scRNA-seq on human islet cells has identified genes associated with type 1 (T1D) and type 2 (T2D) diabetes, genes important for islet cell development and maturation, for islet dysfunction and dedifferentiation, for aging, and genes involved in the transdifferentiation among islet cells. 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 also requires mechanical and enzymatic cell dissociation with unavoidable adverse biological consequences on islet cell subtypes. 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. 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. Detailed characterization of the transcriptional programs in islet cells in health and disease will help to identify therapeutic targets to treat diabetes. 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.ĭiabetes results from deficiency of functional pancreatic β-cells. Interestingly, the INS mRNA-rich cluster (β1) becomes the predominant sub-cluster in vivo. These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. 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). Resultsįirst, 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). In both analyses, we included the intronic reads in the snRNA-seq data with the GRCh38-2020-A library. We employed snRNA-seq to obtain the transcriptomic profile of human islets engrafted in immunodeficient mice. We compared these datasets to scRNA-seq output obtained from human islet cells from the same donor. We obtained nuclear preparations from fresh human islet cells and generated snRNA-seq datasets. 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. However, this approach requires cell dissociation that complicates its utility in vivo. Single-cell RNA sequencing (scRNA-seq) provides valuable insights into human islet cell types and their corresponding stable gene expression profiles.
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