropriate computational tools are key to process and integrate the available information. This chapter focuses on the recent advances on ligand-induced perturbation and single-cell omics computational tools and algorithms, their current limitations, and how the deluge of data can be used to improve the current process of drug research and development.

Multi-omics integration in the age of million single-cell data

An explosion in single-cell technologies has revealed a previously underappreciated heterogeneity of cell types and novel cell-state associations with sex, disease, development and other processes. Starting with transcriptome analyses, single-cell techniques have extended to multi-omics approaches and now enable the simultaneous measurement of data modalities and spatial cellular context. Data are now available for millions of cells, for whole-genome measurements and for multiple modalities. Although analyses of such multimodal datasets have the potential to provide new insights into biological processes that cannot be inferred with a single mode of assay, the integration of very large, complex, multimodal data into biological models and mechanisms represents a considerable challenge. An understanding of the principles of data integration and visualization methods is required to determine what methods are best applied to a particular single-cell dataset. Each class of method has advantages and pitfalls in terms of its ability to achieve various biological goals, including cell-type classification, regulatory network modelling and biological process inference. In choosing a data integration strategy, consideration must be given to whether the multi-omics data are matched (that is, measured on the same cell) or unmatched (that is, measured on different cells) and, more importantly, the overall modelling and visualization goals of the integrated analysis.

Application of single-cell transcriptomics to kinetoplastid research

Kinetoplastid parasites are responsible for both human and animal diseases across the globe where they have a great impact on health and economic well-being. Many species and life cycle stages are difficult to study due to limitations in isolation and culture, as well as to their existence as heterogeneous populations in hosts and vectors. Single-cell transcriptomics (scRNA-seq) has the capacity to overcome many of these difficulties, and can be leveraged to disentangle heterogeneous populations, highlight genes crucial for propagation through the life cycle, and enable detailed analysis of host–parasite interactions. Here, we provide a review of studies that have applied scRNA-seq to protozoan parasites so far. In addition, we provide an overview of sample preparation and technology choice considerations when planning scRNA-seq experiments, as well as challenges faced when analysing the large amounts of data generated. Finally, we highlight areas of kinetoplastid research that could benefit from scRNA-seq technologies.

An inhibitor-mediated beta-cell dedifferentiation model reveals distinct roles for FoxO1 in glucagon repression and insulin maturation

OBJECTIVE

The loss of forkhead box protein O1 (FoxO1) signaling in response to metabolic stress contributes to the etiology of type II diabetes, causing the dedifferentiation of pancreatic beta cells to a cell type reminiscent of endocrine progenitors. Lack of methods to easily model this process in vitro, however, have hindered progress into the identification of key downstream targets and potential inhibitors. We therefore aimed to establish such an in vitro cellular dedifferentiation model and apply it to identify novel agents involved in the maintenance of beta-cell identity.

METHODS

The murine beta-cell line, Min6, was used for primary experiments and high-content screening. Screens encompassed a library of small-molecule drugs representing the chemical and target space of all FDA-approved small molecules with an automated immunofluorescence readout. Validation experiments were performed in a murine alpha-cell line as well as in primary murine and human diabetic islets. Developmental effects were studied in zebrafish and C. elegans models, while diabetic db/db mouse models were used to elucidate global glucose metabolism outcomes.

RESULTS

We show that short-term pharmacological FoxO1 inhibition can model beta-cell dedifferentiation by downregulating beta-cell-specific transcription factors, resulting in the aberrant expression of progenitor genes and the alpha-cell marker glucagon. From a high-content screen, we identified loperamide as a small molecule that can prevent FoxO inhibitor-induced glucagon expression and further stimulate insulin protein processing and secretion by altering calcium levels, intracellular pH, and FoxO1 localization.

CONCLUSIONS

Our study provides novel models, molecular targets, and drug candidates for studying and preventing beta-cell dedifferentiation.

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.

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.

Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq

Single-cell measurements of cellular characteristics have been instrumental in understanding the heterogeneous pathways that drive differentiation, cellular responses to signals, and human disease. Recent advances have allowed paired capture of protein abundance and transcriptomic state, but a lack of epigenetic information in these assays has left a missing link to gene regulation. Using the heterogeneous mixture of cells in human peripheral blood as a test case, we developed a novel scATAC-seq workflow that increases signal-to-noise and allows paired measurement of cell surface markers and chromatin accessibility: integrated cellular indexing of chromatin landscape and epitopes, called ICICLE-seq. We extended this approach using a droplet-based multiomics platform to develop a trimodal assay that simultaneously measures transcriptomics (scRNA-seq), epitopes, and chromatin accessibility (scATAC-seq) from thousands of single cells, which we term TEA-seq. Together, these multimodal single-cell assays provide a novel toolkit to identify type-specific gene regulation and expression grounded in phenotypically defined cell types.

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.

The Potential Roles of Artemisinin and Its Derivatives in the Treatment of Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is a chronic disease that has become a global public health problem. Studies on T2DM prevention and treatment mostly focus on discovering therapeutic drugs. Artemisinin and its derivatives were originally used as antimalarial treatments. In recent years, the roles of artemisinins in T2DM have attracted much attention. Artemisinin treatments not only attenuate insulin resistance and restore islet ß-cell function in T2DM but also have potential therapeutic effects on diabetic complications, including diabetic kidney disease, cognitive impairment, diabetic retinopathy, and diabetic cardiovascular disease. Many in vitro and in vivo experiments have confirmed the therapeutic utility of artemisinin and its derivatives on T2DM, but no article has systematically demonstrated the specific role artemisinin plays in the treatment of T2DM. This review summarizes the potential therapeutic effects and mechanism of artemisinin and its derivatives in T2DM and associated complications, providing a reference for subsequent related research.

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.