Single-cell transcriptomics from human pancreatic islets: sample preparation matters

Single-cell transcriptomic profiling of human pancreatic islets reveals genes responsive to glucose exposure over 24 h

AIMS/HYPOTHESIS

Disruption of pancreatic islet function and glucose homeostasis can lead to the development of sustained hyperglycaemia, beta cell glucotoxicity and subsequently type 2 diabetes. In this study, we explored the effects of in vitro hyperglycaemic conditions on human pancreatic islet gene expression across 24 h in six pancreatic cell types: alpha; beta; gamma; delta; ductal; and acinar. We hypothesised that genes associated with hyperglycaemic conditions may be relevant to the onset and progression of diabetes.

METHODS

We exposed human pancreatic islets from two donors to low (2.8 mmol/l) and high (15.0 mmol/l) glucose concentrations over 24 h in vitro. To assess the transcriptome, we performed single-cell RNA-seq (scRNA-seq) at seven time points. We modelled time as both a discrete and continuous variable to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. Additionally, we integrated genomic features and genetic summary statistics to nominate candidate effector genes. For three of these genes, we functionally characterised the effect on insulin production and secretion using CRISPR interference to knock down gene expression in EndoC-βH1 cells, followed by a glucose-stimulated insulin secretion assay.

RESULTS

In the discrete time models, we identified 1344 genes associated with time and 668 genes associated with glucose exposure across all cell types and time points. In the continuous time models, we identified 1311 genes associated with time, 345 genes associated with glucose exposure and 418 genes associated with interaction effects between time and glucose across all cell types. By integrating these expression profiles with summary statistics from genetic association studies, we identified 2449 candidate effector genes for type 2 diabetes, HbA1c, random blood glucose and fasting blood glucose. Of these candidate effector genes, we showed that three (ERO1B, HNRNPA2B1 and RHOBTB3) exhibited an effect on glucose-stimulated insulin production and secretion in EndoC-βH1 cells.

CONCLUSIONS/INTERPRETATION

The findings of our study provide an in-depth characterisation of the 24 h transcriptomic response of human pancreatic islets to glucose exposure at a single-cell resolution. By integrating differentially expressed genes with genetic signals for type 2 diabetes and glucose-related traits, we provide insights into the molecular mechanisms underlying glucose homeostasis. Finally, we provide functional evidence to support the role of three candidate effector genes in insulin secretion and production.

DATA AVAILABILITY

The scRNA-seq data from the 24 h glucose exposure experiment performed in this study are available in the database of Genotypes and Phenotypes (dbGap; https://www.ncbi.nlm.nih.gov/gap/ ) with accession no. phs001188.v3.p1. Study metadata and summary statistics for the differential expression, gene set enrichment and candidate effector gene prediction analyses are available in the Zenodo data repository ( https://zenodo.org/ ) under accession number 11123248. The code used in this study is publicly available at https://github.com/CollinsLabBioComp/publication-islet_glucose_timecourse .

Ghrelin deletion and conditional ghrelin cell ablation increase pancreatic islet size in mice

Ghrelin exerts key effects on islet hormone secretion to regulate blood glucose levels. Here, we sought to determine whether ghrelin's effects on islets extend to the alteration of islet size and β cell mass. We demonstrate that reducing ghrelin - by ghrelin gene knockout (GKO), conditional ghrelin cell ablation, or high-fat diet (HFD) feeding - was associated with increased mean islet size (up to 62%), percentage of large islets (up to 854%), and β cell cross-sectional area (up to 51%). In GKO mice, these effects were more apparent in 10- to 12-week-old mice than in 4-week-old mice. Higher β cell numbers from decreased β cell apoptosis drove the increase in β cell cross-sectional area. Conditional ghrelin cell ablation in adult mice increased the β cell number per islet by 40% within 4 weeks. A negative correlation between islet size and plasma ghrelin in HFD-fed plus chow-fed WT mice, together with even larger islet sizes in HFD-fed GKO mice than in HFD-fed WT mice, suggests that reduced ghrelin was not solely responsible for diet-induced obesity-associated islet enlargement. Single-cell transcriptomics revealed changes in gene expression in several GKO islet cell types, including upregulation of Manf, Dnajc3, and Gnas expression in β cells, which supports decreased β cell apoptosis and/or increased β cell proliferation. These effects of ghrelin reduction on islet morphology might prove useful when designing new therapies for diabetes.

Functional interrogation of twenty type 2 diabetes-associated genes using isogenic hESC-derived β-like cells

Genetic studies have identified numerous loci associated with type 2 diabetes (T2D), but the functional role of many loci has remained unexplored. In this study, we engineered isogenic knockout human embryonic stem cell (hESC) lines for 20 genes associated with T2D risk. We systematically examined β-cell differentiation, insulin production and secretion, and survival. We performed RNA-seq and ATAC-seq on hESC-β cells from each knockout line. Analyses of T2D GWAS signals overlapping with HNF4A-dependent ATAC peaks identified a specific SNP as a likely causal variant. In addition, we performed integrative association analyses and identified four genes ( CP, RNASE1, PCSK1N and GSTA2 ) associated with insulin production, and two genes ( TAGLN3 and DHRS2 ) associated with sensitivity to lipotoxicity. Finally, we leveraged deep ATAC-seq read coverage to assess allele-specific imbalance at variants heterozygous in the parental hESC line, to identify a single likely functional variant at each of 23 T2D GWAS signals.

Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) provides valuable insights into human islet cell types and their corresponding stable gene expression profiles. However, this approach requires cell dissociation that complicates its utility in vivo. On the other hand, single-nucleus RNA sequencing (snRNA-seq) has compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses, and affords enhanced information from intronic sequences that can be leveraged to identify pre-mRNA transcripts.

METHODS

We obtained nuclear preparations from fresh human islet cells and generated snRNA-seq datasets. We compared these datasets to scRNA-seq output obtained from human islet cells from the same donor. We employed snRNA-seq to obtain the transcriptomic profile of human islets engrafted in immunodeficient mice. In both analyses, we included the intronic reads in the snRNA-seq data with the GRCh38-2020-A library.

RESULTS

First, snRNA-seq analysis shows that the top four differentially and selectively expressed genes in human islet endocrine cells in vitro and in vivo are not the canonical genes but a new set of non-canonical gene markers including ZNF385D, TRPM3, LRFN2, PLUT (β-cells); PTPRT, FAP, PDK4, LOXL4 (α-cells); LRFN5, ADARB2, ERBB4, KCNT2 (δ-cells); and CACNA2D3, THSD7A, CNTNAP5, RBFOX3 (γ-cells). Second, by integrating information from scRNA-seq and snRNA-seq of human islet cells, we distinguish three β-cell sub-clusters: an INS pre-mRNA cluster (β3), an intermediate INS mRNA cluster (β2), and an INS mRNA-rich cluster (β1). These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. Interestingly, the INS mRNA-rich cluster (β1) becomes the predominant sub-cluster in vivo.

CONCLUSIONS

In summary, snRNA-seq and pre-mRNA analysis of human islet cells can accurately identify human islet cell populations, subpopulations, and their dynamic transcriptome profile in vivo.

Overview of Transcriptomic Research on Type 2 Diabetes: Challenges and Perspectives

Type 2 diabetes (T2D) is a common chronic disease whose etiology is known to have a strong genetic component. Standard genetic approaches, although allowing for the detection of a number of gene variants associated with the disease as well as differentially expressed genes, cannot fully explain the hereditary factor in T2D. The explosive growth in the genomic sequencing technologies over the last decades provided an exceptional impetus for transcriptomic studies and new approaches to gene expression measurement, such as RNA-sequencing (RNA-seq) and single-cell technologies. The transcriptomic analysis has the potential to find new biomarkers to identify risk groups for developing T2D and its microvascular and macrovascular complications, which will significantly affect the strategies for early diagnosis, treatment, and preventing the development of complications. In this article, we focused on transcriptomic studies conducted using expression arrays, RNA-seq, and single-cell sequencing to highlight recent findings related to T2D and challenges associated with transcriptome experiments.

Gut mucosa dissociation protocols influence cell type proportions and single-cell gene expression levels

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of the cellular landscape of organs. Most single-cell protocols require fresh material, which limits sample size per experiment, and consequently, introduces batch effects. This is especially true for samples acquired through complex medical procedures, such as intestinal mucosal biopsies. Moreover, the tissue dissociation procedure required for obtaining single cells is a major source of noise; different dissociation procedures applied to different compartments of the tissue induce artificial gene expression differences between cell subsets. To overcome these challenges, we have developed a one-step dissociation protocol and demonstrated its use on cryopreserved gut mucosal biopsies. Using flow cytometry and scRNA-seq analysis, we compared this one-step dissociation protocol with the current gold standard, two-step collagenase digestion, and an adaptation of a recently published alternative, three-step cold-active Bacillus licheniformus protease digestion. Both cell viability and cell type composition were comparable between the one-step and two-step collagenase dissociation, with the former being more time-efficient. The cold protease digestion resulted in equal cell viability, but better preserves the epithelial cell types. Consequently, to analyze the rarer cell types, such as glial cells, larger total biopsy cell numbers are required as input material. The multi-step protocols affected cell types spanning multiple compartments differently. In summary, we show that cryopreserved gut mucosal biopsies can be used to overcome the logistical challenges and batch effects in large scRNA-seq studies. Furthermore, we demonstrate that using cryopreserved biopsies digested using a one-step collagenase protocol enables large-scale scRNA-seq, FACS, organoid generation and intraepithelial lymphocyte expansion.

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Endothelial cells (ECs) are crucial for vascular and whole-body function through their dynamic response to environmental cues. Elucidating the transcriptome and epigenome of ECs is paramount to understanding their roles in development, health, and disease, but is limited in the availability of isolated primary cells. Recent technologies have enabled the high-throughput profiling of EC transcriptome and epigenome, leading to the identification of previously unknown EC cell subpopulations and developmental trajectories. While EC cultures are a useful tool in the exploration of EC function and dysfunction, the culture conditions and multiple passages can introduce external variables that alter the properties of native EC, including morphology, epigenetic state, and gene expression program. To overcome this limitation, the present paper demonstrates a method of isolating human primary ECs from donor mesenteric arteries aiming to capture their native state. ECs in the intimal layer are dissociated mechanically and biochemically with the use of particular enzymes. The resultant cells can be directly used for bulk RNA or single-cell RNA-sequencing or plated for culture. In addition, a workflow is described for the preparation of human arterial tissue for spatial transcriptomics, specifically for a commercially available platform, although this method is also suitable for other spatial transcriptome profiling techniques. This methodology can be applied to different vessels collected from a variety of donors in health or disease states to gain insights into EC transcriptional and epigenetic regulation, a pivotal aspect of endothelial cell biology.

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.

Creation of a Single Cell RNASeq Meta-Atlas to Define Human Liver Immune Homeostasis

The liver is unique in both its ability to maintain immune homeostasis and in its potential for immune tolerance following solid organ transplantation. Single-cell RNA sequencing (scRNA seq) is a powerful approach to generate highly dimensional transcriptome data to understand cellular phenotypes. However, when scRNA data is produced by different groups, with different data models, different standards, and samples processed in different ways, it can be challenging to draw meaningful conclusions from the aggregated data. The goal of this study was to establish a method to combine ‘human liver’ scRNA seq datasets by 1) characterizing the heterogeneity between studies and 2) using the meta-atlas to define the dominant phenotypes across immune cell subpopulations in healthy human liver. Publicly available scRNA seq data generated from liver samples obtained from a combined total of 17 patients and ~32,000 cells were analyzed. Liver-specific immune cells (CD45+) were extracted from each dataset, and immune cell subpopulations (myeloid cells, NK and T cells, plasma cells, and B cells) were examined using dimensionality reduction (UMAP), differential gene expression, and ingenuity pathway analysis. All datasets co-clustered, but cell proportions differed between studies. Gene expression correlation demonstrated similarity across all studies, and canonical pathways that differed between datasets were related to cell stress and oxidative phosphorylation rather than immune-related function. Next, a meta-atlas was generated via data integration and compared against PBMC data to define gene signatures for each hepatic immune subpopulation. This analysis defined key features of hepatic immune homeostasis, with decreased expression across immunologic pathways and enhancement of pathways involved with cell death. This method for meta-analysis of scRNA seq data provides a novel approach to broadly define the features of human liver immune homeostasis. Specific pathways and cellular phenotypes described in this human liver immune meta-atlas provide a critical reference point for further study of immune mediated disease processes within the liver.

Temporal single-cell regeneration studies: the greatest thing since sliced pancreas?

The application of single-cell analytic techniques to the study of stem/progenitor cell niches supports the emerging view that pancreatic cell lineages are in a state of flux between differentiation stages. For all their value, however, such analyses merely offer a snapshot of the cellular palette of the tissue at any given time point. Conclusions about potential developmental/regeneration paths are solely based on bioinformatics inferences. In this context, the advent of new techniques for the long-term culture and lineage tracing of human pancreatic slices offers a virtual window into the native organ and presents the field with a unique opportunity to serially resolve pancreatic regeneration dynamics at the single-cell level.

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.

Human Islets Contain a Beta Cell Type That Expresses Proinsulin But Not the Enzyme That Converts the Precursor to Insulin

In pancreatic beta cells, proinsulin (ProIN) undergoes folding in endoplasmic reticulum/Golgi system and is translocated to secretory vesicles for processing into insulin and C-peptide by the proprotein convertases (PC)1/3 and PC2, and carboxypeptidase E. Human beta cells show significant variation in the level of expression of PC1/3, the critical proconvertase involved in proinsulin processing. To ascertain whether this heterogeneity is correlated with the level of expression of the prohormone and mature hormone, the expression of proinsulin, insulin, and PC1/3 in human beta cells was examined. This analysis identified a human beta cell type that expressed proinsulin but lacked PC1/3 (ProIN⁺PC1/3^-). This beta cell type is absent in rodent islets and is abundant in human islets of adults but scarce in islets from postnatal donors. Human islets also contained a beta cell type that expressed both proinsulin and variable levels of PC1/3 (ProIN⁺PC1/3⁺) and a less abundant cell type that lacked proinsulin but expressed the convertase (ProIN^-PC1/3⁺). These cell phenotypes were altered by type 2 diabetes. These data suggest that these three cell types represent different stages of a dynamic process with proinsulin folding in ProIN⁺PC1/3^- cells, proinsulin conversion into insulin in ProIN⁺PC1/3⁺cells, and replenishment of the proinsulin content in ProIN^-PC1/3⁺ cells.

Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D

Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, key tissues and cell-types required for functional inference are absent from large-scale resources. Here we explore the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using data from 420 donors. We find: (a) 7741 cis-eQTLs in islets with a replication rate across 44 GTEx tissues between 40% and 73%; (b) marked overlap between islet cis-eQTL signals and active regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most strongly implicated in GWAS signal location; (c) enrichment of islet cis-eQTL signals with T2D risk variants identified in genome-wide association studies; and (d) colocalization between 47 islet cis-eQTLs and variants influencing T2D or glycemic traits, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in disease relevant tissues.