- Open Access
Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles
Genome Medicine volume 15, Article number: 30 (2023)
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.
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.
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.
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.
Diabetes results from deficiency of functional pancreatic β-cells [1, 2]. Detailed characterization of the transcriptional programs in islet cells in health and disease will help to identify therapeutic targets to treat diabetes . The recent, and now widely used, application of single-cell RNA sequencing (scRNA-seq) on human islets from healthy donors and patients with diabetes is providing a wealth of data regarding islet cell populations and their established transcriptome profile [4,5,6,7,8,9,10]. scRNA-seq of dispersed cells from human islets or pancreas tissue represents an obvious advance over bulk RNA sequencing of whole islets or tissue, and a clear improvement over bulk transcriptomic analysis of sorted islet cell subtypes, both of which require mechanical and enzymatic cell disruption and inevitable cellular stress. scRNA-seq also requires mechanical and enzymatic cell dissociation with unavoidable adverse biological consequences on islet cell subtypes. Further, scRNA-seq typically focusses on exon reads, which provides limited or no information on the pre-mRNA status of the corresponding genes, since most of the mRNA analyzed is mature, stored mRNA .scRNA-seq on human islet cells has identified genes associated with type 1 (T1D) and type 2 (T2D) diabetes [5,6,7], genes important for islet cell development and maturation [8, 9], for islet dysfunction and dedifferentiation [10, 11], for aging , and genes involved in the transdifferentiation among islet cells [13, 14]. These studies typically use “canonical” gene sets to annotate different islet cell populations, and new gene sets are continuously identified that more precisely define islet cell subtypes as they passage from development, stem cell differentiation through maturation . scRNA-seq has also confirmed the presence of heterogeneity among human β-cells, defining several β-cell subtypes with different gene profiles [16, 17]. Although these are remarkable advances, it remains true that these scRNA-seq studies have been mostly performed using isolated human islets cultured in vitro, which may not reflect actual in vivo biology. Further, scRNA-seq analysis of human cadaveric pancreas biopsies or human islet grafts in mice requires mechanical and enzymatic cell dissociation, which results in low cellular yields, causes non-physiologic cellular stress, eliminates specific cell subpopulations sensitive to these stresses, and thus represents small subpopulations of islet cells that survived the vicissitudes of organ donation, islet isolation, and islet cell dispersion. Thus, scRNA-seq datasets may not reflect the complete universe of the normal islet cell population [18,19,20,21,22].
In contrast, single-nucleus RNA sequencing (snRNA-seq) uses isolated nuclei, has substantial advantages including reduced dissociation bias, compatibility with frozen samples, and elimination of dissociation-induced transcriptional stress responses, and uncovers mostly unspliced pre-mRNAs containing introns, as well as exons. Combining exonic and intronic sequences reveals important information on the pre-mRNA transcriptome status of a cell at a given moment in time [23, 24]. This approach should also be adaptable to analysis of pre-mRNA transcriptome profiles of human islet cells in vivo in previously fixed or frozen samples, as well as in human islet grafts from mice. Pioneer work by Basile et al. recently reported a side-by-side comparison of snRNA-seq and scRNA-seq of human islet cells to validate the robustness of the former as an alternative sequencing strategy especially when scRNA-seq is not ideal . They observed virtually complete concordance in identifying human islet 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. However, these studies did not take advantage of the direct comparison of exonic mature mRNA reads (scRNA-seq) vs. exonic plus intronic reads (pre-mRNA, snRNA-seq) to infer the real-time global pre-mRNA transcriptome status of the cell. This is in part due to the absence of human islet cell annotation references that include intronic data, and the absence of adequate gene sets to identify human islet cells using snRNA-seq analysis. In addition, these studies did not identify different β-cell subpopulations using snRNA-seq datasets.
In this study, we addressed these latter issues by directly comparing scRNA-seq and snRNA-seq analysis of human islet cells to determine whether (1) they provide similar results on human islet cell populations; (2) intron plus exon reads in the snRNA-seq analysis provide additional information that further defines islet cell populations; (3) new gene sets can be defined in the snRNA-seq analysis that more accurately portray islet cell populations; (4) β-cell heterogeneity can be defined using snRNA-seq analysis; and (5) the snRNA-seq approach can enrich analysis of transcriptome profiles in human islets in vivo. Reassuringly, the results indicate that scRNA-seq and snRNA-seq using exon or combined intron plus exon reads, respectively, are interchangeable to identify human islet cell populations both in vitro and in vivo. However, the results also make the important point that at the pre-mRNA level, canonical endocrine cell genes are not the top highly and selectively expressed genes in these islet endocrine cells. Conversely, these studies reveal new, non-canonical gene sets that accurately identify endocrine cell types in the snRNA-seq analysis of human islets that can be used in in vitro as well as in vivo human islet studies. Finally, integrating datasets from both scRNA-seq and snRNA-seq analyses, we distinguish three different β-cell subpopulations reflecting differences in their INS mRNA transcriptional stage. These differences imply distinct biological functions according to gene set enrichment analysis (GSEA). Overall, this study supports the use of snRNA-seq technology and pre-mRNA analysis as a tool for deciphering human islet cell populations and subpopulations and their distinct biological functions in health and disease.
Human islet samples
Adult human pancreatic islets from seven brain-dead donors were generated by Prodo Laboratories (Aliso Viejo, CA) according to the standard procedures . Islets were harvested from pancreata from deceased organ donors without any identifying information and with informed consent properly and legally secured, and Western Institutional Review Board (WIRB) approval. The average donor age was 38 ± 5 and 71% of them were male donors. Complete donor demographic information is provided in Additional file 1: Table S1. Human islets were procured in serum-free medium, centrifuged, washed, and then cultured in complete RPMI medium (5 mM glucose) and non-adhesive culture plates (3000–5000 human islets equivalents [IEQs] 1 IEQ = 150 μm diameter islet/plate) in 5% CO2 at 37 °C overnight before initiation of the studies. Human pancreatic islets from three donors (N = 3) were used for the in vitro studies (detailed below) and human islets from four donors were used in human islet transplant experiments in immunosuppressed mice (detailed below).
Human islet cells and nuclei processing
Human islets from three different donors (3000 IEQs/donor) (Additional file 1: Table S1) were collected, washed twice with PBS (Ca2 + /Mg2 + free), and then centrifuged at 300 rpm for 3 min. After removing the PBS, 200 µl pre-warmed Accutase (cat# 25–058-CL, Corning) were added and the islets were incubated at 37 °C for 10 min. Then, complete RPMI medium was added to the tubes, the samples were centrifuged at 1000 rpm for 3 min, and the pellet washed with PBS (Ca2 + /Mg2 + free). Half of the cells were resuspended in binding buffer (cat# 130–090-101, Miltenyi Biotec) with dead cell removal beads, incubated for 15 min at room temperature and applied onto the dead cell removal column (cat # 130–042-401, Miltenyi Biotec), which was attached to the MACS separator. Subsequently, the effluent was collected, centrifuged, and resuspended with 200 µl 2% BSA and 200 U/ml RNase inhibitor in PBS. The cells were then mixed with AOPI (Cat# CS2-0106, Nexcelon Bioscience) at 1:1 ratio and the cell concentration measured with the Countess 3 Automated Cell Counter (Thermo-Fisher). The other half of the cells was homogenized with a pestle, and their nuclei were isolated with the Minute™ single nucleus isolation kit for tissue/cells (Cat# SN-047, Invent Biotechnologies, INC). Briefly, cells were resuspended in 600 µl cold lysis buffer, incubated on ice for 10 min and then transferred into a filter with a collection tube. The tubes were then centrifuged at 600 × g for 5 min, the supernatants removed, and the pellets resuspended in 500 µl cold washing buffer. After centrifugation at 500 × g for 5 min, the supernatants were removed, and the nuclei pellet resuspended with 55 µl 2% BSA and 200 U/ml RNase inhibitor in PBS. Nuclei were then mixed with AOPI at 1:1 ratio and the nuclei concentration measured with the Countess 3 Automated Cell Counter. After this, nuclei samples were processed in an identical way as to the cell samples.
Human islet transplantation into RAG-1−/− immunodeficient mice
One thousand human IEQs from four different donors (Additional file 1: Table S1) were transplanted into the renal sub-capsular space of 4–5-month-old euglycemic RAG1−/− mice as described previously in detail [26, 27], and the animals were followed for 3 months. At the end of the follow-up period, mice were sacrificed by CO2 inhalation. Human islet grafts harvested 3 months after transplantation were washed twice with PBS (Ca2 + /Mg2 + free) and centrifuged at 300 rpm for 3 min. Nuclei were isolated as indicated above. Animal studies and procedures were performed with the approval of and in accordance with guidelines established by the Institutional Animal Care and Use Committee of the Icahn School of Medicine at Mount Sinai (IACUC #2015–0107).
Single-cell and single-nucleus RNA sequencing, alignment, and matrix generation
Cells and nuclei samples were prepared according to the 10X Genomics Single Cell 3’ V3.1 Reagent Kit protocol, processed with 10X Genomic Chromium Controller for partitioning and barcoding, followed by the cDNA library generation. The total cell concentration was analyzed by Countess 3, then sequenced by NovaSeq 6000 System (Illumina) at the Weill Cornell Medicine, Genomics and Epigenomics Core. FASTQ files were aligned with Cell Ranger V.6.1.1 with Single Cell 3’ V3 chemistry on the 10X Cloud’s pipeline. In the analysis, we included the intronic reads only in the snRNA-seq data with GRCh38-2020-A library. For the human islet graft samples, which were also processed identically as the snRNA-seq dataset from in vitro islets, we included intronic reads as well and used the GRCh38-mm10-2020-A library to distinguish human and residual mouse genes. After the 10X h5 format file was generated, data were analyzed on the R platform with Seurat package V.4.1.1 .
Quality control, integration, and projection
Ambient mRNA adjustment on the scRNA-seq, snRNA-seq, and in vivo snRNA-seq data was performed using SoupX (20% contamination estimation) . Cells with less than a 500 gene count, less than 250 gene varieties, less than 0.8 log10 genes per UMI, and a greater than 20% mitochondrial gene ratio were filtered out. Then, doublets were algorithmically removed with the Doubltfinder package (20% estimation for in vitro scRNA/snRNA-seq, 10% estimation for in vivo snRNA-seq data) . Datasets were projected to publicly available Azimuth’s Human Pancreas reference (https://azimuth.hubmapconsortium.org/references/human_pancreas/) according to the script template of demo data with resulting reduction and cell type annotation [7, 28, 31,32,33,34,35,36]. Finally, differential expression analysis within snRNA-seq datasets for identification of new gene sets was performed. The empirically based doublet finder hyperparameter following 10X genomics guidelines (https://kb.10xgenomics.com/hc/en-us/articles/360001378811-What-is-the-maximum-number-of-cells-that-can-be-profiled-) was an 8% doublet formation ratio per cell/nucleus recovered for the in vitro studies. Using this doublet formation rate, several quadruple hormonal clusters and scattered and un-clustered cells were observed when we used Azimuth-based reduction. However, when we used a 20% doublet rate , we found a cleaner edge, and a more defined UMAP structure than when using the empirically based hyperparameter.
Human in vivo islet datasets were projected onto our integrated scRNA/snRNA-seq dataset as a reference. Since human islet grafts from harvested mouse kidneys naturally contain residual mouse cells and their mRNAs, nuclei with more than 10% of mouse genes were filtered out during quality control process. For the in vivo data, we used an empirically based doublet finder hyperparameter as the 10X genomics table suggests and found that a 10% doublet rate is appropriate to define several human islet cell populations with cleaner cluster edges and more defined UMAP structures.
Unsupervised data analysis
After data quality control, scRNA-seq and snRNA-seq data were integrated using Seurat’s SCTransform function without allocating method parameters . Next, cell type identity was assigned according to the normalized gene expression level, referencing the canonical pancreatic cell type genes . In vivo snRNA-seq data were created by integration among four samples of in vivo snRNA-seq data and cell types were annotated by referring to both canonical markers and newly found snRNA markers from this study.
Pseudo-time and RNA velocity
After separating the β-cell cluster from the main data structure, we performed pseudo-time analysis using the monocle 3 package. First, we extracted gene and cell metadata from PCA feature and RNA counts. Monocle 3 uses a graph-based learning strategy by providing an inbuilt function to choose the base. It measures a vector distance between an interested cell along the path, and length is defined by the quantified transcriptional change along the trajectory inferred as RGE (reversed graph embedding). The learned-graph trajectory yielded one linear, non-branching transition overall. Subsequently, we recreated the monocle object with them, then UMAP coordinates were embedded into monocle’s reduction data to maintain the UMAP structure of the β-cell cluster. Using monocle 3’s built-in learn graph function by setting the “FALSE” hyperparameter for using partition, pseudo-time order was created by setting up the base at β3 cluster. The unbiased progression was also confirmed by the RNA velocity analysis [38, 39].
For RNA velocity analysis, we extracted cell identifier, PCA/UMAP coordinate, and metadata from main Seurat data object. Then we recreated matrix file with GetAssayData function from Seurat and writeMM from Matrix package. Loom file was created with Velocyto by referencing GRCh38 human genome for scRNA/snRNA-seq and GRCh38-mm10 for the human islet graft in vivo snRNA-seq. After loom file is obtained, cell identifiers (UMIs), UMAP coordinates, and β-cell subtype were embedded to AnnData with scVelo Package. From AnnData formatted data, spliced/unspliced counts were measured by proportions function of scVelo. With the stochastic modeling hyperparameter, we estimated the velocity and visualized with velocity streamline of the β-cell sub-clusters’ transitions [40, 41].
Comparative analysis of β-cell subpopulations from previous published studies
To create the closest proximity to Dorrell et al. report data type , we created pseudo-bulk RNA sequencing data by taking average expression of the different β-cell subtypes in our study. Then, we visualized the expression of genes from Dorell et al. (, Fig. 4 in that report). To avoid the color-cue induced confusion, we adjusted our color scale with identical theme (blue- black-yellow) as well.
To define the molecular and cell function, single-cell level gene set enrichment analysis was performed using the escape package which accesses the entire Molecular Signature Database (v.7.0) [30, 42,43,44]. The whole C2 library enrichment with chemical and genetic perturbations and canonical pathways containing five databases (Biocarta, KEGG, PID, Reactome, and Wikipathways) were employed. Additionally, the C5 (Gene Ontology) library was also investigated using keywords such as β-cell, pancreas, pancreatic, and exclusion keywords such as cancer, carcinoma, anomaly, or other pathologies. After enrichment scores were calculated for each single cell, they were added to the metadata for analysis and visualization.
RNA in situ hybridization
RNA fluorescence in situ hybridization was performed on dispersed human islet cells using the RNA scope platform. Briefly, dissociated cells from human islets were plated on poly-D-lysine-coated coverslips and incubated for 30 min at 37 °C, 5%CO2. Cells were then fixed in 4% paraformaldehyde and in situ hybridization performed using the RNAscope® Multiplex Fluorescent Reagent Kit v2, probes Hs-ZNF385D (cat#1,161,581) targeting intron sequences in the region 808,843–810,453 of NC_000003.12:22,372,641–21,412,218 and Hs-ZNF385D (cat#116,501) targeting exon sequences in the region 150–1266 of NM_024697.3 which are present in ZNF385D pre-mRNA and mature mRNA respectively, and opal 620 (cat#FP1495001KT, Akoya Biosciences) following the instructions of the manufacturer (ACD Bio-Techne). Insulin immunolabeling was performed using anti-proinsulin/C-peptide antibody (cat# GN-ID4, DSHB), Alexa FluorTM 488 goat anti-rat IgG secondary antibody (cat#A11006, Invitrogen), and DAPI for nuclei detection.
Data are presented as bar graphs, violin plots, and scatterplots and show means ± SE. Statistical significance analysis was performed using Wilcoxon rank-sum test or Student’s t test for comparison between groups as indicated in the figure legends. P < 0.05 was considered statistically significant. The simplified asterisk statistical significance annotation followed conventional criteria of 0.05, 0.01, 0.0001, and 0.0001 for increment number of asterisks.
All protocols were performed with the approval of and in accordance with guidelines established by the Icahn School of Medicine at Mount Sinai Institutional Animal Care and Use Committee (IACUC #2015–0107).
RNA-seq profiling of cells and nuclei from adult human islets
Figure 1A and B depict the approach and data analysis workflow used for these studies. Islets from three healthy adult human islet donors (Additional file 1: Table S1) were used. Islet cells and nuclei from each donor were analyzed side-by-side. Cells from 3000 IEQs from each of the human islet preparations were dispersed, nuclei extracted from half of the cells and the other half went through the dead cell removal process kit. After quality assessment and counting, 5000–16,000 cells or nuclei for each sample were loaded into the 10X Genomics Chromium Controller, poly-A transcripts reversed transcribed and amplified, cDNA tagmented, and the resulting libraries sequenced to a depth of 250–500 million reads per sample (Additional file 1: Table S2). scRNA-seq and snRNA-seq data were projected onto the Azimuth human pancreas reference to determine islet cell populations and identify new gene sets as markers for these cells. In addition, scRNA-seq and snRNA-seq data were first integrated and then separated for the analysis of different β-cell subpopulations.
Outlier cells potentially representing low-quality cells or multiple cell captures were removed by excluding those with very low or high UMIs. Therefore, cells with either log-normalized UMI/cell-nucleus or gene/cell-nucleus values > 2.5 standard deviations (SDs) or < − 2.5 SDs from the median value were removed . Cells and nuclei with high expression of mitochondrial genes with a cutoff of 20% were also removed. Estimated ambient RNA contamination was set up to 20% for the SoupX algorithm, and data were adjusted accordingly. RNA contamination in the scRNA-seq and snRNA-seq dataset were similar (contamination estimator rho ranged between 0.02 and 0.07). Ultimately, 10,732 cells (4395, 4137, and 2200 cells per human islet preparation) and 11,018 nuclei (3033, 6533, and 1452 nuclei per human islet preparation) were analyzed. The number of genes and reads sequenced per cell was lower in the snRNA-seq compared to the scRNA-seq approach (Fig. 1C) but the ratio between usable versus sequenced reads determining sequencing efficiency was similar in both methods (0.974 ± 0.002 scRNA-seq vs. 0.966 ± 0.002 snRNA-seq). The percentage of mitochondrial genes sequenced in the nuclei preparations were below 1% and clearly and significantly lower than the mitochondrial genes sequenced in the cell preparations (Fig. 1C). As expected, the number of genes and reads was significantly higher when intron plus exon reads were analyzed compared with exon reads alone in snRNA-seq data (Fig. 1D).
Based on the data quality above, we next compared scRNA-seq using exon reads with snRNA-seq using both intron and exon reads for improved gene detection and mapping as previously done [23, 46]. scRNA-seq analyzes both nuclear and cytoplasmic transcripts with a majority being cytoplasmic, whereas snRNA-seq profiles mostly nuclear transcripts with minimal transcripts derived from cytoplasm or rough ER during nuclei isolation [47, 48]. Therefore, we expected that RNA-seq reads would be different in the scRNA and snRNA sequencing profiles. In cells, 17 ± 0.7% reads were intronic reads, while in nuclei these were 53 ± 1.9%. On the other hand, in cells 73 ± 1.6% reads were exonic reads in contrast to 32 ± 0.8% in nuclei. As expected, therefore, complete or near complete linearity of gene expression correlation occurred only when nuclei were compared to nuclei or cells to cells (R = 0.91–0.93) (Fig. 1E), while this correlation was lower when cells were compared with nuclei (R = 0.67) (Fig. 1F). Indeed, the number of common genes detected by both RNA sequencing approaches (UMI > 20) was 16,452 (71.3%), while 1433 genes (6.2%) were exclusively detected in scRNA-seq, and 5184 genes (22.5%) were exclusively detected in snRNA-seq (Fig. 1G). This indicates that almost 29% of the genes detected by both approaches are different for the same human islet samples suggesting that the two RNA sequencing methods might reveal differences in the identity of islet cell populations. Even using a range of UMIs from 0.1 to 100 to determine the number of common genes detected, differences in gene detection were 13, 15, 27, 29, 30, and 32% (Additional file 1: Fig. S1). Interestingly, unsupervised clustering of cells and nuclei using exonic reads (scRNA-seq) or intronic plus exonic reads (snRNA-seq) revealed clusters with similar locations and with strong overlap in their UMAPs using Seurat’s integration algorithm (Fig. 1H,I) .
Supervised classification of human islet cell types with scRNA-seq and snRNA-seq
We next projected the scRNA-seq and snRNA-seq datasets onto a publicly available Azimuth integrated human pancreas reference that comprises six different scRNA-seq datasets generated using several different single-cell technologies employing Seurat (Figs. 1B and 2A–D) [7, 28, 31,32,33,34,35,36]. Projection was done with exonic reads for scRNA-seq (Fig. 2A,B) and exonic or intronic plus exonic reads for snRNA-seq (Fig. 2C,D). When we projected the human islet scRNA-seq data of the current study onto the reference, we found that there was a near perfect alignment of the different human islet cell populations with a prediction score median of 1, and a mean of 0.948 (Fig. 2A,B). Projection of the snRNA-seq data to the reference using exonic reads or intronic plus exonic reads also led to a high degree of alignment (Fig. 2C,D) with a prediction score significantly higher with exon plus introns than with exons alone (median 0.967 vs 0.944, and mean 0.898 vs 0.861) (Fig. 2D). To determine whether the observed higher probability of alignment to a reference with exon plus intron reads could be explained by different UMI depth, we subsampled the intron-inclusive library with the median value of 1247 UMIs (median of exonic reads only library) and analyzed the reference alignment prediction score. We found that the alignment prediction score was still superior with the intron-inclusive library than with the exon only library following subsampling (Additional file 1: Fig. S2). This further validates the use of information from intron plus exon reads for detailed analysis of human islet cell populations using snRNA-seq. The data also indicate that human islet cell clusters from scRNA-seq and snRNA-seq share a high degree of similarity and that snRNA-seq data containing intron plus exon reads are interchangeable with scRNA-seq data for the identification of human islet cell type clusters.
Next, we tested the association of the different clusters generated from the scRNA-seq and snRNA-seq data (Fig. 2A, C) with gene expression levels of canonical genes in different endocrine cell clusters (Fig. 2E–H). A strong correlation was observed between established canonical gene cell markers (GCG, INS, SST, and PPY) along with several other known selective markers with α-, β-, δ-, and γ-cells in scRNA-seq. Thus, cell types in the UMAP were accordingly assigned in the scRNA-seq analysis (Fig. 2E, G, Additional file 2: Data S1). However, the strong correlation of these canonical gene markers observed in scRNA-seq was weaker in the snRNA-seq for α-, β-, and γ-cells (Fig. 2F, H, Additional file 3: Data S2). For example, in β-cells, INS and IAPP are the top differentially expressed genes, whereas they are not the top differentially expressed genes in the snRNA-seq datasets. Furthermore, even after subsampling the UMI to median of snRNA-seq (which is lower than scRNA-seq, Fig. 1C), the pattern of expression level of canonical genes is weaker in the snRNA-seq for α-, β-, and γ-cells (Additional file 1: Fig. S2B). These results highlight the need for the identification of new gene sets as markers for islet endocrine cells that more appropriately define them in the snRNA-seq analysis.
Novel gene sets in the snRNA-seq dataset for the identification of human islet endocrine cell types
We next performed differential gene expression analysis between scRNA-seq and snRNA-seq samples and investigated the biotype of the snRNA-seq enriched genes. Even with the inclusion of intronic reads, most of the genes in snRNA-seq are protein-coding genes (Fig. 3A). Next, we annotated our snRNA-seq data with projection onto Azimuth’s human pancreas reference and tested differential gene expression for each cluster with the entire dataset (Fig. 1B). Using this approach, differentially expressed genes in each cell cluster were identified with a p value ~ 0 and log2FC greater than 1.5. Even if the candidate genes were qualified by these criteria, we omitted genes that showed considerable expression (log2FC > 0.85) in other cell clusters. Thereby, we compiled a list with the top four differentially expressed genes for each cell type (Fig. 3B and Additional file 3: Data S2). Interestingly, none of the top four differentially expressed genes in scRNA-seq that define endocrine cells, i.e., INS, GCG, STS, or PPY appear in this short list of differentially and selectively expressed genes in snRNA-seq. This indicates that these canonical genes are not the top differentially expressed genes in the snRNA dataset among α-, β-, δ-, and γ-cells. To confirm the reliability of these newly identified gene markers in endocrine cells, we tested them on the snRNA-seq and scRNA-seq data objects. They showed a clear and mostly exclusive pattern of expression in the corresponding cell clusters both in the snRNA-seq and scRNA-seq, but with higher expression in the snRNA-seq datasets (Fig. 3C,D). In particular, top endocrine cell gene markers (PTPRT, ZNF385D, LRFN5, and CNTNAP5 for α-, β-, δ-, and γ-cells, respectively) displayed a more distinctive localization pattern than their corresponding canonical single-cell clustering gene markers (GCG, INS, SST, and PPY) for α-, β-, δ-, and γ-cells, respectively, in the snRNA-seq data objects (Figs. 2H and 3E). Of note, CNTNAP5 did not display highly different expression pattern in γ-cells, yet it was considered a γ-cell gene marker based on high adjusted p value (1.58 × 10−5) and log2FC of 1.654. Interestingly, the top differentially expressed genes in the snRNA-seq analysis in non-endocrine cells contained the canonical gene markers that define these cell types in scRNA-seq (REG1A, CFTR, FLT1, COL1A1, and PRKG1 for acinar, ductal, endothelial, activated stellate, quiescent stellate cell, respectively). This suggests an interesting dichotomy between human endocrine and non-endocrine cells regarding steady-state transcript abundance of canonical genes (Fig. 3F). To gain confidence on the usefulness of the new gene sets for snRNA-seq analysis of human islet cells, we analyzed the only previously published snRNA-seq dataset of isolated human islets  but using exon + intron reads. We projected the snRNA-seq data into the Azimuth integrated human pancreas reference and analyzed the expression levels of the novel gene sets. As shown in Additional file 1: Figs. S3A and S3C, the novel gene sets described above showed a clear and mostly exclusive pattern of expression in the corresponding cell cluster; however, the number of nuclei in this study was limited, 471 nuclei, and no alpha cells were detected.
To validate the presence of these newly identified genes from the snRNA-seq analysis as cell markers, we focused on β-cells and performed RNA scope to detect ZNF385D mRNA in dispersed human islet cells from healthy donors (Additional file 1: Table S1). As shown in Fig. 3G, ZNF385D mRNA expression was clearly and uniquely detected in human β-cells. Expression using an intronic probe was limited to the nucleus (Fig. 3G, top) while using an exonic probe located the signal in both the cytoplasm and the nucleus (Fig. 3G, bottom).
Comparative scRNA-seq and snRNA-seq analysis distinguishes three different β-cell subtypes
To identify human β-cell subtypes within the β-cell cluster, we created a new data object integrating scRNA-seq and snRNA-seq datasets to compare INS gene expression patterns (Figs. 1B and 4A–C). Combining the two datasets effectively increased the power of the analysis while providing additional information from both cytoplasmic and nuclear transcriptomes. After sub-setting the identified β-cell cluster, we generated clusters with a Louvain resolution of 0.8 (Fig. 4C) to assign three β-cell sub-clusters. INS gene expression in the β-cell clusters between scRNA- and snRNA-seq data objects was different in terms of topographical location (Fig. 4D,E and Additional file 4: Data S3). Cluster 3 displayed lower INS expression in scRNA-seq data but the highest in snRNA-seq data object (Fig. 4E), suggesting that cluster 3 includes β-cells with mainly INS pre-mRNA since snRNA-seq analyzes mostly pre-mRNA. Similar results were observed when a previous snRNA-seq dataset from human islets  was analyzed (Additional file 1: Figs. S3D and S3E).
Detailed analysis of cells in between clusters 2 and 3 following cluster separation with a high Louvain algorithm resolution of 2.0 to resolve smaller clusters (Additional file 1: Fig. S4A) identified cluster 23 as the cluster in between clusters 2 and 3 (Additional file 5: Data S4). Differential gene expression analysis of cluster 23 (transitioning) cluster against all remaining clusters showed high expression levels of mostly ribosomal protein genes (Additional file 1: Fig. S4B-C). GSEA (Reactome) depicted biological translation elongation and termination as the top biological processes in these inter-cluster 2–3 cells (Additional file 1: Fig. S4D).
Next, we created pseudo-time and RNA velocity trajectory graphs, assigned cluster 3 as the base (Fig. 4F, and Additional file 1: Fig. S5) and rearranged the order of each cluster according to the pseudo-time or RNA velocity trajectory into β1, β2, and β3 cells (Fig. 4G and Additional file 1: Fig. S5A-B). Similar progression from cluster 3 to cluster 1 occurred by using integrated data from both datasets in pseudo-time or RNA velocity analysis. RNA velocity analysis of individual scRNA-seq data showed similar transition from cluster 3 to cluster 1. Interestingly, RNA velocity analysis of only snRNA-seq data showed bifurcation from the end of cluster 3 to edge of cluster 3 and then from cluster 2 to cluster 1 (Additional file 1: Fig. S5B). These results indicate that identification and transition among the β-cell sub-clusters can be observed with integrated or individual RNA-seq data but this transition seems to differ between scRNA-seq and snRNA-seq for cluster 3. This can very likely be because RNA velocity is based on the ratio of exon-to-intron reads, leveraging the fact that newly transcribed, unspliced mRNAs infer a time derivate of gene expression state, but exon-to-intron reads are low in snRNA-seq (Additional file 1: Fig. S5A) complicating the interpretation of the transition among clusters. Therefore, for integrated or scRNA-seq data, RNA velocity is appropriate but for snRNA-seq it seems that RNA velocity is less optimal for analyzing the progression of β-cell maturation.
Since cells in the β1 cell cluster have stable INS expression as inferred from the scRNA-seq (mature mRNA) β-cell cluster object, but very low INS expression as inferred from the snRNA-seq (pre-mRNA) β-cell cluster object, we considered these cells as the INS-rich cell subpopulation. Next, we looked at ZNF385D expression, the highest differentially expressed gene in snRNA-seq in β-cells and found minimal expression in the β-cell sub-clusters in the scRNA-seq data object but different topographical location in the snRNA-seq data object (Fig. 4H,I) where β1 represents cells with high ZNF385D pre-mRNA expression, opposite to INS expression (Fig. 4E). Next, we looked at the expression of the INS mRNA-binding protein HNRNPA2B1, an RNA-binding protein that regulates INS mRNA stability and translation , to determine whether the β2 cell cluster represents a transition stage between β3 and β1. As shown in Fig. 4J,K, HNRNPA2B1 expression is increased in clusters 2 and 1 compared to cluster 3, suggesting that cluster 2 may represent an “in transition” β-cell type in which cells go from transcriptionally active β3 cells to β1 cells with mature stored INS mRNA.
Furthermore, we also performed a comparison analysis of β-cell subtypes described in Dorrell et al.  with β-cell subpopulations in our study. We referred gene sets from the RNA-seq data in Dorell et al. study (, Fig. 4 in that report) and visualized gene expression by average heatmap (Additional file 1: Fig. S6A-B). Gene sets for ST8SIA1− β1/β2 (HCN4 to G6PC2) and ST8SIA1+ β3/β4 (KCNE4 to SIX3) in the Dorrell et al. report had a mixed pattern in our datasets (Additional file 1: Fig. S6A). However, we find that gene sets for CD9− β1/β3 (FKBP5 to NPY) and CD9+ β2/β4 (CD9 to EPB41L1) in the Dorrell et al. report had a closer correlation with our dataset (Additional file 1: Fig. S6B). Indeed, gene set expression in β3 correlated with Dorrell et al. β2/β4 and the current β1 correlated with Dorrell et al. β1/β3. Expression of CD9 corresponded well with their study. In addition, β1/β3 in the Dorrell et al. report showed the highest GSIS response and the current β1 cells showed the highest expression level of genes involved in the regulation of insulin secretion pathway (see below). Moreover, the current β2 cells showed a transitional expression pattern between β3 and β1.
Gene pathway analysis in the three different β-cell subtypes
To research the potential biological differences among β3, β2, and β1, we performed GSEA of the combined datasets from scRNA- and snRNA-seq experiments [42,43,44,45]. Interestingly, enrichment of genes that define the biological processes of extracellular matrix (ECM) formation, interaction, and response were uniquely present in the β3 sub-cluster (Fig. 5A). Equally, GSEA also indicated that the biological processes of intracellular vesicle budding, transport, and formation are mainly present in the β2 sub-cluster (Fig. 5B), while genes for the biological processes of insulin secretion, processing, and glucose metabolism are mainly represented in the β2 and β1 sub-clusters (Fig. 5C). Additional information was also obtained on several biological processes of importance for the development, differentiation, gene expression regulation, and proliferation of the β-cell when we input the datasets from both RNA-seq approaches into the GSEA. As shown in Fig. 5D, the genes involved in β-cell proliferation were more highly expressed in the β3 sub-cluster, the genes involved in the regulation of gene expression were prominent in the β2 sub-cluster while the genes involved in β-cell development and differentiation were more highly expressed in the β1 sub-cluster. These results clearly delineate different sets of genes for specific cell functions in the different β-cell sub-clusters emphasizing the heterogeneity of β-cells in the human islet.
Single-nucleus RNA-seq analysis of human islets in vivo
Next, we sought to examine the different human islet cell populations in vivo using human islet grafts transplanted in euglycemic immunosuppressed mice. For this purpose, we transplanted 1000 human IEQs from four healthy donors into the kidney capsule of RAG1−/− mice and harvested the graft 3 months after transplantation (Fig. 6A). Nuclei were directly extracted from the grafts by the Minute™ single nucleus isolation kit and 5000–16,000 nuclei per sample after quality assessment and counting were loaded into the 10X Genomics Chromium Controller, poly-A transcripts reversed transcribed and amplified, cDNA tagmented, and the resulting library sequenced to a depth of 250–500 million reads per sample (Additional file 1: Table S2). Ultimately, 7765 nuclei (1632, 1509, 3911, and 713 nuclei per human islet preparation) were analyzed. The number of genes was 2226 ± 263, the number of gene counts was 4125.64 ± 263, and the ratio between usable versus sequenced reads determining sequencing efficiency was 0.999 ± 0.001. The percentage of mitochondrial genes sequenced in the nuclei preparations were below 1%. snRNA-seq data were projected onto the Azimuth human pancreas reference to identify islet cell populations using canonical gene sets as well as the new gene sets described earlier (Figs. 1A and 6A). In addition, the snRNA-seq data were projected onto the integrated scRNA-seq and snRNA-seq data from the in vitro studies for the analysis of different β-cell subpopulations (Fig. 6A). Gene counts and/or UMI count outliers or cells with high expression of mitochondrial genes or with ambient RNA contamination ≥ 20% were removed. During the quality control process, we found that ≤ 10% mouse gene ratio was optimal for identifying different cell types in vivo in this islet transplant setting without considerable mouse gene influence on the clustering pattern.
Using the reference-based reduction and annotation with Azimuth, we confirmed seven distinct human islet cell clusters, primarily comprised of endocrine cells (Fig. 6B). For simplicity, we omitted clusters with less than five cells from the dataset. The set of gene markers identified by the snRNA-seq analysis in vitro (Fig. 3C) showed clearer patterns of specific cell expression in both the average expression-based dotplot (Fig. 6C and Additional file 6: Data S5) and the scatterplot (Fig. 6D) compared to the non-specific pattern of the canonical gene markers. This specific cell cluster alignment of the newly identified gene sets from the snRNA-seq analysis in vitro persisted when an internal reference-based reduction and annotation (the scRNA-seq plus snRNA-seq integrated datasets from the in vitro samples, unsupervised, and marker annotated) was used (Fig. 6E–G). To gain confidence on the usefulness of the new gene sets for snRNA-seq analysis of human islet cells in grafts, we analyzed the previously published snRNA-seq dataset of human islet grafts by Basile et al.  using exon + intron reads. As shown in Additional file 1: Fig. S3B-C, the novel gene sets showed a clear and mostly exclusive pattern of expression in the corresponding cell clusters, validating the new gene sets as specific markers of cell types.
β-cell subtypes in human islets in vivo
First, we determined whether the β-cell subtypes identified in the scRNA-seq/snRNA-seq in vitro study were still present in human islets in the in vivo setting. Thus, we projected the in vivo snRNA-seq dataset onto the in vitro scRNA-/snRNA-seq dataset reference which is pre-labeled with β-cell subtypes (Fig. 6E) and extracted the β-cell cluster (Fig. 7A and Additional file 4: Data S3). We separated β-cell sub-clusters from the main data cluster and examined the gene expression patterns (Fig. 7A–H). Interestingly, and in contrast to the snRNA-seq data of in vitro human islets, the β1 cluster in human islets in vivo displayed a similar level of INS expression compared with the β2 and β3 clusters (Fig. 7B,C). Similar results were observed when a previous snRNA-seq dataset from human islet grafts  was analyzed (Additional file 1: Fig. S3D-E). In addition, in our dataset, the expression of ZNF385D and HNRNPA2B1 appeared to be similar in the β-cell sub-clusters of human islets in vivo and in vitro (Fig. 7D–G). Importantly, the proportion of cells in the β1 sub-cluster with stable INS expression was significantly increased while the proportion of cells in the β2 transition cluster and the β3 with mostly INS pre-mRNA were significantly decreased in human islets in vivo compared with in vitro (Fig. 7H). We speculate that this may represent a phenotypic or maturational transfer of cells from the lower INS expressing β3 and β2 groups to the more mature INS mRNA β1 group, potentially reflecting maturational features of the in vivo microenvironment as compared to the less physiological in vitro conditions.
We also performed gene set enrichment analysis of the different β-cell subpopulations of human islets in vivo using the same approach as above (Figs. 5 and 8). As observed in vitro (Fig. 5), the β3 cell subtype displayed higher expression levels of genes involved in ECM biological processes compared with β2 and β1 cell subtypes (Fig. 8A). The expression of genes involved in the biological processes of intracellular vesicle budding, transport and formation, insulin secretion, processing and glucose metabolism, β-cell differentiation, development, proliferation, and gene expression regulation in the β2 and β1 cell subtypes remained comparable in vivo and in vitro (Fig. 8B–D). Interestingly, as shown in Additional file 1: Fig. S7, the expression of genes included in the “reactome_glucose-metabolism” gene set such as SLC2A2, GK, PFKFB2, and GPI increased their expression while G6PC2 decreased expression in β2 and β1 compared with β3 cells suggesting upregulation of glycolysis-related gene pathways. This correlates with β2 and β1 displaying higher expression of genes involved in differentiation/maturity and insulin secretion. Indeed, HKI and HKIII, SLC2A1 and SLC2A3, LDHA, ALDOB and SLC16A1 (“disallowed” genes,  were expressed at very low levels or not expressed at all in the β-cell sub-clusters. Notably, human β3 cells displayed lower expression of genes involved in β-cell proliferation in vivo than in vitro (Fig. 8B–D), suggesting again perhaps that the in vivo microenvironment may provide cues for β-cells to favor a more functional, but less mitogenic status compared with the less physiologic in vitro setting.
Over the past decade, scRNA-seq analysis has been employed to analyze specific human islet cell populations and their transcriptome profiles in vitro. However, its application for analyzing human islet cell populations in pancreas samples postmortem or islet grafts post-transplantation has been limited by the need for mechanical and/or enzymatic tissue cell dissociation and inadequate low yields of cell numbers that likely reflect cells that were resistant to damage and death and are also removed from their normal cell–cell interactions [18,19,20, 46, 53, 54]. In addition, in many cases, only frozen or fixed human islets/pancreas samples, that are unsuitable for preparation of single cells, are available. These limitations complicate the use of scRNA-seq for in vivo human pancreas/islet tissue analysis. In this report, we make eight important new observations that address these problems.
First, we demonstrate for the first time that snRNA-seq can accurately annotate human islet cell types in vivo, their transcriptomic profiles, their heterogeneity, and their corresponding biological processes. Second, we make the observation that including intronic pre-mRNA reads in the snRNA-seq analysis provides greater accuracy and superior correlation scores than using only exonic reads for human islet cell type annotation. Third, we provide the first reference annotation library (coding- or non-coding region-based) for human islet snRNA-seq. Remarkably, this annotation library proves to be superior in unsupervised cell cluster annotation to existing “canonical” gene set annotation markers for snRNA-seq. Fourth, to our surprise, the top four differentially and selectively expressed genes in human endocrine cells derived from snRNA-seq are not the “canonical genes” used to define islet cell populations in established gene sets. Fifth, through integration of scRNA-seq and snRNA-seq datasets in the same human islets, and by comparing the expression of both mature mRNA and pre-mRNA encoded by the INS gene, the principal β-cell identity factor, we detect three different β-cell subpopulations with different transcriptome profiles, ranging from an INS pre-mRNA-rich cluster to a cluster rich in mature INS mRNA. Sixth, using GSEA tools, we observe that the three β-cell subtypes likely perform different fundamental biological processes. Seventh, we extend these integrated datasets from in vitro scRNA-seq and snRNA-seq studies to in vivo in human islet grafts. We observe these same global islet cell populations and the three apparently distinct β-cell subpopulations, validating the use of snRNA-seq on intact, un-dissociated human islets, and on intact human islet grafts. These approaches can also be applied to stored intact tissues as well . And eighth, we find that the proportions of cells in the three β-cell clusters in vivo are altered as compared to in vitro, in agreement with the notion that the in vivo environment differs from in vitro culture conditions, in a manner that favors a mature, fully functional β-cell phenotype (β1 cluster) over an incompletely differentiated proliferative β-cell phenotype (β3 cluster). Collectively, these observations provide a new lens through which to understand human islet cell biology, and new tools to explore human islet cell biology in vivo.
Our initial goal was to establish a method that could be leveraged to study intact human islet cell populations in human islet grafts in mice and defining their specific transcriptome profile in vivo. The current reference libraries to define human pancreas cell populations and transcriptome profiles have been derived from scRNA-seq studies using principally in vitro studies based on exon reads, with the rationale that mature RNAs comprise the majority of cellular mRNA in intact cells [7, 28,29,30,31,32]. However, the majority of reads obtained from our snRNA-seq analysis relate to intron sequences, reflecting the predominance of pre-mRNA in the nucleus. Based on this, we first explored whether the snRNA-seq dataset with and without intronic reads would provide similar results when projected onto the already established Azimuth scRNA-seq human pancreas reference library [7, 28,29,30,31,32]. Unexpectedly, the inclusion of intron reads in the snRNA-seq dataset actually enhanced the prediction score for cell type annotation in the Azimuth reference. Similar results have been found in other tissues as well . Thus, we elected to incorporate intron plus exon reads in our subsequent analyses.
We were surprised to observe that expression of the main “canonical” genes used to define human endocrine cell populations in scRNA-seq analysis (INS, GCG, SST, and PPY) were not the top four differentially and selectively expressed genes in the snRNA-seq studies. This indicates that the most abundant mRNAs in human islet endocrine cells do not reflect the most profused pre-mRNAs in those cells. This is perhaps not surprising when considering that a large proportion of steady-state cytoplasmic mRNA in β-cells is the relatively stable insulin mRNA, stored on polyribosomes, awaiting translation in response to a glucose stimulus . Eukaryotic gene expression is controlled by multiple steps: first, pre-mRNA is transcribed in the nucleus; second, pre-mRNA is spliced; third, mature mRNA is assembled with specific RNA-binding proteins forming a messenger ribonucleoprotein (mRNP) complex; fourth, mRNPs are targeted to and translocate through nuclear pore complexes (NPCs) in the nuclear membrane; and fifth, the mRNPs are directionally released into the cytoplasm for translation [56,57,58,59]. Alterations in any of these steps can lead to changes in the nuclear content of specific pre-mRNAs and in gene expression patterns [58, 59]. Insulin pre-mRNA contains two introns that are not spliced with the same efficiency [60, 61]. In human islets, intron 1– and intron 2–containing INS pre-mRNAs are ~ 150- and 2000-fold less abundant, respectively, than mature INS mRNA . Importantly, it has been suggested that INS pre-mRNA levels (particularly intron 2–containing pre-mRNAs) may be the most reliable reflection of acute changes to human INS gene transcription induced by glucose . Based on these studies, and since our experiments compared scRNA-seq and snRNA-seq in the same human samples cultured at 5 mM glucose, the fact that INS mRNA is the top gene in scRNA-seq but not in the snRNA-seq suggests that this is not the result of changes in glucose stimulus, donor demographics, splicing rates, mRNPs, NPC complexes, or translation rates. Nevertheless, these are parameters that need further studies to decipher how they can alter INS and other pre-mRNAs in multiple conditions affecting endocrine cells including aging, hyperglycemia, and inflammation among others. These findings also mandate development of new gene sets and a new reference library for annotation of snRNA-seq datasets derived from human islet samples. Here, we have established this reference library and identified new gene sets for human endocrine cells using snRNA-seq analysis. These include ZNF385D, TRPM3, LRFN2 and PLUT (for β-cells); PTPRT, FAP, PDK4, and LOXL4 (for α-cells); LRFN5, ADARB2, ERBB4, and KCNT2 (for δ-cells); and CACNA2D3, THSD7A, CNTNAP5, and RBFOX3 (for γ-cells). Analysis of the only published data on snRNA-seq in isolated human islets  confirmed the usefulness of this new gene sets for snRNA-seq analysis using exon + intron reads. To validate ZNF385D, encoding zinc finger protein 385D, involved in neurocognitive development in the brain , but whose presence and function have not been demonstrated in human β-cells thus far, we performed ZNF385D RNA scope. We show for the first time that an intronic ZNF385D probe detects the gene in nuclei, while an exonic ZNF385D probe detects the gene in both nuclei and cytoplasm of human β-cells, but not other islet cell types. Future studies will need to decipher the biological role of this zinc finger protein in the human β-cell.
Through integration of the scRNA-seq and the snRNA-seq datasets from human islets in vitro, and using INS expression as the starting point in the pseudo-time and RNA velocity analyses, we identify three distinct human β-cell subpopulations. Since the 1980s, heterogeneity of rodent and human β-cells has been described identifying β-cell subpopulations based on their different capacities of insulin secretion, insulin biosynthesis, glucose metabolism, Ca2 + dynamics, intracellular network connectivity, membrane excitability and electrical coupling, and hub cells, leader cells, first responders, wave originators, and followers have been identified among the different β-cells [63,64,65,66,67,68]. β-cell heterogeneity can influence when diabetes occurs and the response to treatments; therefore, identification of functional and regenerative characteristics and markers of distinct β-cell subpopulations can help in the design of novel regenerative and functional approaches to treat diabetes . Studies by Lickert’s group have identified two subpopulations of β-cells, proliferation-competent β-cells, and mature β-cells with distinct molecular, physiological, and ultrastructural features . scRNA-seq approaches [16, 17, 70] have also identified several human β-cell subpopulations with different transcriptome profiles, but never using a combination of both scRNA-seq and snRNA-seq techniques. Indeed, scRNA-seq studies have identified different human β-cell subtypes based on their transcriptional profiles on ER stress and autophagy markers and functional profiles [16, 17, 71]. Transcriptional deregulation leading to cellular immaturity, reorganization of β-cell transcription factor networks, and reduced glucose-stimulated insulin release in different β-cell subtypes occurs during β-cell aging . In the current study, and since INS mRNA expression is higher in β3 cells in the snRNA-seq data, we postulate that β3 cells represent the β-cells which have higher levels of INS pre-mRNA. Conversely, in β1 cells where there is little or no expression of INS in the snRNA-seq, but very high expression level in scRNA-seq, we speculate that these β-cells represent a population in which mature INS mRNA is stored, awaiting transcription. Finally, β2 cells appear to represent cells in a transitional stage closer to β1. In support of these possibilities, GSEA analysis of the biological processes occurring in these β-cell sub-clusters highlighted that although all are β-cells, β1, β2, and β3 display a spectrum of functions with respect to ECM, vesicle formation, insulin processing and secretion, glucose metabolism, proliferation, gene expression regulation, and differentiation. Comparison analysis with previous β-cell subpopulations identified by Dorrell et al.  shows that at least with the CD9 cell marker, the current study’s β3 subtype correlates with Dorrell et al. β2/β4 and the current study’s β1 (highest expression in genes related to regulation of insulin secretion) to β1/β3 (highest insulin secretion response) in the Dorrell et al. report . This illustrates the utility of snRNA-seq analysis for further deciphering the heterogeneity in defined β-cell populations. However, the lack of functionality tests in the human islet cells limits the association of β-cell subpopulations with their functional state in our studies.
Recently, Reddick et al. have analyzed human islet grafts using scRNA-seq and identified populations of α-, β-, and δ-cell subsets . These studies used a large number of islets (4000) transplanted into mice and required enzymatic and mechanical tissue dissociation of the islet grafts. Assuming an average of 1000 cells per islet, 4000 islets should contain something in the range of 4,000,000 cells. Importantly, Reddick et al. retrieved approximately 700 cells for analysis. This is a general experience and further emphasizes the concept that the small subset of cells recovered from intact tissue may represent the few most able to resist harsh cell harvesting techniques, and not the general population of islet cells.
More recently, Basile et al. also reported the use of snRNA-seq on human islet grafts transplanted into mice . This study excluded intronic reads, focusing only on exonic reads, and observed a strong concordance among islet cell types and gene signatures between cells analyzed by scRNA-seq and snRNA-seq. In retrospect, we speculate that this study likely would have been further enhanced by including intronic reads, these being the most abundant nuclear reads, both with respect to accuracy of islet cell type annotation, and gaining additional information on the differential pre-mRNA levels of genes of interest. This is illustrated here, where we found that the novel gene annotation data derived from the snRNA-seq analysis in vitro permitted us to define the identity of the human islet cells in the islet grafts with greater accuracy and resolution than scRNA-seq, and also allowed us to distinguish the pre-mRNA status of cells and genes of interest. Using this approach, we were able to identify the three β-cell subpopulations in grafts in vivo enriched for similar biological processes as those observed in vitro. Furthermore, these results were also confirmed when using the human islet dataset from the Basile et al. paper . Interestingly, by pseudo-time analysis, we observed that cells in clusters β3 and β2 appeared to transition into, or at least be replaced by β1 cells, as islets moved from in vitro to in vivo, such that β1 cells became the most prominent β-cell subtype in the human islet graft. Nevertheless, the change in the proportion of β-cell sub-clusters found between islets and islet grafts could also reflect the different organization of the endocrine tissue in both conditions and not related to the response of β-cells to the in vivo environment per se. Future studies employing spatial transcriptomics which provide morphological context to gene expression changes and presence of different cell subclasses are warranted to determine this possibility.
Interestingly, although the “biological processes” categorizations were generally maintained in the three β-cell sub-clusters in vitro and in vivo, we note that the “proliferation” biological process was present in the β3 sub-cluster in vitro, but not present in this sub-cluster in vivo. This makes several points. First, β-cell proliferative capacity is typically higher in in vitro studies as compared to in vivo studies. This dichotomy is clearly illustrated in studies testing therapeutic agents that induce β-cell proliferation in vivo for regenerative purposes, which consistently reveal the lower β-cell proliferation rates in vivo than in vitro [26, 27, 71]. Second, dynamic changes occur among the different β-cell subpopulations in vitro and in vivo. And third, that there is an inverse correlation between the β-cell proliferation process and insulin secretion at least in vitro since β3 cells have proliferative capacity, but low insulin secretion pathway, while in β1 cells it is the opposite, highlighting the proliferation-function dichotomy in β-cells [73, 74]. Finally, the use of snRNA-seq and β-cell subpopulations analysis in vivo in islet grafts invites a future plethora of studies analyzing the effect of drugs and physiological and pathological situations in vivo in human islet cells. Furthermore, harnessing the power of this technique that allows capturing of cell classes that are difficult to achieve by dissociation based scRNA-seq including endothelial cells, resident macrophages among others could help to identify and analyze rare cell subpopulations and their transcriptomic profiles.
In summary, the current limitations to obtain a high yield of unstressed cells from tissues for sc-RNA-seq analysis together with the information in this study provide strong support for the use of snRNA-seq analysis and intron-inclusive libraries to define cell populations and transcriptome profiles of islet cells in human islets/pancreas tissue in vivo. Using this approach, we have found new gene sets to define islet cell populations in snRNA-seq studies that can be used for islet cell identification and transcriptome profile in vivo. We have identified three β-cell subpopulations with dynamic gene profiles under basal conditions. Finally, we show that that this technique will be valuable in assessing effects on human islet cell subtypes in islet grafts transplanted into mice in response to multiple physiologic, pathologic, and therapeutic challenges, as well as models of T1D and T2D.
We propose that snRNA-seq and pre-mRNA analysis can accurately identify human islet cell populations, subpopulations, and their dynamic transcriptome profile in vitro and in vivo in human islet grafts.
Availability of data and materials
All raw sequencing reads for the scRNA-seq and snRNA-seq data generated from cultured human islets and engrafted human islet tissues are compiled and available from NCBI SRA PRJNA900882 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE217837) under accession number GSE217837 . The publicly available scRNA-seq and snRNA-seq datasets on human islets that were also used in this manuscript are from Basile G et al.  who used single-nucleus RNA sequencing to interrogate transcriptomic profiles of archived human pancreatic islets, PRJNA856476, NCBI Sequence Read Archive, https://www.ncbi.nlm.nih.gov/bioproject/PRJNA856476 and PRJNA631512, NCBI Sequence Read Archive, https://www.ncbi.nlm.nih.gov/bioproject/PRJNA631512. R/Shell/Python code for data handling and analysis is publicly available through from Github repository (https://github.com/randystyle21/scRNA-snRNA-Marker/blob/main/README.md) .
Kaestner KH, Powers AC, Naji A, HPAP Consortium, Atkinson MA. NIH initiative to improve understanding of the pancreas, islet, and autoimmunity in type 1 diabetes: The Human Pancreas Analysis Program (HPAP). Diabetes. 2019;68(7):1394–402.
Halban PA, Polonsky KS, Bowden DW, Hawkins MA, Ling C, Mather KJ, Powers AC, Rhodes CJ, Sussel L, Weir GC. β-cell failure in type 2 diabetes: postulated mechanisms and prospects for prevention and treatment. J Clin Endocrinol Metab. 2014;99(6):1983–92.
Sachs S, Bastidas-Ponce A, Tritschler S, Bakhti M, Böttcher A, Sánchez-Garrido MA, Tarquis-Medina M, Kleinert M, Fischer K, Jall S, Harger A, Bader E, Roscioni S, Ussar S, Feuchtinger A, Yesildag B, Neelakandhan A, Jensen CB, Cornu M, Yang B, Finan B, DiMarchi RD, Tschöp MH, Theis FJ, Hofmann SM, Müller TD, Lickert H. Targeted pharmacological therapy restores β-cell function for diabetes remission. Nat Metab. 2020;2(2):192–209.
Wang YJ, Kaestner KH. Single-cell RNA-Seq of the pancreatic islets–a promise not yet fulfilled? Cell Metab. 2019;29(3):539–44.
Wang YJ, Schug J, Won KJ, Liu C, Naji A, Avrahami D, Golson ML, Kaestner KH. Single-cell transcriptomics of the human endocrine pancreas. Diabetes. 2016;65(10):3028–38.
Xin Y, Kim J, Okamoto H, Ni M, Wei Y, Adler C, Murphy AJ, Yancopoulos GD, Lin C, Gromada J. RNA sequencing of single human islet cells reveals type 2 diabetes genes. Cell Metab. 2016;24(4):608–15.
Segerstolpe Å, Palasantza A, Eliasson P, Andersson EM, Andréasson AC, Sun X, Picelli S, Sabirsh A, Clausen M, Bjursell MK, Smith DM, Kasper M, Ämmälä C, Sandberg R. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 2016;24(4):593–607.
Ramond C, Beydag-Tasöz BS, Azad A, van de Bunt M, Petersen MBK, Beer NL, Glaser N, Berthault C, Gloyn AL, Hansson M, McCarthy MI, Honoré C, Grapin-Botton A, Scharfmann R. Understanding human fetal pancreas development using subpopulation sorting. RNA sequencing and single-cell profiling Development. 2018;145(16):dev165480.
Augsornworawat P, Maxwell KG, Velazco-Cruz L, Millman JR. Single-cell transcriptome profiling reveals β cell maturation in stem cell-derived islets after transplantation. Cell Rep. 2020;32(8): 108067.
Camunas-Soler J, Dai XQ, Hang Y, Bautista A, Lyon J, Suzuki K, Kim SK, Quake SR, MacDonald PE. Patch-Seq links single-cell transcriptomes to human islet dysfunction in diabetes. Cell Metab. 2020;31(5):1017-1031.e4.
Avrahami D, Wang YJ, Schug J, Feleke E, Gao L, Liu C; HPAP Consortium, Naji A, Glaser B, Kaestner KH. Single-cell transcriptomics of human islet ontogeny defines the molecular basis of β-cell dedifferentiation in T2D. Mol Metab. 2020. 42:101057.
Enge M, Arda HE, Mignardi M, Beausang J, Bottino R, Kim SK, Quake SR. Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns. Cell. 2017;171(2):321-330.e14.
Saikia M, Holter MM, Donahue LR, Lee IS, Zheng QC, Wise JL, Todero JE, Phuong DJ, Garibay D, Coch R, Sloop KW, Garcia-Ocana A, Danko CG, Cummings BP. GLP-1 receptor signaling increases PCSK1 and β cell features in human α cells. JCI Insight. 2021;6(3): e141851.
Perez-Frances M, van Gurp L, Abate MV, Cigliola V, Furuyama K, Bru-Tari E, Oropeza D, Carreaux T, Fujitani Y, Thorel F, Herrera PL. Pancreatic Ppy-expressing γ-cells display mixed phenotypic traits and the adaptive plasticity to engage insulin production. Nat Commun. 2021;12(1):4458.
van Gurp L, Fodoulian L, Oropeza D, Furuyama K, Bru-Tari E, Vu AN, Kaddis JS, Rodríguez I, Thorel F, Herrera PL. Generation of human islet cell type-specific identity genesets. Nat Commun. 2022;13(1):2020.
Dorrell C, Schug J, Canaday PS, Russ HA, Tarlow BD, Grompe MT, Horton T, Hebrok M, Streeter PR, Kaestner KH, Grompe M. Human islets contain four distinct subtypes of β cells. Nat Commun. 2016;7:11756.
Dominguez-Gutierrez G, Xin Y, Gromada J. Heterogeneity of human pancreatic β-cells. Mol Metab. 2019;27S(Suppl):S7–14.
Ohgushi M, Matsumura M, Eiraku M, Murakami K, Aramaki T, Nishiyama A, Muguruma K, Nakano T, Suga H, Ueno M, Ishizaki T, Suemori H, Narumiya S, Niwa H, Sasai Y. Molecular pathway and cell state responsible for dissociation-induced apoptosis in human pluripotent stem cells. Cell Stem Cell. 2010;7(2):225–39.
van den Brink SC, Sage F, Vértesy Á, Spanjaard B, Peterson-Maduro J, Baron CS, Robin C, van Oudenaarden A. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat Methods. 2017;14(10):935–6.
Redick SD, Leehy L, Rittenhouse AR, Blodgett DM, Derr AG, Kucukural A, Garber MG, Shultz LD, Greiner DL, Wang JP, Harlan DM, Bortell R, Jurczyk A. Recovery of viable endocrine-specific cells and transcriptomes from human pancreatic islet-engrafted mice. FASEB J. 2020;34(1):1901–11.
Tosti L, Hang Y, Debnath O, Tiesmeyer S, Trefzer T, Steiger K, Ten FW, Lukassen S, Ballke S, Kühl AA, Spieckermann S, Bottino R, Ishaque N, Weichert W, Kim SK, Eils R, Conrad C. Single-nucleus and in situ RNA-sequencing reveal cell topographies in the human pancreas. Gastroenterology. 2021;160(4):1330-1344.e11.
Basile G, Kahraman S, Dirice E, Pan H, Dreyfuss JM, Kulkarni RN. Using single-nucleus RNA-sequencing to interrogate transcriptomic profiles of archived human pancreatic islets. Genome Med. 2021;13(1):128.
Lee S, Zhang AY, Su S, Ng AP, Holik AZ, Asselin-Labat M-L, Ritchie ME, Law CW. Covering all your bases: incorporating intron signal from RNA-Seq data. NAR Genomics and Bioinformatics. 2020;2(3):lqaa073.
Wu H, Kirita Y, Donnelly EL, Humphreys BD. Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: rare cell types and novel cell states revealed in fibrosis. JASN. 2019;30(1):23–32.
Scharp DW, Arulmoli J, Morgan K, Sunshine H, Hao E. Advances in human islet processing: manufacturing steps to achieve predictable islet outcomes from research pancreases. OBM Transplantation. 2019; 3(1), https://doi.org/10.21926/obm.transplant.1901052.
Wang P, Alvarez-Perez JC, Felsenfeld DP, Liu H, Sivendran S, Bender A, Kumar A, Sanchez R, Scott DK, Garcia-Ocaña A, Stewart AF. A high-throughput chemical screen reveals that harmine-mediated inhibition of DYRK1A increases human pancreatic beta cell replication. Nat Med. 2015;21(4):383–8.
Ackeifi C, Wang P, Karakose E, Manning Fox JE, González BJ, Liu H, Wilson J, Swartz E, Berrouet C, Li Y, Kumar K, MacDonald PE, Sanchez R, Thorens B, DeVita R, Homann D, Egli D, Scott DK, Garcia-Ocaña A, Stewart AF. GLP-1 receptor agonists synergize with DYRK1A inhibitors to potentiate functional human β cell regeneration. Sci Transl Med. 2020;12(530):eaaw9996.
Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM 3rd, Hao Y, Stoeckius M, Smibert P, Satija R. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888-1902.e21.
Young MD, Behjati S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience. 2020;9(12):giaa151.
McGinnis CS, Murrow LM, Gartner ZJ. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 2019;8(4):329-337.e4.
Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M, Hoffman P, Stoeckius M, Papalexi E, Mimitou EP, Jain J, Srivastava A, Stuart T, Fleming LM, Yeung B, Rogers AJ, McElrath JM, Blish CA, Gottardo R, Smibert P, Satija R. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573-3587.e29.
Xin Y, Adler C, Kim J, Ding Y, Ni M, Wei Y, Macdonald L, Okamoto H. Single-cell RNA sequencing and analysis of human pancreatic islets. J Vis Exp. 2019;(149):59866. https://doi.org/10.3791/59866.
Grün D, Muraro MJ, Boisset JC, Wiebrands K, Lyubimova A, Dharmadhikari G, van den Born M, van Es J, Jansen E, Clevers H, de Koning EJP, van Oudenaarden A. De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell. 2016;19(2):266–77.
Muraro MJ, Dharmadhikari G, Grün D, Groen N, Dielen T, Jansen E, van Gurp L, Engelse MA, Carlotti F, de Koning EJ, van Oudenaarden A. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 2016;3(4):385-394.e3.
Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM, Melton DA, Yanai I. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 2016;3(4):346-360.e4.
Lawlor N, George J, Bolisetty M, Kursawe R, Sun L, Sivakamasundari V, Kycia I, Robson P, Stitzel ML. Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes. Genome Res. 2017;27(2):208–22.
Saeki K, Chang G, Kanaya N, Wu X, Wang J, Bernal L, Ha D, Neuhausen SL, Chen S. Mammary cell gene expression atlas links epithelial cell remodeling events to breast carcinogenesis. Commun Biol. 2021;4(1):660.
Trapnell C, Cacchiarelli D, Grimsby J, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32:381–6.
Qiu X, Mao Q, Tang Y, et al. Reversed graph embedding resolves complex single-cell trajectories. Nat Methods. 2017;14:979–82.
La Manno G, Soldatov R, Zeisel A, et al. RNA velocity of single cells. Nature. 2018;560:494–8.
Bergen V, Lange M, Peidli S, Wolf FA, Theis FJ. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat Biotechnol. 2020;38:1408–14.
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102(43):15545–50.
Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739–40.
Borcherding N, Andrews J. Package escape: easy single cell analysis platform for enrichment. R package version 1.6.0. 2022.
Bonnycastle LL, Gildea DE, Yan T, Narisu N, Swift AJ, Wolfsberg TG, Erdos MR, Collins FS. Single-cell transcriptomics from human pancreatic islets: sample preparation matters. Biol Methods Protoc. 2020;5(1):bpz019.
Slyper M, Porter CBM, Ashenberg O, Waldman J, Drokhlyansky E, Wakiro I, Smillie C, Smith-Rosario G, Wu J, Dionne D, Vigneau S, Jané-Valbuena J, Tickle TL, Napolitano S, Su MJ, Patel AG, Karlstrom A, Gritsch S, Nomura M, Waghray A, Gohil SH, Tsankov AM, Jerby-Arnon L, Cohen O, Klughammer J, Rosen Y, Gould J, Nguyen L, Hofree M, Tramontozzi PJ, Li B, Wu CJ, Izar B, Haq R, Hodi FS, Yoon CH, Hata AN, Baker SJ, Suvà ML, Bueno R, Stover EH, Clay MR, Dyer MA, Collins NB, Matulonis UA, Wagle N, Johnson BE, Rotem A, Rozenblatt-Rosen O, Regev A. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat Med. 2020;26(5):792–802.
Bakken TE, Hodge RD, Miller JA, Yao Z, Nguyen TN, Aevermann B, Barkan E, Bertagnolli D, Casper T, Dee N, Garren E. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLoS ONE. 2018;13(12): e0209648.
Lake BB, Codeluppi S, Yung YC, Gao D, Chun J, Kharchenko PV, Linnarsson S, Zhang K. A comparative strategy for single-nucleus and single-cell transcriptomes confirms accuracy in predicted cell-type expression from nuclear RNA. Sci Rep. 2017;7(1):6031.
Hafemeiste, C, Satija R. Normalization and variance stabilization of single-cell RNA-Seq data using regularized negative binomial regression. Genome Biol. 2019; 20(1), https://doi.org/10.1186/s13059-019-1874-1.
Morgan M, Shepherd L. AnnotationHub: Client to access AnnotationHub resources. R package version 3.4.0. 2022.
Süss C, Czupalla C, Winter C, Pursche T, Knoch KP, Schroeder M, Hoflack B, Solimena M. Rapid changes of mRNA-binding protein levels following glucose and 3-isobutyl-1-methylxanthine stimulation of insulinoma INS-1 cells. Mol Cell Proteomics. 2009;8(3):393–408.
Pullen TJ, Khan AM, Barton G, Butcher SA, Sun G, Rutter GA. Identification of genes selectively disallowed in the pancreatic islet. Islets. 2010;2:89–95.
O'Flanagan CH, Campbell KR, Zhang AW, Kabeer F, Lim JLP, Biele J, Eirew P, Lai D, McPherson A, Kong E, Bates C, Borkowski K, Wiens M, Hewitson B, Hopkins J, Pham J, Ceglia N, Moore R, Mungall AJ, McAlpine JN; CRUK IMAXT Grand Challenge Team, Shah SP, Aparicio S. Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol. 2019; 20(1):210.
Pena-Ortiz MA, Shafiq S, Rowland ME, Bérubé NG. Selective isolation of mouse glial nuclei optimized for reliable downstream omics analyses. J Neurosci Methods. 2022;369: 109480.
Itoh N, Okamoto H. Translational control of proinsulin synthesis by glucose. Nature. 1980;283(5742):100–2.
Vasiljević J, Torkko JM, Knoch KPS, M. The making of insulin in health and disease. Diabetologia. 2020;63:1981–9.
Moss ND, Sussel L. mRNA Processing: an emerging frontier in the regulation of pancreatic β cell function. Front Genet. 2020;11:983.
Carmody SR, Wente SR. mRNA nuclear export at a glance. J Cell Sci. 2009;122(Pt 12):1933–7.
Park HS, Lee J, Lee HS, Ahn SH, Ryu HY. Nuclear mRNA export and aging. Int J Mol Sci. 2022;23(10):5451.
Wang J, Shen L, Najafi H, Kolberg J, Matschinsky FM, Urdea M, German M. Regulation of insulin preRNA splicing by glucose. Proc Natl Acad Sci U S A. 1997;94(9):4360–5.
Evans-Molina C, Garmey JC, Ketchum R, Brayman KL, Deng S, Mirmira RG. Glucose regulation of insulin gene transcription and pre-mRNA processing in human islets. Diabetes. 2007;56(3):827–35.
Eicher JD, Powers NR, Miller LL, Akshoomoff N, Amaral DG, Bloss CS, Libiger O, Schork NJ, Darst BF, Casey BJ, Chang L, Ernst T, Frazier J, Kaufmann WE, Keating B, Kenet T, Kennedy D, Mostofsky S, Murray SS, Sowell ER, Bartsch H, Kuperman JM, Brown TT, Hagler DJ Jr, Dale AM, Jernigan TL, St Pourcain B, Davey Smith G, Ring SM, Gruen JR. Pediatric Imaging, Neurocognition, and Genetics Study. Genome-wide association study of shared components of reading disability and language impairment. Genes Brain Behav. 2013;12(8):792–801.
Schuit FC, In’t Veld PA, Pipeleers DG. Glucose stimulates proinsulin biosynthesis by a dose-dependent recruitment of pancreatic beta cells. Proc Natl Acad Sci U S A. 1988;85(11):3865–9.
Van Schravendijk CF, Kiekens R, Pipeleers DG. Pancreatic beta cell heterogeneity in glucose-induced insulin secretion. J Biol Chem. 1992;267(30):21344–8.
Pipeleers D, Kiekens R, Ling Z, Wilikens A, Schuit F. Physiologic relevance of heterogeneity in the pancreatic beta-cell population. Diabetologia. 1994;37(Suppl 2):S57-64.
Johnston NR, Mitchell RK, Haythorne E, Pessoa MP, Semplici F, Ferrer J, Piemonti L, Marchetti P, Bugliani M, Bosco D, Berishvili E, Duncanson P, Watkinson M, Broichhagen J, Trauner D, Rutter GA, Hodson DJ. Beta cell hubs dictate pancreatic islet responses to glucose. Cell Metab. 2016;24(3):389–401.
Salem V, Silva LD, Suba K, Georgiadou E, Neda Mousavy Gharavy S, Akhtar N, Martin-Alonso A, Gaboriau DCA, Rothery SM, Stylianides T, Carrat G, Pullen TJ, Singh SP, Hodson DJ, Leclerc I, Shapiro AMJ, Marchetti P, Briant LJB, Distaso W, Ninov N, Rutter GA. Leader β-cells coordinate Ca2+ dynamics across pancreatic islets in vivo. Nat Metab. 2019; (6):615–629.
Kravets V, Dwulet JM, Schleicher WE, Hodson DJ, Davis AM, Pyle L, Piscopio RA, Sticco-Ivins M, Benninger RKP. Functional architecture of pancreatic islets identifies a population of first responder cells that drive the first-phase calcium response. PLoS Biol. 2022;20(9): e3001761.
Roscioni SS, Migliorini A, Gegg M, Lickert H. Impact of islet architecture on β-cell heterogeneity, plasticity and function. Nat Rev Endocrinol. 2016;12(12):695–709.
Bader E, Migliorini A, Gegg M, Moruzzi N, Gerdes J, Roscioni SS, Bakhti M, Brandl E, Irmler M, Beckers J, Aichler M, Feuchtinger A, Leitzinger C, Zischka H, Wang-Sattler R, Jastroch M, Tschöp M, Machicao F, Staiger H, Häring HU, Chmelova H, Chouinard JA, Oskolkov N, Korsgren O, Speier S, Lickert H. Identification of proliferative and mature β-cells in the islets of Langerhans. Nature. 2016;535(7612):430–4.
Shrestha S, Erikson G, Lyon J, Spigelman AF, Bautista A, Manning Fox JE, Dos Santos C, Shokhirev M, Cartailler JP, Hetzer MW, MacDonald PE, Arrojo E Drigo R. Aging compromises human islet beta cell function and identity by decreasing transcription factor activity and inducing ER stress. Sci Adv. 2022; 8(40):eabo3932.
Wang P, Karakose E, Liu H, Swartz E, Ackeifi C, Zlatanic V, Wilson J, González BJ, Bender A, Takane KK, Ye L, Harb G, Pagliuca F, Homann D, Egli D, Argmann C, Scott DK, Garcia-Ocaña A, Stewart AF. Combined inhibition of DYRK1A, SMAD, and trithorax pathways synergizes to induce robust replication in adult human beta cells. Cell Metab. 2019;29(3):638-652.e5.
Puri S, Roy N, Russ HA, Leonhardt L, French EK, Roy R, Bengtsson H, Scott DK, Stewart AF, Hebrok M. Replication confers β cell immaturity. Nat Commun. 2018;9(1):485.
Bonner-Weir S, Weir GC. New sources of pancreatic beta-cells. Nat Biotechnol. 2005;23(7):857–61.
Garcia-Ocana A. Single cell RNA/single nucleus RNA sequencing data of human pancreatic islet (human). PRJNA900882, NCBI Sequence Read Archive. 2022. https://www.ncbi.nlm.nih.gov/bioproject/PRJNA900882
Kang, R. scRNA-snRNA-Marker. GitHub Repository, 2023. https://github.com/randystyle21/scRNA-snRNA-Marker
We thank the NIDDK-supported Einstein-Sinai DRC and the Human Adenovirus and Islet Core for help with the proposed studies and Prodo Labs for supplying human cadaveric islets. We thank Luis Santos for his help with the initial technical steps of the snRNA-seq technique and Drs. Nika Danial, Giorgio Basile, and Rohit Kulkarni for their comments of the study and manuscript.
We acknowledge support from NIH grants P-30 DK020541, R-01 DK116873, R-01 DK125285, R-01 DK105015, R-01 DK116873, R-01 DK129196, R-01 DK113079, R-01 DK126450, R-01 DK130300, and an Einstein-Sinai DRC Pilot and Feasibility Grant (to GL).
Ethics approval and consent to participate
The study complied with all relevant ethical regulations for work with human cells for research purposes. Islets were purchased from Prodo Laboratories, Inc. which has been granted the right to be a service provider of human islets obtained from human donor pancreases by the US Department of Health and the FDA. Donor organs and processing of those organs for the purpose of distributing human islet cells has been audited by the Western Institutional Review Board (WIRB). In addition to abiding by US laws, Prodo also abides by the regulations of the United Network of Organ Sharing (UNOS). Strict, confidential HIPAA policies are in place to maintain the anonymity of each donor. Prodo also obtains the proper consent forms with each organ provided by UNOS. Prodo certifies that informed consent was properly and legally secured and obtained from the donor or the donor’s family (including donor’s legally acceptable representative) in writing, after having been informed of all aspect of the donation of human tissue(s) that are relevant to their decision. Because the donors are brain dead, the institutional review board considers the tissue as “Exempt” from Human Studies Approval.
Animal studies and protocols were performed with the approval of and in accordance with guidelines established by the Institutional Animal Care and Use Committee of the Icahn School of Medicine at Mount Sinai (IACUC #2015–0107).
All research performed in this study followed the Declaration of the Helsinki Ethical Principles for Medical Research Involving Human Samples.
Consent for publication
A.G-O. consults for Sun Pharmaceuticals Industries. A.F.S., G.L., and A.G-O. have filed patents on β-cell regenerative drugs through the Icahn School of Medicine at Mount Sinai. The remaining authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Additional file 1: Table S1.
Characteristics of human islet donors and human islet preparations used for the transcriptomic analysis. Table S2. Number of cells/nuclei loaded, targeted, and recovered, and the mean and median of reads and genes per cell/nuclei for the different samples used in this study. Figure S1. Gene overlap between scRNA-seq and snRNA-seq using UMIs > 0.1,1,10,20,50,100. Figure S2. Analysis of reference alignment prediction score, expression level and percentage of cells using subsampling to the same UMI depth for both intron-inclusive library and the exonic only library. Figure S3. snRNA-seq gene markers in human islet cells and β-cell subtypes in the previously published dataset for snRNA-seq data in vitro and in vivo from Basile et al., reference 22. Figure S4. Analysis of β-cell inter-cluster 3-2 region using a high Louvain algorithm resolution of 2.0 to resolve smaller clusters. Figure S5. RNA Velocity analysis of integrated and individual scRNA-seq and snRNA-seq data from human islets in vitro. Figure S6. Differentially expressed gene sets from β-cell subtypes in Dorrell et al. reportprojected into the β-cell subclusters of the current study. Figure S7. Gene expression in the “reactome_glucose-metabolism” gene set plus SLC2A1-3, LDHA and SLC16A1genes in the different β-cell subclusters of the current study.
Additional file 2: Data S1.
Differentially expressed genes in the different human islet cell clusters from the scRNA-seq studies.
Additional file 3: Data S2.
Differentially expressed genes in the different human islet cell clusters from the snRNA-seq studies in vitro.
Additional file 4: Data S3.
Differentially expressed genes in the different human β-cell subclusters from the integrated, scRNA-seq and snRNA-seq in vitro studies and the snRNA-seq in vivo studies.
Additional file 5: Data S4.
Differentially expressed genes in the human β-cell 3-2 transition subclusters from the integrated datasets in the in vitro studies.
Additional file 6: Data S5.
Differentially expressed genes in the different human islet cell clusters from the snRNA-seq studies in vivo.
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Kang, R.B., Li, Y., Rosselot, C. et al. Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles. Genome Med 15, 30 (2023). https://doi.org/10.1186/s13073-023-01179-2
- Human islet
- Human pancreatic beta cell
- Single-cell RNA sequencing
- Single-nucleus RNA sequencing
- Human islet graft