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NKX2-2 based nuclei sorting on human archival pancreas enables the enrichment of islet endocrine populations for single nucleus RNA sequencing

GSE252614 Homo sapiens Expression profiling by high throughput sequencing 5 samples Submitted 2024/04/01 Platform GPL24676
Summary
Current approaches to profile the single-cell transcriptomics of human pancreatic endocrine cells almost exclusively rely on freshly isolated islets. However, human islets are limited in availability. Furthermore, the extensive processing steps during islet isolation and subsequent single cell dissociation might alter gene expressions. In this work, we cross-compared five nuclei isolation protocols and selected the citric acid method as the best strategy to isolate nuclei with high RNA integrity and low cytoplasmic contamination from human pancreata. We innovated fluorescence-activated nuclei sorting (FANS) based on the positive signal of NKX2-2 antibody to enrich for nuclei of the endocrine population from the entire nuclei pool of the pancreas. Our sample preparation procedure generated high-quality single-nucleus gene-expression libraries while preserving the endocrine population diversity. We observed comparable endocrine cellular composition and cell type signature gene expression between our snRNA-seq libraries and conventional scRNA-seq libraries generated with live cells from freshly isolated human islets. Our work fills a technological gap and helps to unleash archival pancreatic tissue for molecular profiling targeting the endocrine population. We expect that our protocol can be used to enrich nuclei for transcriptomics study from various populations in the pancreas and in different organs/tissues.
Published in
NKX2-2 based nuclei sorting on frozen human archival pancreas enables the enrichment of islet endocrine populations for single-nucleus RNA sequencing
Xie G, Toledo MP, Hu X et al. · BMC genomics 2024 · PMID 38689254 · doi:10.1186/s12864-024-10335-w
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Also filed as BioProject PRJNA1061519 and SRA study SRP482213. Searching any of these in the dataset finder brings you back here.

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