Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes ß-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 (ß-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 ß-cell sub-clusters: an INS pre-mRNA cluster (ß3), an intermediate INS mRNA cluster (ß2), and an INS mRNA-rich cluster (ß1). These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. Interestingly, the INS mRNA-rich cluster (ß1) 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.

Human Pancreatic α-Cell Heterogeneity and Trajectory Inference Analysis Using Integrated Single Cell- and Single Nucleus-RNA Sequencing Platforms.

Prior studies have shown that pancreatic α-cells can transdifferentiate into ß-cells, and that ß-cells de-differentiate and are prone to acquire an α-cell phenotype in type 2 diabetes (T2D). However, the specific human α-cell and ß-cell subtypes that are involved in α-to-ß-cell and ß-to-α-cell transitions are unknown. Here, we have integrated single cell RNA sequencing (scRNA-seq) and single nucleus RNA-seq (snRNA-seq) of isolated human islets and human islet grafts and provide additional insight into α-ß cell fate switching. Using this approach, we make seven novel observations. 1) There are five different GCG -expressing human α-cell subclusters [α1, α2, α-ß-transition 1 (AB-Tr1), α-ß-transition 2 (AB-Tr2), and α-ß (AB) cluster] with different transcriptome profiles in human islets from non-diabetic donors. 2) The AB subcluster displays multihormonal gene expression, inferred mostly from snRNA-seq data suggesting identification by pre-mRNA expression. 3) The α1, α2, AB-Tr1, and AB-Tr2 subclusters are enriched in genes specific for α-cell function while AB cells are enriched in genes related to pancreatic progenitor and ß-cell pathways; 4) Trajectory inference analysis of extracted α- and ß-cell clusters and RNA velocity/PAGA analysis suggests a bifurcate transition potential for AB towards both α- and ß-cells. 5) Gene commonality analysis identifies ZNF385D, TRPM3, CASR, MEG3 and HDAC9 as signature for trajectories moving towards ß-cells and SMOC1, PLCE1, PAPPA2, ZNF331, ALDH1A1, SLC30A8, BTG2, TM4SF4, NR4A1 and PSCK2 as signature for trajectories moving towards α-cells. 6) Remarkably, in contrast to the events in vitro , the AB subcluster is not identified in vivo in human islet grafts and trajectory inference analysis suggests only unidirectional transition from α-to-ß-cells in vivo . 7) Analysis of scRNA-seq datasets from adult human T2D donor islets reveals a clear unidirectional transition from ß-to-α-cells compatible with dedifferentiation or conversion into α-cells. Collectively, these studies show that snRNA-seq and scRNA-seq can be leveraged to identify transitions in the transcriptional status among human islet endocrine cell subpopulations in vitro , in vivo , in non-diabetes and in T2D. They reveal the potential gene signatures for common trajectories involved in interconversion between α- and ß-cells and highlight the utility and power of studying single nuclear transcriptomes of human islets in vivo . Most importantly, they illustrate the importance of studying human islets in their natural in vivo setting.