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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

Engström, Karin LU ; Nilsson, Åsa LU ; Ofori, Jones K. LU ; Wierup, Nils LU ; Bacos, Karl LU orcid and Ling, Charlotte LU orcid (2025) In Scientific Reports 15(1).
Abstract

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... (More)

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.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Annotation, Marker genes, Pancreatic islets, Single-cell RNA-sequencing, Single-nuclei RNA-sequencing, Type 2 diabetes
in
Scientific Reports
volume
15
issue
1
article number
36133
publisher
Nature Publishing Group
external identifiers
  • scopus:105018991588
  • pmid:41102292
ISSN
2045-2322
DOI
10.1038/s41598-025-21595-1
language
English
LU publication?
yes
id
0718af03-40a8-4209-b116-2998deead181
date added to LUP
2025-12-11 11:36:26
date last changed
2025-12-12 03:00:20
@article{0718af03-40a8-4209-b116-2998deead181,
  abstract     = {{<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 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.</p>}},
  author       = {{Engström, Karin and Nilsson, Åsa and Ofori, Jones K. and Wierup, Nils and Bacos, Karl and Ling, Charlotte}},
  issn         = {{2045-2322}},
  keywords     = {{Annotation; Marker genes; Pancreatic islets; Single-cell RNA-sequencing; Single-nuclei RNA-sequencing; Type 2 diabetes}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Reports}},
  title        = {{Evaluating cell-specific gene expression using single-cell and single-nuclei RNA-sequencing data from human pancreatic islets of the same donors}},
  url          = {{http://dx.doi.org/10.1038/s41598-025-21595-1}},
  doi          = {{10.1038/s41598-025-21595-1}},
  volume       = {{15}},
  year         = {{2025}},
}