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<p>Single-cell and single-nuclei RNA-sequencing (scRNA-seq and snRNA-seq) analyze cell-specific transcriptomes. However, only snRNA-seq applies to frozen biobanked samples. For human pancreatic islets, marker genes and reference-based cell type annotation methods are mainly from scRNA-seq datasets and may not be suitable for snRNA-seq. We compared human islet scRNA-seq and snRNA-seq data from the same donors (N = 4) and evaluated annotation methods by studying cell type composition and gene detection, and identified novel marker genes. We compared cell type annotations: (1) manual annotation based on identified marker genes, (2) reference-based annotation using Azimuth’s scRNA-seq <i>pancreasref</i> dataset, or (3) Seurat’s label transfer from the Human Pancreas Analysis Program (HPAP)&#xa0;scRNA-seq dataset. ScRNA-seq and snRNA-seq identified the same cell types, but predicted cell type proportions differed. Cell type proportion-differences between annotation methods were larger for snRNA-seq. Reference-based annotations generated higher cell type prediction and mapping scores for scRNA-seq than snRNA-seq. Manual annotation identified the novel snRNA-seq markers <i>DOCK10, KIRREL3</i> (beta cells), <i>STK32B</i> (alpha cells), <i>MECOM</i>, <i>AC007368.1</i> (acinar cells), <i>LAMC2</i> and <i>SLC28A3</i> (ductal cells), which improve snRNA-seq-based annotation. We confirmed <i>ZNF385D</i> as a snRNA-seq beta cell marker and <i>ZNF385D</i> silencing reduced insulin secretion. In conclusion, this study discovered novel snRNA-seq cell type marker genes in human pancreatic islets, and highlights the need for tailored snRNA-seq annotation strategies.</p>

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Evaluating cell-specific gene expression using single-cell and single-nuclei RNA-sequencing data from human pancreatic islets of the same donors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月16日

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  • Karin Engström,
  • Åsa Nilsson,
  • Jones K. Ofori,
  • Nils Wierup,
  • Karl Bacos,
  • Charlotte Ling

摘要

Single-cell and single-nuclei RNA-sequencing (scRNA-seq and snRNA-seq) analyze cell-specific transcriptomes. However, only snRNA-seq applies to frozen biobanked samples. For human pancreatic islets, marker genes and reference-based cell type annotation methods are mainly from scRNA-seq datasets and may not be suitable for snRNA-seq. We compared human islet scRNA-seq and snRNA-seq data from the same donors (N = 4) and evaluated annotation methods by studying cell type composition and gene detection, and identified novel marker genes. We compared cell type annotations: (1) manual annotation based on identified marker genes, (2) reference-based annotation using Azimuth’s scRNA-seq pancreasref dataset, or (3) Seurat’s label transfer from the Human Pancreas Analysis Program (HPAP) scRNA-seq dataset. ScRNA-seq and snRNA-seq identified the same cell types, but predicted cell type proportions differed. Cell type proportion-differences between annotation methods were larger for snRNA-seq. Reference-based annotations generated higher cell type prediction and mapping scores for scRNA-seq than snRNA-seq. Manual annotation identified the novel snRNA-seq markers DOCK10, KIRREL3 (beta cells), STK32B (alpha cells), MECOM, AC007368.1 (acinar cells), LAMC2 and SLC28A3 (ductal cells), which improve snRNA-seq-based annotation. We confirmed ZNF385D as a snRNA-seq beta cell marker and ZNF385D silencing reduced insulin secretion. In conclusion, this study discovered novel snRNA-seq cell type marker genes in human pancreatic islets, and highlights the need for tailored snRNA-seq annotation strategies.