Evidence map›Paper›PMID 41102292›Full record

ArticleScientific reports2025

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

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

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Karin EngströmEpidemiology and Bioinformatics, Division of Occupational and Environmental Medicine, Department of Laboratory Medicine, Lund University, Lund, Sweden. karin.engstrom@med.lu.se.
Åsa NilssonHuman Tissue Lab, Department of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund University, Scania University Hospital, Malmö, Sweden.
Jones K OforiEpigenetics and Diabetes Unit, Department of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund University, Scania University Hospital, Malmö, Sweden.
Nils WierupNeuroendocrine Cell Biology, Department of Experimental Medical Science, Lund University Diabetes Centre, Lund University, Scania University Hospital, Malmö, Sweden.
Karl BacosEpigenetics and Diabetes Unit, Department of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund University, Scania University Hospital, Malmö, Sweden.
Charlotte LingEpigenetics and Diabetes Unit, Department of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund University, Scania University Hospital, Malmö, Sweden. charlotte.ling@med.lu.se.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 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.

Indexed as

Islets of LangerhansSequence Analysis, RNASingle-Cell AnalysisGene Expression ProfilingHumansMolecular Sequence AnnotationRNA, Small NuclearTissue DonorsTranscriptomeRNA, Small NuclearAnnotationMarker genesPancreatic isletsSingle-cell RNA-sequencingSingle-nuclei RNA-sequencingType 2 diabetes

Identifiers

PMID41102292
PMCPMC12533216

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.