Single Nucleus RNA Sequencing of Human Pancreatic Islets In Vitro and In Vivo Identifies New Gene Sets and Three β-Cell Subpopulations with Different Transcriptional Profile

Abstract

Single-cell RNA sequencing (scRNA-seq) has provided 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 and provides limited information on the active transcriptional status of islet cells. On the other hand, single-nucleus RNA sequencing (snRNA-seq) does not require cell dissociation and affords enhanced information from intronic sequences that can be leveraged to identify actively transcribing genes in islet cell populations. Here, we first sought to compare scRNA-seq and snRNA-seq analysis of human islets in vitro using exon reads or combined exon and intron reads, respectively. Datasets reveal similar human islet cell clusters using both approaches. In the snRNA-seq data, however, the top differentially expressed genes in human islet endocrine cells are not the canonical genes but a new set of non-canonical gene markers including ZNF385D, TRPM3, LRFN2, PLUT ({beta} cells), PTPRT, FAP, PDK4, LOXL4 ( cells), LRFN5, ADARB2, ERBB4, KCNT2 ({delta} cells) and CACNA2D3, THSD7A, CNTNAP5, RBFOX3 ({gamma} cells). Notably, these markers also accurately define endocrine cell populations in human islet grafts in vivo. Further, by integrating the information from nuclear and cytoplasmic transcriptomes, we identify three {beta}-cell sub-clusters: an active INS mRNA transcribing cluster ({beta}1), an intermediate INS mRNA-transcribing cluster ({beta}2), and a mature INS mRNA rich cluster ({beta}3). These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. Interestingly, the INS mRNA rich cluster ({beta}3) becomes the predominant sub-cluster in vivo. In summary, snRNA-seq analysis of human islet cells is a previously unrecognized tool that can be accurately employed for improved identification of human islet cell types and their transcriptional status in vivo.