Single-cell RNA sequencing (scRNA-seq) of human islets requires cell dissociation and does not provide information on the transcriptional status of islet cells. On the other hand, single-nucleus RNA sequencing (snRNA-seq) does not require cell dissociation and provides abundant information on intronic sequences that can be used to identify actively transcribed genes. Based on this, we seek to compare scRNA-seq and snRNA-seq approaches in human islets to determine: 1) whether similar cell clusters could be detected; 2) whether new gene markers could be discovered in human islet endocrine cells using intronic reads; 3) whether human beta cell subpopulations could be identified based on INS expression dynamics; and 4) whether snRNA-seq could be used in human islet grafts transplanted in immunosuppressed mice. We performed scRNA-seq and snRNA-seq on three pairs of human islets obtained from three healthy adult human donors using exon only or exon plus intron reads, respectively. Analysis of integrated data revealed similar human islet cell clusters. In the snRNA-seq data, however, top differentially expressed genes were identified as new markers of human endocrine cells such as ZNF385D (β-cells) , PTPRT (α-cells) , LRFN5 (δ-cells) and CACNA2D3 (PP cells) . These markers also accurately define endocrine cell populations in human islet grafts. Additionally, we distinguished several beta cell sub-clusters- INS rich cluster, HNRNPA2B1 (vesicle) rich cluster and active INS transcribing cluster.

In conclusion, snRNA-seq analysis of human islet cells is a previously unrecognized tool for the identification of human islet cell types in samples where nuclear RNA processing is required. By comparing INS expression between scRNA-seq and snRNA-seq data sets, we can detect different beta cell sub-populations with distinct gene expression patterns representing different biological dynamic states.


R.Kang: None. Y.Li: None. C.Rosselot: None. D.Scott: None. A.Garcia-ocana: Consultant; Sun Pharmaceutical Industries Ltd. G.Lu: None.


NIH DK105015

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